{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "from IPython.display import HTML, display, Image, HTML\n",
    "from IPython.core.display import HTML\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Advanced probeset selection\n",
    "\n",
    "In this tutorial we look into customizing the gene set selection for more specific use cases and explore the parameters \n",
    "of the selection procedure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false,
    "tags": [
     "remove_input",
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "image/png": {
       "width": 700
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"./abstract_figures/selection_procedure.png\", width=700, embed=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Spapros probeset selection pipeline simultaneously optimizes for capturing transcriptional variation and cell type identification while taking into account prior knowledge, technical constraints and probe design. The main steps, as visualized in the figure above, are: \n",
    "\n",
    "**Constrains**\n",
    "\n",
    "In the first step, genes are filtered based on technology specific probe design constraints. This part is captured in the [end-to-end probeset selection tutorial](./spapros_tutorial_end_to_end_selection.ipynb). (Besides probe design constraints also expression level constraints can be applied as shown in the [Selection with experession constraints](#v.-Selection-with-expression-constraints) example below. Those affect multiple steps of the pipeline though)\n",
    "\n",
    "**Variable genes**\n",
    "\n",
    "The next step is a PCA based selection of genes that capture general variation. The subsequent steps of the pipeline are encouraged to select from that set of genes. If you want the selection to be focused only on marker genes and cell type classification, the PCA-based selection step can be omitted, see the [Cell type classification only](#ii.-Cell-type-classification-only) example for this case.\n",
    "\n",
    "**Distinguish celltypes**\n",
    "\n",
    "For each cell type, a binary classification tree is trained on the previously selected gene set. Here the pipeline builds for each cell type a combinatorial rule how to distinguish it from other cell types.\n",
    "\n",
    "**DE gene addition**\n",
    "\n",
    "Similarly to the last step, trees are also trained on DE genes and compared to the trees trained on the PCA based selection. If a DE tree performs better with respect to cell type classification, then DE genes are added to the already selected gene set.\n",
    "\n",
    "**Prior knowledge**\n",
    "\n",
    "There are two possibilities to incorporate prior knowledge. For both, we present use cases in this tutorial: \n",
    "\n",
    "- [Select a few additional genes](#i.-Select-a-few-additional-genes): pre-selected genes are augmented by combinatorially selecting additional genes around them\n",
    "- [Selection with curated marker list](#iv.-Selection-with-curated-marker-list): add markers from a given list in case the signals from that list are not captured with the already selected genes\n",
    "\n",
    "**Probe design**\n",
    "\n",
    "Design the final technology specific probes for the selected genes. As the **Constraints** step, this part is also only captured in the [end-to-end probeset selection tutorial](./spapros_tutorial_end_to_end_selection.ipynb).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden"
   },
   "source": [
    "For a detailed description of the pipeline check out our [preprint](TODO: Add link when bioarxiv is out) (especially the results section *end-to-end probe set selection with Spapros* and the *methods* section).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Content**  \n",
    "\n",
    "0. [Import packages](#0.-Import-packages)  \n",
    "1. [Load and preprocess data](#1.-Load-and-preprocess-data)  \n",
    "2. [Use cases](#2.-Use-cases)  \n",
    "    i. [Select a few additional genes](#i.-Select-a-few-additional-genes)  \n",
    "    ii. [Cell type classification only](#ii.-Cell-type-classification-only)  \n",
    "    iii. [Selection for high numbers of genes (> 150)](#iii.-Selection-for-high-numbers-of-genes-(-150))  \n",
    "    iv. [Selection with curated marker list](#iv.-Selection-with-curated-marker-list)  \n",
    "    v. [Selection with expression constraints](#v.-Selection-with-expression-constraints)  \n",
    "    vi. [Your use case is not captured?](#vi.-Your-use-case-is-not-captured)\n",
    "3. [What's next](#3.-What's-next)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scanpy as sc\n",
    "import spapros as sp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scanpy==1.9.1 anndata==0.8.0 umap==0.5.3 numpy==1.21.6 scipy==1.7.3 pandas==1.3.5 scikit-learn==1.0.2 statsmodels==0.13.2 python-igraph==0.9.11 pynndescent==0.5.7\n",
      "spapros==0.1.0\n"
     ]
    }
   ],
   "source": [
    "sc.settings.verbosity = 0\n",
    "sc.logging.print_header()\n",
    "print(f\"spapros=={sp.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load and preprocess data\n",
    "\n",
    "For Spapros selections the count data should be log-normalised. Notably genes should **not** be scaled to mean=0 and std=1. We pre-select a given number of highly variable genes, here 1000. In real world applications we typically go for 8000.   \n",
    "    \n",
    "The example data set that we use here has a raw version and a processed version with scaled counts. To get log normalised counts we use the raw version. Cell and gene filters, cell type annotations, and the umap embedding we get from the processed version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 2638 × 1838\n",
       "    obs: 'celltype'\n",
       "    var: 'gene_ids', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n",
       "    uns: 'log1p', 'hvg'\n",
       "    obsm: 'X_umap'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata = sc.datasets.pbmc3k()\n",
    "adata_tmp = sc.datasets.pbmc3k_processed()\n",
    "\n",
    "# Get infos from the processed dataset\n",
    "adata = adata[adata_tmp.obs_names, adata_tmp.var_names]\n",
    "adata.obs['celltype'] = adata_tmp.obs['louvain']\n",
    "adata.obsm['X_umap'] = adata_tmp.obsm['X_umap']\n",
    "del adata_tmp\n",
    "\n",
    "# Preprocess counts and get highly variable genes\n",
    "sc.pp.normalize_total(adata)\n",
    "sc.pp.log1p(adata)\n",
    "sc.pp.highly_variable_genes(adata, flavor=\"cell_ranger\", n_top_genes=1000)\n",
    "\n",
    "adata\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Use cases\n",
    "\n",
    "We will go through some specific use cases for gene selection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### i. Select a few additional genes\n",
    "\n",
    "In some situations you already selected multiple genes that you want to measure in a targeted spatial transcriptomics experiment but you still have capacity for further genes. For this scenario the `preselected_genes` parameter was introduced. \n",
    "\n",
    "Let's distinguish two scenarios    \n",
    "\n",
    "1. We preselected 40 genes and want to add 10 (adds up to 50 in total)    \n",
    "2. We preselected 40 genes and want to add 100 (adds up to 140 in total)    \n",
    "\n",
    "For the 2. scenario simply adding the first 40 genes in `preselected_genes` and going with default parameters is the right way to go. For the 1. scenario (which we look at in the code below) the parameter `n_pca_genes` should be reduced. The reason is that Spapros will build trees based on the pre selected genes and the genes from the PCA selection. If there are a lot of PCA genes in the pool, then the trees might ignore most of the preselected genes. This is not extremly problematic, but it's preferable to build up our combinatorial rules on the pre selected genes. We'll reduce `n_pca_genes` from 100 (default) to 20 for the given case. Note however that the `preselected_genes` can include genes that overlap with the pca genes, therefore we more specifically set `n_pca_genes` to the number that provides us 20 more pca genes on top of the preselected list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a96ad980335849c3af9565c46340b138",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: The following celltypes' test set sizes for forest training are below min_test_n (=20):\n",
      "\t Dendritic cells : 9\n",
      "\t Megakaryocytes  : 3\n",
      "The genes selected for those cell types potentially don't generalize well. Find the genes for each of those cell types in self.genes_of_primary_trees after running self.select_probeset().\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list of pre selected genes\n",
    "preselected_genes = [\n",
    "    'PF4', 'HLA-DPB1', 'FCGR3A', 'GZMB', 'CCL5', 'S100A8', 'IL32', 'HLA-DQA1', 'NKG7', 'AIF1', 'CD79A', 'LTB', 'TYROBP',\n",
    "    'HLA-DMA', 'GZMK', 'HLA-DRB1', 'FCN1', 'S100A11', 'GNLY', 'GZMH', 'CST3', 'RBM39', 'HLA-DPA1', 'LST1', 'LGALS2', \n",
    "    'FCER1A', 'MS4A1', 'TRAF3IP3', 'PRF1', 'LGALS1', 'PRDX1', 'ATP5O', 'C1orf162', 'CTSS', 'SELL', 'CTSW', 'GIMAP4', \n",
    "    'LINC00926', 'MS4A6A', 'GSTP1'\n",
    "]\n",
    "\n",
    "# Get `n_pca_genes` to obtain the top 20 pca genes that are not already in preselected_genes\n",
    "tmp = sp.se.select_pca_genes(adata, n=adata.n_vars, inplace=False)[\"selection_ranking\"]\n",
    "n_pca_genes = tmp.loc[~tmp.index.isin(preselected_genes)].sort_values().iloc[:20].max().astype(int)\n",
    "\n",
    "# create an instance of the ProbesetSelector class\n",
    "selector = sp.se.ProbesetSelector(\n",
    "    adata, \n",
    "    n=50, \n",
    "    n_pca_genes=n_pca_genes,\n",
    "    preselected_genes=preselected_genes,\n",
    "    celltype_key=\"celltype\", \n",
    "    verbosity=1, \n",
    "    save_dir=None, \n",
    ")\n",
    "\n",
    "# select probe set\n",
    "selector.select_probeset()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can investigate the selected gene set. The annotations in the table describe where genes originate from:\n",
    "- the first 40 genes are annotated with `pre_selected=True` \n",
    "- the added genes from 41 till 50 are annotated with `selection=True` and `pre_selected=False`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_nr</th>\n",
       "      <th>selection</th>\n",
       "      <th>rank</th>\n",
       "      <th>marker_rank</th>\n",
       "      <th>tree_rank</th>\n",
       "      <th>importance_score</th>\n",
       "      <th>pca_score</th>\n",
       "      <th>pre_selected</th>\n",
       "      <th>prior_selected</th>\n",
       "      <th>pca_selected</th>\n",
       "      <th>celltypes_DE_1vsall</th>\n",
       "      <th>celltypes_DE_specific</th>\n",
       "      <th>celltypes_DE</th>\n",
       "      <th>celltypes_marker</th>\n",
       "      <th>list_only_ct_marker</th>\n",
       "      <th>required_marker</th>\n",
       "      <th>required_list_marker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PF4</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.810399</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Megakaryocytes</td>\n",
       "      <td></td>\n",
       "      <td>Megakaryocytes</td>\n",
       "      <td>Megakaryocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD79A</th>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.895466</td>\n",
       "      <td>1.226536</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>B cells</td>\n",
       "      <td></td>\n",
       "      <td>B cells</td>\n",
       "      <td>B cells</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCL5</th>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.545002</td>\n",
       "      <td>2.553322</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td></td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GZMB</th>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.530616</td>\n",
       "      <td>1.252898</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NK cells</td>\n",
       "      <td></td>\n",
       "      <td>NK cells</td>\n",
       "      <td>NK cells</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gene_nr  selection  rank  marker_rank  tree_rank  importance_score  \\\n",
       "PF4          1       True   1.0          1.0        1.0          1.000000   \n",
       "CD79A        2       True   1.0          1.0        1.0          0.895466   \n",
       "CCL5         3       True   1.0          1.0        1.0          0.545002   \n",
       "GZMB         4       True   1.0          1.0        1.0          0.530616   \n",
       "\n",
       "       pca_score  pre_selected  prior_selected  pca_selected  \\\n",
       "PF4     0.810399          True           False         False   \n",
       "CD79A   1.226536          True           False         False   \n",
       "CCL5    2.553322          True           False          True   \n",
       "GZMB    1.252898          True           False         False   \n",
       "\n",
       "      celltypes_DE_1vsall celltypes_DE_specific    celltypes_DE  \\\n",
       "PF4        Megakaryocytes                        Megakaryocytes   \n",
       "CD79A             B cells                               B cells   \n",
       "CCL5          CD8 T cells                           CD8 T cells   \n",
       "GZMB             NK cells                              NK cells   \n",
       "\n",
       "      celltypes_marker  list_only_ct_marker  required_marker  \\\n",
       "PF4     Megakaryocytes                False             True   \n",
       "CD79A          B cells                False             True   \n",
       "CCL5       CD8 T cells                False             True   \n",
       "GZMB          NK cells                False             True   \n",
       "\n",
       "       required_list_marker  \n",
       "PF4                   False  \n",
       "CD79A                 False  \n",
       "CCL5                  False  \n",
       "GZMB                  False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_nr</th>\n",
       "      <th>selection</th>\n",
       "      <th>rank</th>\n",
       "      <th>marker_rank</th>\n",
       "      <th>tree_rank</th>\n",
       "      <th>importance_score</th>\n",
       "      <th>pca_score</th>\n",
       "      <th>pre_selected</th>\n",
       "      <th>prior_selected</th>\n",
       "      <th>pca_selected</th>\n",
       "      <th>celltypes_DE_1vsall</th>\n",
       "      <th>celltypes_DE_specific</th>\n",
       "      <th>celltypes_DE</th>\n",
       "      <th>celltypes_marker</th>\n",
       "      <th>list_only_ct_marker</th>\n",
       "      <th>required_marker</th>\n",
       "      <th>required_list_marker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MS4A6A</th>\n",
       "      <td>39</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.003518</td>\n",
       "      <td>0.481730</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GIMAP4</th>\n",
       "      <td>40</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.001383</td>\n",
       "      <td>2.422619</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPX1</th>\n",
       "      <td>41</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.002822</td>\n",
       "      <td>2.025547</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Megakaryocytes</td>\n",
       "      <td></td>\n",
       "      <td>Megakaryocytes</td>\n",
       "      <td>Megakaryocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FCER1G</th>\n",
       "      <td>42</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010767</td>\n",
       "      <td>1.324965</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>FCGR3A+ Monocytes</td>\n",
       "      <td></td>\n",
       "      <td>FCGR3A+ Monocytes</td>\n",
       "      <td>FCGR3A+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gene_nr  selection  rank  marker_rank  tree_rank  importance_score  \\\n",
       "MS4A6A       39       True   1.0          NaN        3.0          0.003518   \n",
       "GIMAP4       40       True   1.0          NaN        3.0          0.001383   \n",
       "GPX1         41       True   2.0          2.0        1.0          0.002822   \n",
       "FCER1G       42       True   2.0          NaN        1.0          0.010767   \n",
       "\n",
       "        pca_score  pre_selected  prior_selected  pca_selected  \\\n",
       "MS4A6A   0.481730          True           False         False   \n",
       "GIMAP4   2.422619          True           False          True   \n",
       "GPX1     2.025547         False           False          True   \n",
       "FCER1G   1.324965         False           False          True   \n",
       "\n",
       "       celltypes_DE_1vsall celltypes_DE_specific       celltypes_DE  \\\n",
       "MS4A6A                                                                \n",
       "GIMAP4                                                                \n",
       "GPX1        Megakaryocytes                           Megakaryocytes   \n",
       "FCER1G   FCGR3A+ Monocytes                        FCGR3A+ Monocytes   \n",
       "\n",
       "         celltypes_marker  list_only_ct_marker  required_marker  \\\n",
       "MS4A6A                                   False            False   \n",
       "GIMAP4                                   False            False   \n",
       "GPX1       Megakaryocytes                False             True   \n",
       "FCER1G  FCGR3A+ Monocytes                False            False   \n",
       "\n",
       "        required_list_marker  \n",
       "MS4A6A                 False  \n",
       "GIMAP4                 False  \n",
       "GPX1                   False  \n",
       "FCER1G                 False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_nr</th>\n",
       "      <th>selection</th>\n",
       "      <th>rank</th>\n",
       "      <th>marker_rank</th>\n",
       "      <th>tree_rank</th>\n",
       "      <th>importance_score</th>\n",
       "      <th>pca_score</th>\n",
       "      <th>pre_selected</th>\n",
       "      <th>prior_selected</th>\n",
       "      <th>pca_selected</th>\n",
       "      <th>celltypes_DE_1vsall</th>\n",
       "      <th>celltypes_DE_specific</th>\n",
       "      <th>celltypes_DE</th>\n",
       "      <th>celltypes_marker</th>\n",
       "      <th>list_only_ct_marker</th>\n",
       "      <th>required_marker</th>\n",
       "      <th>required_list_marker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CD79B</th>\n",
       "      <td>49</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.022714</td>\n",
       "      <td>1.304566</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>B cells</td>\n",
       "      <td></td>\n",
       "      <td>B cells</td>\n",
       "      <td>B cells</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARL6IP5</th>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.016238</td>\n",
       "      <td>2.319210</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STK17A</th>\n",
       "      <td>51</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.006099</td>\n",
       "      <td>1.412628</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GIMAP7</th>\n",
       "      <td>52</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.001398</td>\n",
       "      <td>2.961351</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td></td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         gene_nr  selection  rank  marker_rank  tree_rank  importance_score  \\\n",
       "CD79B         49       True   3.0          NaN        3.0          0.022714   \n",
       "ARL6IP5       50       True   4.0          NaN        4.0          0.016238   \n",
       "STK17A        51      False   5.0          NaN        5.0          0.006099   \n",
       "GIMAP7        52      False   5.0          NaN        5.0          0.001398   \n",
       "\n",
       "         pca_score  pre_selected  prior_selected  pca_selected  \\\n",
       "CD79B     1.304566         False           False          True   \n",
       "ARL6IP5   2.319210         False           False          True   \n",
       "STK17A    1.412628         False           False          True   \n",
       "GIMAP7    2.961351         False           False          True   \n",
       "\n",
       "        celltypes_DE_1vsall celltypes_DE_specific celltypes_DE  \\\n",
       "CD79B               B cells                            B cells   \n",
       "ARL6IP5                                                          \n",
       "STK17A                                                           \n",
       "GIMAP7          CD4 T cells                        CD4 T cells   \n",
       "\n",
       "        celltypes_marker  list_only_ct_marker  required_marker  \\\n",
       "CD79B            B cells                False            False   \n",
       "ARL6IP5                                 False            False   \n",
       "STK17A                                  False            False   \n",
       "GIMAP7       CD4 T cells                False            False   \n",
       "\n",
       "         required_list_marker  \n",
       "CD79B                   False  \n",
       "ARL6IP5                 False  \n",
       "STK17A                  False  \n",
       "GIMAP7                  False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(selector.probeset.head(4))\n",
    "display(selector.probeset.iloc[38:42])\n",
    "display(selector.probeset.iloc[48:52])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that besides the `preselected_genes` parameter there is also the `prior_genes` parameter (for \"prioritized genes\"). This one acts similar to `preselected_genes`, however genes in `prior_genes` are not necessarily selected, they're only added to the pool of PCA preselected genes from which we build trees."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ii. Cell type classification only\n",
    "\n",
    "Spapros optimizes for two objectives:\n",
    "1. cell type identification\n",
    "2. recovery of transcriptomic variation\n",
    "\n",
    "The procedure prioritzes the 1st objective over the 2nd. The variation recovery also leads to the selection of genes that show gradients in multiple cell types (see the example of FOS in our [preprint](TODO: add link to preprint), Fig. 3). In classical gene panel selection approaches the focus lies on marker genes, ideally markers that are specific to single cell types. In the downstream analysis of spatial data genes like FOS are not useful for identifying the major cell types, these genes should even be excluded from the clustering to get better separated cell type clusters. If you don't want to select genes like FOS and only focus on the first objective you can skip the PCA-based selection by setting `n_pca_genes=0`. In that case only trees on DE genes are trained, the PCA related steps are neglected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: No PCA selection will be performed since n_pca_genes = 0. The selected genes will only be based on the DE forests. In that case it can happen that fewer than n = 30 genes are selected. To get n = 30 exclude the selected genes of the first run from adata, rerun the method, and combine the results of the two runs.\n",
      "\n",
      "Note: The following celltypes' test set sizes for forest training are below min_test_n (=20):\n",
      "\t Dendritic cells : 9\n",
      "\t Megakaryocytes  : 3\n",
      "The genes selected for those cell types potentially don't generalize well. Find the genes for each of those cell types in self.genes_of_primary_trees after running self.select_probeset().\n"
     ]
    }
   ],
   "source": [
    "# create an instance of the ProbesetSelector class\n",
    "selector = sp.se.ProbesetSelector(\n",
    "    adata,\n",
    "    n=30,\n",
    "    n_pca_genes=0,\n",
    "    celltype_key=\"celltype\",\n",
    "    verbosity=1,\n",
    "    save_dir=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d48e8247a2e4447be47d412435c8084",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select probe set\n",
    "selector.select_probeset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_nr</th>\n",
       "      <th>selection</th>\n",
       "      <th>rank</th>\n",
       "      <th>marker_rank</th>\n",
       "      <th>tree_rank</th>\n",
       "      <th>importance_score</th>\n",
       "      <th>pca_score</th>\n",
       "      <th>pre_selected</th>\n",
       "      <th>prior_selected</th>\n",
       "      <th>pca_selected</th>\n",
       "      <th>celltypes_DE_1vsall</th>\n",
       "      <th>celltypes_DE_specific</th>\n",
       "      <th>celltypes_DE</th>\n",
       "      <th>celltypes_marker</th>\n",
       "      <th>list_only_ct_marker</th>\n",
       "      <th>required_marker</th>\n",
       "      <th>required_list_marker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PF4</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Megakaryocytes</td>\n",
       "      <td></td>\n",
       "      <td>Megakaryocytes</td>\n",
       "      <td>Megakaryocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD79A</th>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.914911</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>B cells</td>\n",
       "      <td></td>\n",
       "      <td>B cells</td>\n",
       "      <td>B cells</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S100A8</th>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.831315</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td></td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gene_nr  selection  rank  marker_rank  tree_rank  importance_score  \\\n",
       "PF4           1       True   1.0          1.0        1.0          1.000000   \n",
       "CD79A         2       True   1.0          1.0        1.0          0.914911   \n",
       "S100A8        3       True   1.0          1.0        1.0          0.831315   \n",
       "\n",
       "        pca_score  pre_selected  prior_selected  pca_selected  \\\n",
       "PF4             0         False           False         False   \n",
       "CD79A           0         False           False         False   \n",
       "S100A8          0         False           False         False   \n",
       "\n",
       "       celltypes_DE_1vsall celltypes_DE_specific     celltypes_DE  \\\n",
       "PF4         Megakaryocytes                         Megakaryocytes   \n",
       "CD79A              B cells                                B cells   \n",
       "S100A8     CD14+ Monocytes                        CD14+ Monocytes   \n",
       "\n",
       "       celltypes_marker  list_only_ct_marker  required_marker  \\\n",
       "PF4      Megakaryocytes                False             True   \n",
       "CD79A           B cells                False             True   \n",
       "S100A8  CD14+ Monocytes                False             True   \n",
       "\n",
       "        required_list_marker  \n",
       "PF4                    False  \n",
       "CD79A                  False  \n",
       "S100A8                 False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_nr</th>\n",
       "      <th>selection</th>\n",
       "      <th>rank</th>\n",
       "      <th>marker_rank</th>\n",
       "      <th>tree_rank</th>\n",
       "      <th>importance_score</th>\n",
       "      <th>pca_score</th>\n",
       "      <th>pre_selected</th>\n",
       "      <th>prior_selected</th>\n",
       "      <th>pca_selected</th>\n",
       "      <th>celltypes_DE_1vsall</th>\n",
       "      <th>celltypes_DE_specific</th>\n",
       "      <th>celltypes_DE</th>\n",
       "      <th>celltypes_marker</th>\n",
       "      <th>list_only_ct_marker</th>\n",
       "      <th>required_marker</th>\n",
       "      <th>required_list_marker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FGFBP2</th>\n",
       "      <td>28</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009360</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>NK cells</td>\n",
       "      <td>NK cells</td>\n",
       "      <td>NK cells</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-DQB1</th>\n",
       "      <td>29</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008832</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>Dendritic cells</td>\n",
       "      <td>Dendritic cells</td>\n",
       "      <td>Dendritic cells</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTSW</th>\n",
       "      <td>30</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008201</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          gene_nr  selection  rank  marker_rank  tree_rank  importance_score  \\\n",
       "FGFBP2         28       True   1.0          NaN        1.0          0.009360   \n",
       "HLA-DQB1       29       True   1.0          NaN        1.0          0.008832   \n",
       "CTSW           30       True   1.0          NaN        1.0          0.008201   \n",
       "\n",
       "          pca_score  pre_selected  prior_selected  pca_selected  \\\n",
       "FGFBP2            0         False           False         False   \n",
       "HLA-DQB1          0         False           False         False   \n",
       "CTSW              0         False           False         False   \n",
       "\n",
       "         celltypes_DE_1vsall celltypes_DE_specific     celltypes_DE  \\\n",
       "FGFBP2                                    NK cells         NK cells   \n",
       "HLA-DQB1                           Dendritic cells  Dendritic cells   \n",
       "CTSW                                   CD8 T cells      CD8 T cells   \n",
       "\n",
       "         celltypes_marker  list_only_ct_marker  required_marker  \\\n",
       "FGFBP2           NK cells                False            False   \n",
       "HLA-DQB1  Dendritic cells                False            False   \n",
       "CTSW          CD8 T cells                False            False   \n",
       "\n",
       "          required_list_marker  \n",
       "FGFBP2                   False  \n",
       "HLA-DQB1                 False  \n",
       "CTSW                     False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(selector.probeset[selector.probeset[\"selection\"]].head(3))\n",
    "display(selector.probeset[selector.probeset[\"selection\"]].tail(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### iii. Selection for high numbers of genes (> 150)\n",
    "\n",
    "Spapros works well over different ranges of numbers of genes to select. However, for higher numbers of genes (~ > 150, can be lower/higher for lower/higher nr of cell types) a dual selection can make sense. This is e.g. relevant for protocols like MERFISH, and also if you want to increase the robustness on the cell type classification objective (less focus on recovery of variation that is independent of cell type labels).\n",
    "\n",
    "The idea is to run a selection first (we do not fix `n` and we do not use PCA selected genes for that run), exclude the genes of that selection, run a second (standard) selection, and combine the results of both runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "898c4f2210494fcabd87d1967404e007",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: The following celltypes' test set sizes for forest training are below min_test_n (=20):\n",
      "\t Dendritic cells : 9\n",
      "\t Megakaryocytes  : 3\n",
      "The genes selected for those cell types potentially don't generalize well. Find the genes for each of those cell types in self.genes_of_primary_trees after running self.select_probeset().\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First run\n",
    "\n",
    "# create an instance of the ProbesetSelector class\n",
    "selector = sp.se.ProbesetSelector(\n",
    "    adata,\n",
    "    n=None,\n",
    "    n_pca_genes=0,\n",
    "    celltype_key=\"celltype\",\n",
    "    verbosity=1,\n",
    "    save_dir=None,\n",
    ")\n",
    "\n",
    "# select probe set\n",
    "selector.select_probeset()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33\n"
     ]
    }
   ],
   "source": [
    "# Save genes of the first selection\n",
    "first_selection = selector.probeset[selector.probeset[\"selection\"]].index.tolist()\n",
    "n_genes_first_selection = len(first_selection)\n",
    "print(n_genes_first_selection)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the number of cell types is quite low in our example dataset the first selection only lead to 33 genes. Next we select the rest of genes to get to 150."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d4ca3586be2431f9089c80949cf9867",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: The following celltypes' test set sizes for forest training are below min_test_n (=20):\n",
      "\t Dendritic cells : 9\n",
      "\t Megakaryocytes  : 3\n",
      "The genes selected for those cell types potentially don't generalize well. Find the genes for each of those cell types in self.genes_of_primary_trees after running self.select_probeset().\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Second run\n",
    "\n",
    "# create an instance of the ProbesetSelector class\n",
    "selector2 = sp.se.ProbesetSelector(\n",
    "    adata[:,[v for v in adata.var_names if v not in first_selection]],\n",
    "    n=150 - n_genes_first_selection,\n",
    "    n_pca_genes=100,\n",
    "    celltype_key=\"celltype\",\n",
    "    verbosity=1,\n",
    "    save_dir=None,\n",
    ")\n",
    "\n",
    "# select probe set\n",
    "selector2.select_probeset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150\n"
     ]
    }
   ],
   "source": [
    "second_selection = selector2.probeset[selector2.probeset[\"selection\"]].index.tolist()\n",
    "final_selection = first_selection + second_selection\n",
    "print(len(np.unique(final_selection)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This adds up to a final gene set of 150 genes.  \n",
    "\n",
    "For even higher numbers of genes you might want to consider running multiple additional pre selections with `n_pca_genes=0`.   \n",
    "\n",
    "Side note: unfortunately our plotting functions are only accessible with each individual selector and not the combination of multiple selectors. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### iv. Selection with curated marker list\n",
    "\n",
    "Often you already have some curated list of markers. Such a list can be added as prior knowledge to achieve the following aims   \n",
    "\n",
    "- make sure that the signal of the markers in the list are captured by the selected probe set otherwise add markers from the list   \n",
    "- add markers for cell types that don't occur in `adata.obs[celltype_key]`   \n",
    "\n",
    "The criteria how markers are added or how the \"signal is captured\" is described in the following. Note however that if you just want to add certain genes you can also add them with the `preselected_genes` (see [i.](#i.-Select-a-few-additional-genes)) parameter.\n",
    "\n",
    "Let's group the cell types in the `marker_list` into:   \n",
    "\n",
    "- *ct_group1*: cell types that also occur in `adata.obs[celltype_key]`   \n",
    "- *ct_group2*: cell types that **do not** occur in `adata.obs[celltype_key]`   \n",
    "   \n",
    "The key parameters of the `ProbesetSelector` that define how marker signals are checked for and if markers should be added are:   \n",
    "\n",
    "- `n_list_markers`   \n",
    "- `marker_corr_th`   \n",
    "\n",
    "You can set `n_list_markers` to an `int`, then the method will take care to have captured at least `n_list_markers` per cell type. Note however that the *ct_group1* cell types will also already be captured by the genes of the trained trees. Therefore it can make sense to have different numbers for the two groups, which is achieved by setting e.g. `n_list_markers = {'adata_celltypes':2, 'list_celltypes':3}`. As mentioned above the markers for *ct_group1* and *ct_group2* are handeled differently. For *ct_group1* the method checks if it already selected `n_list_markers` that have correlations above `marker_corr_th` (default: 0.5) with at least `n_list_markers` for a given cell type, otherwise markers from the list are added. For *ct_group2* the correlation criteria doesn't make much sense since the correlation is calculated on the given cell types in `adata` and those shouldn't express a marker for a cell type of *ct_group2*. Therefore from *ct_group2* `n_list_markers` markers are just added.\n",
    "\n",
    "Note that there is also the `n_min_markers` parameter. This one makes sure that enough genes either from DE tests during the selection or (if given) from the `marker_list` are selected per cell type of *ct_group1*. So `n_min_markers` also applies if no `marker_list` is given."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define an example marker list. We added `'cell type A'` as a *ct_group2* (i.e. not in `adata.obs[celltype_key]`) cell type example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "marker_list = {\n",
    "    'CD4 T cells': ['IL7R'], \n",
    "    'CD14+ Monocytes': ['CD14', 'LYZ'], \n",
    "    'B cells': ['MS4A1'], \n",
    "    'CD8 T cells': ['CD8A'], \n",
    "    'NK cells': ['GNLY', 'NKG7'], \n",
    "    'FCGR3A+ Monocytes': ['FCGR3A', 'MS4A7'], \n",
    "    'Dendritic cells': ['FCER1A', 'CST3'], \n",
    "    'Megakaryocytes': ['NAPA-AS1', 'PPBP'],\n",
    "    'cell type A': [\"Gene X\", \"Gene Y\", \"CAMK2N1\"],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c009f98fd2f43ce87244d977b28ff27",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: The following celltypes' test set sizes for forest training are below min_test_n (=20):\n",
      "\t Dendritic cells : 9\n",
      "\t Megakaryocytes  : 3\n",
      "The genes selected for those cell types potentially don't generalize well. Find the genes for each of those cell types in self.genes_of_primary_trees after running self.select_probeset().\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create an instance of the ProbesetSelector class\n",
    "selector = sp.se.ProbesetSelector(\n",
    "    adata,\n",
    "    n=None,\n",
    "    celltype_key=\"celltype\",\n",
    "    verbosity=1,\n",
    "    save_dir=None,\n",
    "    marker_list=marker_list,\n",
    "    n_list_markers={'adata_celltypes': 2, 'list_celltypes': 3},\n",
    ")\n",
    "\n",
    "# select probe set\n",
    "selector.select_probeset()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_nr</th>\n",
       "      <th>selection</th>\n",
       "      <th>rank</th>\n",
       "      <th>marker_rank</th>\n",
       "      <th>tree_rank</th>\n",
       "      <th>importance_score</th>\n",
       "      <th>pca_score</th>\n",
       "      <th>pre_selected</th>\n",
       "      <th>prior_selected</th>\n",
       "      <th>pca_selected</th>\n",
       "      <th>celltypes_DE_1vsall</th>\n",
       "      <th>celltypes_DE_specific</th>\n",
       "      <th>celltypes_DE</th>\n",
       "      <th>celltypes_marker</th>\n",
       "      <th>list_only_ct_marker</th>\n",
       "      <th>required_marker</th>\n",
       "      <th>required_list_marker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FCGR3A</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.612390</td>\n",
       "      <td>1.321373</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>FCGR3A+ Monocytes</td>\n",
       "      <td>FCGR3A+ Monocytes</td>\n",
       "      <td>FCGR3A+ Monocytes,FCGR3A+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKG7</th>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.248463</td>\n",
       "      <td>1.658470</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NK cells,CD8 T cells</td>\n",
       "      <td></td>\n",
       "      <td>NK cells,CD8 T cells</td>\n",
       "      <td>NK cells,CD8 T cells,NK cells</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CST3</th>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.056431</td>\n",
       "      <td>1.557674</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>CD14+ Monocytes,Dendritic cells</td>\n",
       "      <td></td>\n",
       "      <td>CD14+ Monocytes,Dendritic cells</td>\n",
       "      <td>CD14+ Monocytes,Dendritic cells,Dendritic cells</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS4A1</th>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012243</td>\n",
       "      <td>0.814808</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>B cells</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-DPB1</th>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.649235</td>\n",
       "      <td>1.997253</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>B cells,Dendritic cells</td>\n",
       "      <td></td>\n",
       "      <td>B cells,Dendritic cells</td>\n",
       "      <td>B cells,Dendritic cells</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCL5</th>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.478418</td>\n",
       "      <td>2.553322</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td></td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL32</th>\n",
       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.185111</td>\n",
       "      <td>3.377428</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td></td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GNLY</th>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.075589</td>\n",
       "      <td>2.167588</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NK cells</td>\n",
       "      <td></td>\n",
       "      <td>NK cells</td>\n",
       "      <td>NK cells,NK cells</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FCER1A</th>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.018118</td>\n",
       "      <td>0.298179</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>Dendritic cells</td>\n",
       "      <td>Dendritic cells</td>\n",
       "      <td>Dendritic cells,Dendritic cells</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PF4</th>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.810399</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Megakaryocytes</td>\n",
       "      <td></td>\n",
       "      <td>Megakaryocytes</td>\n",
       "      <td>Megakaryocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-DQA1</th>\n",
       "      <td>11</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.562045</td>\n",
       "      <td>0.755408</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>Dendritic cells</td>\n",
       "      <td>Dendritic cells</td>\n",
       "      <td>Dendritic cells</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GZMB</th>\n",
       "      <td>12</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.512924</td>\n",
       "      <td>1.252898</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NK cells</td>\n",
       "      <td></td>\n",
       "      <td>NK cells</td>\n",
       "      <td>NK cells</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIF1</th>\n",
       "      <td>13</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.238008</td>\n",
       "      <td>1.527263</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>FCGR3A+ Monocytes</td>\n",
       "      <td></td>\n",
       "      <td>FCGR3A+ Monocytes</td>\n",
       "      <td>FCGR3A+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD79A</th>\n",
       "      <td>14</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.222419</td>\n",
       "      <td>1.226536</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>B cells</td>\n",
       "      <td></td>\n",
       "      <td>B cells</td>\n",
       "      <td>B cells</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S100A8</th>\n",
       "      <td>15</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.203852</td>\n",
       "      <td>2.254612</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td></td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-DMA</th>\n",
       "      <td>16</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167341</td>\n",
       "      <td>0.694292</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LTB</th>\n",
       "      <td>17</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.139606</td>\n",
       "      <td>3.029459</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td></td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GZMK</th>\n",
       "      <td>18</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.124733</td>\n",
       "      <td>1.680233</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-DRB1</th>\n",
       "      <td>19</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.097574</td>\n",
       "      <td>1.538738</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TYROBP</th>\n",
       "      <td>20</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.093040</td>\n",
       "      <td>1.352422</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td></td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FCN1</th>\n",
       "      <td>21</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.088904</td>\n",
       "      <td>1.070598</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S100A11</th>\n",
       "      <td>22</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.078515</td>\n",
       "      <td>2.468161</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GZMH</th>\n",
       "      <td>23</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.058625</td>\n",
       "      <td>1.264788</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RBM39</th>\n",
       "      <td>24</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.036062</td>\n",
       "      <td>0.657891</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-DPA1</th>\n",
       "      <td>25</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024234</td>\n",
       "      <td>1.429546</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Dendritic cells</td>\n",
       "      <td></td>\n",
       "      <td>Dendritic cells</td>\n",
       "      <td>Dendritic cells</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LST1</th>\n",
       "      <td>26</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.023708</td>\n",
       "      <td>1.263540</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>FCGR3A+ Monocytes</td>\n",
       "      <td></td>\n",
       "      <td>FCGR3A+ Monocytes</td>\n",
       "      <td>FCGR3A+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGALS2</th>\n",
       "      <td>27</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.023468</td>\n",
       "      <td>1.126618</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRAF3IP3</th>\n",
       "      <td>28</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011468</td>\n",
       "      <td>2.615410</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRF1</th>\n",
       "      <td>29</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010033</td>\n",
       "      <td>0.807865</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>NK cells</td>\n",
       "      <td>NK cells</td>\n",
       "      <td>NK cells</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGALS1</th>\n",
       "      <td>30</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009938</td>\n",
       "      <td>2.895099</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRDX1</th>\n",
       "      <td>31</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009259</td>\n",
       "      <td>0.625947</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATP5O</th>\n",
       "      <td>32</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007816</td>\n",
       "      <td>2.455435</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C1orf162</th>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007754</td>\n",
       "      <td>0.889644</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTSS</th>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007547</td>\n",
       "      <td>2.285013</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SELL</th>\n",
       "      <td>35</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007499</td>\n",
       "      <td>2.800353</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTSW</th>\n",
       "      <td>36</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006245</td>\n",
       "      <td>1.300789</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GIMAP4</th>\n",
       "      <td>37</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005317</td>\n",
       "      <td>2.422619</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LINC00926</th>\n",
       "      <td>38</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004536</td>\n",
       "      <td>0.563208</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS4A6A</th>\n",
       "      <td>39</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004139</td>\n",
       "      <td>0.481730</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GSTP1</th>\n",
       "      <td>40</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003561</td>\n",
       "      <td>1.483085</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PDIA3</th>\n",
       "      <td>41</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003147</td>\n",
       "      <td>0.749059</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOPX</th>\n",
       "      <td>42</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003119</td>\n",
       "      <td>0.910448</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERH</th>\n",
       "      <td>43</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.002811</td>\n",
       "      <td>0.586232</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COTL1</th>\n",
       "      <td>44</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000730</td>\n",
       "      <td>2.993346</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>FCGR3A+ Monocytes</td>\n",
       "      <td>FCGR3A+ Monocytes</td>\n",
       "      <td>FCGR3A+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMK2N1</th>\n",
       "      <td>45</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.029484</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>cell type A</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gene X</th>\n",
       "      <td>46</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>cell type A</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gene Y</th>\n",
       "      <td>47</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>cell type A</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPBP</th>\n",
       "      <td>48</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.163331</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Megakaryocytes</td>\n",
       "      <td></td>\n",
       "      <td>Megakaryocytes</td>\n",
       "      <td>Megakaryocytes,Megakaryocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD14</th>\n",
       "      <td>49</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD8A</th>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>CD8 T cells</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL7R</th>\n",
       "      <td>51</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>CD4 T cells</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAPA-AS1</th>\n",
       "      <td>52</td>\n",
       "      <td>True</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.035167</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Megakaryocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LYZ</th>\n",
       "      <td>53</td>\n",
       "      <td>True</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>CD14+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS4A7</th>\n",
       "      <td>54</td>\n",
       "      <td>True</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>FCGR3A+ Monocytes</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           gene_nr  selection  rank  marker_rank  tree_rank  importance_score  \\\n",
       "FCGR3A           1       True   1.0          1.0        1.0          0.612390   \n",
       "NKG7             2       True   1.0          1.0        1.0          0.248463   \n",
       "CST3             3       True   1.0          1.0        1.0          0.056431   \n",
       "MS4A1            4       True   1.0          1.0        1.0          0.012243   \n",
       "HLA-DPB1         5       True   1.0          2.0        1.0          0.649235   \n",
       "CCL5             6       True   1.0          2.0        1.0          0.478418   \n",
       "IL32             7       True   1.0          2.0        1.0          0.185111   \n",
       "GNLY             8       True   1.0          2.0        1.0          0.075589   \n",
       "FCER1A           9       True   1.0          2.0        1.0          0.018118   \n",
       "PF4             10       True   1.0          NaN        1.0          1.000000   \n",
       "HLA-DQA1        11       True   1.0          NaN        1.0          0.562045   \n",
       "GZMB            12       True   1.0          NaN        1.0          0.512924   \n",
       "AIF1            13       True   1.0          NaN        1.0          0.238008   \n",
       "CD79A           14       True   1.0          NaN        1.0          0.222419   \n",
       "S100A8          15       True   1.0          NaN        1.0          0.203852   \n",
       "HLA-DMA         16       True   1.0          NaN        1.0          0.167341   \n",
       "LTB             17       True   1.0          NaN        1.0          0.139606   \n",
       "GZMK            18       True   1.0          NaN        1.0          0.124733   \n",
       "HLA-DRB1        19       True   1.0          NaN        1.0          0.097574   \n",
       "TYROBP          20       True   1.0          NaN        1.0          0.093040   \n",
       "FCN1            21       True   1.0          NaN        1.0          0.088904   \n",
       "S100A11         22       True   1.0          NaN        1.0          0.078515   \n",
       "GZMH            23       True   1.0          NaN        1.0          0.058625   \n",
       "RBM39           24       True   1.0          NaN        1.0          0.036062   \n",
       "HLA-DPA1        25       True   1.0          NaN        1.0          0.024234   \n",
       "LST1            26       True   1.0          NaN        1.0          0.023708   \n",
       "LGALS2          27       True   1.0          NaN        1.0          0.023468   \n",
       "TRAF3IP3        28       True   1.0          NaN        1.0          0.011468   \n",
       "PRF1            29       True   1.0          NaN        1.0          0.010033   \n",
       "LGALS1          30       True   1.0          NaN        1.0          0.009938   \n",
       "PRDX1           31       True   1.0          NaN        1.0          0.009259   \n",
       "ATP5O           32       True   1.0          NaN        1.0          0.007816   \n",
       "C1orf162        33       True   1.0          NaN        1.0          0.007754   \n",
       "CTSS            34       True   1.0          NaN        1.0          0.007547   \n",
       "SELL            35       True   1.0          NaN        1.0          0.007499   \n",
       "CTSW            36       True   1.0          NaN        1.0          0.006245   \n",
       "GIMAP4          37       True   1.0          NaN        1.0          0.005317   \n",
       "LINC00926       38       True   1.0          NaN        1.0          0.004536   \n",
       "MS4A6A          39       True   1.0          NaN        1.0          0.004139   \n",
       "GSTP1           40       True   1.0          NaN        1.0          0.003561   \n",
       "PDIA3           41       True   1.0          NaN        1.0          0.003147   \n",
       "HOPX            42       True   1.0          NaN        1.0          0.003119   \n",
       "ERH             43       True   1.0          NaN        1.0          0.002811   \n",
       "COTL1           44       True   1.0          NaN        1.0          0.000730   \n",
       "CAMK2N1         45       True   2.0          1.0        NaN               NaN   \n",
       "Gene X          46       True   2.0          2.0        NaN               NaN   \n",
       "Gene Y          47       True   2.0          3.0        NaN               NaN   \n",
       "PPBP            48       True   3.0          1.0        2.0          1.000000   \n",
       "CD14            49       True   3.0          1.0        NaN               NaN   \n",
       "CD8A            50       True   3.0          1.0        NaN               NaN   \n",
       "IL7R            51       True   3.0          1.0        NaN               NaN   \n",
       "NAPA-AS1        52       True   4.0          2.0        NaN               NaN   \n",
       "LYZ             53       True   4.0          2.0        NaN               NaN   \n",
       "MS4A7           54       True   4.0          2.0        NaN               NaN   \n",
       "\n",
       "           pca_score  pre_selected  prior_selected  pca_selected  \\\n",
       "FCGR3A      1.321373         False           False          True   \n",
       "NKG7        1.658470         False           False          True   \n",
       "CST3        1.557674         False           False          True   \n",
       "MS4A1       0.814808         False           False          True   \n",
       "HLA-DPB1    1.997253         False           False          True   \n",
       "CCL5        2.553322         False           False          True   \n",
       "IL32        3.377428         False           False          True   \n",
       "GNLY        2.167588         False           False          True   \n",
       "FCER1A      0.298179         False           False         False   \n",
       "PF4         0.810399         False           False          True   \n",
       "HLA-DQA1    0.755408         False           False          True   \n",
       "GZMB        1.252898         False           False          True   \n",
       "AIF1        1.527263         False           False          True   \n",
       "CD79A       1.226536         False           False          True   \n",
       "S100A8      2.254612         False           False          True   \n",
       "HLA-DMA     0.694292         False           False          True   \n",
       "LTB         3.029459         False           False          True   \n",
       "GZMK        1.680233         False           False          True   \n",
       "HLA-DRB1    1.538738         False           False          True   \n",
       "TYROBP      1.352422         False           False          True   \n",
       "FCN1        1.070598         False           False          True   \n",
       "S100A11     2.468161         False           False          True   \n",
       "GZMH        1.264788         False           False          True   \n",
       "RBM39       0.657891         False           False          True   \n",
       "HLA-DPA1    1.429546         False           False          True   \n",
       "LST1        1.263540         False           False          True   \n",
       "LGALS2      1.126618         False           False          True   \n",
       "TRAF3IP3    2.615410         False           False          True   \n",
       "PRF1        0.807865         False           False          True   \n",
       "LGALS1      2.895099         False           False          True   \n",
       "PRDX1       0.625947         False           False          True   \n",
       "ATP5O       2.455435         False           False          True   \n",
       "C1orf162    0.889644         False           False          True   \n",
       "CTSS        2.285013         False           False          True   \n",
       "SELL        2.800353         False           False          True   \n",
       "CTSW        1.300789         False           False          True   \n",
       "GIMAP4      2.422619         False           False          True   \n",
       "LINC00926   0.563208         False           False          True   \n",
       "MS4A6A      0.481730         False           False          True   \n",
       "GSTP1       1.483085         False           False          True   \n",
       "PDIA3       0.749059         False           False          True   \n",
       "HOPX        0.910448         False           False          True   \n",
       "ERH         0.586232         False           False          True   \n",
       "COTL1       2.993346         False           False          True   \n",
       "CAMK2N1     0.029484         False           False         False   \n",
       "Gene X      0.000000         False           False         False   \n",
       "Gene Y      0.000000         False           False         False   \n",
       "PPBP        1.163331         False           False          True   \n",
       "CD14        0.000000         False           False         False   \n",
       "CD8A        0.000000         False           False         False   \n",
       "IL7R        0.000000         False           False         False   \n",
       "NAPA-AS1    0.035167         False           False         False   \n",
       "LYZ         0.000000         False           False         False   \n",
       "MS4A7       0.000000         False           False         False   \n",
       "\n",
       "                       celltypes_DE_1vsall celltypes_DE_specific  \\\n",
       "FCGR3A                                         FCGR3A+ Monocytes   \n",
       "NKG7                  NK cells,CD8 T cells                         \n",
       "CST3       CD14+ Monocytes,Dendritic cells                         \n",
       "MS4A1                                                              \n",
       "HLA-DPB1           B cells,Dendritic cells                         \n",
       "CCL5                           CD8 T cells                         \n",
       "IL32                           CD4 T cells                         \n",
       "GNLY                              NK cells                         \n",
       "FCER1A                                           Dendritic cells   \n",
       "PF4                         Megakaryocytes                         \n",
       "HLA-DQA1                                         Dendritic cells   \n",
       "GZMB                              NK cells                         \n",
       "AIF1                     FCGR3A+ Monocytes                         \n",
       "CD79A                              B cells                         \n",
       "S100A8                     CD14+ Monocytes                         \n",
       "HLA-DMA                                                            \n",
       "LTB                            CD4 T cells                         \n",
       "GZMK                                                 CD8 T cells   \n",
       "HLA-DRB1                                                           \n",
       "TYROBP                     CD14+ Monocytes                         \n",
       "FCN1                                             CD14+ Monocytes   \n",
       "S100A11                                                            \n",
       "GZMH                                                               \n",
       "RBM39                                                              \n",
       "HLA-DPA1                   Dendritic cells                         \n",
       "LST1                     FCGR3A+ Monocytes                         \n",
       "LGALS2                                           CD14+ Monocytes   \n",
       "TRAF3IP3                                                           \n",
       "PRF1                                                    NK cells   \n",
       "LGALS1                                                             \n",
       "PRDX1                                                              \n",
       "ATP5O                                                              \n",
       "C1orf162                                                           \n",
       "CTSS                                             CD14+ Monocytes   \n",
       "SELL                                                               \n",
       "CTSW                                                 CD8 T cells   \n",
       "GIMAP4                                                             \n",
       "LINC00926                                                          \n",
       "MS4A6A                                                             \n",
       "GSTP1                                                              \n",
       "PDIA3                                                              \n",
       "HOPX                                                               \n",
       "ERH                                                                \n",
       "COTL1                                          FCGR3A+ Monocytes   \n",
       "CAMK2N1                                                            \n",
       "Gene X                                                             \n",
       "Gene Y                                                             \n",
       "PPBP                        Megakaryocytes                         \n",
       "CD14                                                               \n",
       "CD8A                                                               \n",
       "IL7R                                                               \n",
       "NAPA-AS1                                                           \n",
       "LYZ                                                                \n",
       "MS4A7                                                              \n",
       "\n",
       "                              celltypes_DE  \\\n",
       "FCGR3A                   FCGR3A+ Monocytes   \n",
       "NKG7                  NK cells,CD8 T cells   \n",
       "CST3       CD14+ Monocytes,Dendritic cells   \n",
       "MS4A1                                        \n",
       "HLA-DPB1           B cells,Dendritic cells   \n",
       "CCL5                           CD8 T cells   \n",
       "IL32                           CD4 T cells   \n",
       "GNLY                              NK cells   \n",
       "FCER1A                     Dendritic cells   \n",
       "PF4                         Megakaryocytes   \n",
       "HLA-DQA1                   Dendritic cells   \n",
       "GZMB                              NK cells   \n",
       "AIF1                     FCGR3A+ Monocytes   \n",
       "CD79A                              B cells   \n",
       "S100A8                     CD14+ Monocytes   \n",
       "HLA-DMA                                      \n",
       "LTB                            CD4 T cells   \n",
       "GZMK                           CD8 T cells   \n",
       "HLA-DRB1                                     \n",
       "TYROBP                     CD14+ Monocytes   \n",
       "FCN1                       CD14+ Monocytes   \n",
       "S100A11                                      \n",
       "GZMH                                         \n",
       "RBM39                                        \n",
       "HLA-DPA1                   Dendritic cells   \n",
       "LST1                     FCGR3A+ Monocytes   \n",
       "LGALS2                     CD14+ Monocytes   \n",
       "TRAF3IP3                                     \n",
       "PRF1                              NK cells   \n",
       "LGALS1                                       \n",
       "PRDX1                                        \n",
       "ATP5O                                        \n",
       "C1orf162                                     \n",
       "CTSS                       CD14+ Monocytes   \n",
       "SELL                                         \n",
       "CTSW                           CD8 T cells   \n",
       "GIMAP4                                       \n",
       "LINC00926                                    \n",
       "MS4A6A                                       \n",
       "GSTP1                                        \n",
       "PDIA3                                        \n",
       "HOPX                                         \n",
       "ERH                                          \n",
       "COTL1                    FCGR3A+ Monocytes   \n",
       "CAMK2N1                                      \n",
       "Gene X                                       \n",
       "Gene Y                                       \n",
       "PPBP                        Megakaryocytes   \n",
       "CD14                                         \n",
       "CD8A                                         \n",
       "IL7R                                         \n",
       "NAPA-AS1                                     \n",
       "LYZ                                          \n",
       "MS4A7                                        \n",
       "\n",
       "                                          celltypes_marker  \\\n",
       "FCGR3A                 FCGR3A+ Monocytes,FCGR3A+ Monocytes   \n",
       "NKG7                         NK cells,CD8 T cells,NK cells   \n",
       "CST3       CD14+ Monocytes,Dendritic cells,Dendritic cells   \n",
       "MS4A1                                              B cells   \n",
       "HLA-DPB1                           B cells,Dendritic cells   \n",
       "CCL5                                           CD8 T cells   \n",
       "IL32                                           CD4 T cells   \n",
       "GNLY                                     NK cells,NK cells   \n",
       "FCER1A                     Dendritic cells,Dendritic cells   \n",
       "PF4                                         Megakaryocytes   \n",
       "HLA-DQA1                                   Dendritic cells   \n",
       "GZMB                                              NK cells   \n",
       "AIF1                                     FCGR3A+ Monocytes   \n",
       "CD79A                                              B cells   \n",
       "S100A8                                     CD14+ Monocytes   \n",
       "HLA-DMA                                                      \n",
       "LTB                                            CD4 T cells   \n",
       "GZMK                                           CD8 T cells   \n",
       "HLA-DRB1                                                     \n",
       "TYROBP                                     CD14+ Monocytes   \n",
       "FCN1                                       CD14+ Monocytes   \n",
       "S100A11                                                      \n",
       "GZMH                                                         \n",
       "RBM39                                                        \n",
       "HLA-DPA1                                   Dendritic cells   \n",
       "LST1                                     FCGR3A+ Monocytes   \n",
       "LGALS2                                     CD14+ Monocytes   \n",
       "TRAF3IP3                                                     \n",
       "PRF1                                              NK cells   \n",
       "LGALS1                                                       \n",
       "PRDX1                                                        \n",
       "ATP5O                                                        \n",
       "C1orf162                                                     \n",
       "CTSS                                       CD14+ Monocytes   \n",
       "SELL                                                         \n",
       "CTSW                                           CD8 T cells   \n",
       "GIMAP4                                                       \n",
       "LINC00926                                                    \n",
       "MS4A6A                                                       \n",
       "GSTP1                                                        \n",
       "PDIA3                                                        \n",
       "HOPX                                                         \n",
       "ERH                                                          \n",
       "COTL1                                    FCGR3A+ Monocytes   \n",
       "CAMK2N1                                        cell type A   \n",
       "Gene X                                         cell type A   \n",
       "Gene Y                                         cell type A   \n",
       "PPBP                         Megakaryocytes,Megakaryocytes   \n",
       "CD14                                       CD14+ Monocytes   \n",
       "CD8A                                           CD8 T cells   \n",
       "IL7R                                           CD4 T cells   \n",
       "NAPA-AS1                                    Megakaryocytes   \n",
       "LYZ                                        CD14+ Monocytes   \n",
       "MS4A7                                    FCGR3A+ Monocytes   \n",
       "\n",
       "           list_only_ct_marker  required_marker  required_list_marker  \n",
       "FCGR3A                   False             True                 False  \n",
       "NKG7                     False             True                 False  \n",
       "CST3                     False             True                 False  \n",
       "MS4A1                    False             True                 False  \n",
       "HLA-DPB1                 False             True                 False  \n",
       "CCL5                     False             True                 False  \n",
       "IL32                     False             True                 False  \n",
       "GNLY                     False             True                 False  \n",
       "FCER1A                   False             True                 False  \n",
       "PF4                      False            False                 False  \n",
       "HLA-DQA1                 False            False                 False  \n",
       "GZMB                     False            False                 False  \n",
       "AIF1                     False            False                 False  \n",
       "CD79A                    False            False                 False  \n",
       "S100A8                   False            False                 False  \n",
       "HLA-DMA                  False            False                 False  \n",
       "LTB                      False            False                 False  \n",
       "GZMK                     False            False                 False  \n",
       "HLA-DRB1                 False            False                 False  \n",
       "TYROBP                   False            False                 False  \n",
       "FCN1                     False            False                 False  \n",
       "S100A11                  False            False                 False  \n",
       "GZMH                     False            False                 False  \n",
       "RBM39                    False            False                 False  \n",
       "HLA-DPA1                 False            False                 False  \n",
       "LST1                     False            False                 False  \n",
       "LGALS2                   False            False                 False  \n",
       "TRAF3IP3                 False            False                 False  \n",
       "PRF1                     False            False                 False  \n",
       "LGALS1                   False            False                 False  \n",
       "PRDX1                    False            False                 False  \n",
       "ATP5O                    False            False                 False  \n",
       "C1orf162                 False            False                 False  \n",
       "CTSS                     False            False                 False  \n",
       "SELL                     False            False                 False  \n",
       "CTSW                     False            False                 False  \n",
       "GIMAP4                   False            False                 False  \n",
       "LINC00926                False            False                 False  \n",
       "MS4A6A                   False            False                 False  \n",
       "GSTP1                    False            False                 False  \n",
       "PDIA3                    False            False                 False  \n",
       "HOPX                     False            False                 False  \n",
       "ERH                      False            False                 False  \n",
       "COTL1                    False            False                 False  \n",
       "CAMK2N1                   True             True                 False  \n",
       "Gene X                    True             True                 False  \n",
       "Gene Y                    True             True                 False  \n",
       "PPBP                     False             True                 False  \n",
       "CD14                     False             True                 False  \n",
       "CD8A                     False             True                 False  \n",
       "IL7R                     False             True                 False  \n",
       "NAPA-AS1                 False             True                 False  \n",
       "LYZ                      False             True                 False  \n",
       "MS4A7                    False             True                 False  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selector.probeset[selector.probeset[\"selection\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The required markers that don't occur in the trees (tree_rank: NaN) are added to fulfill the `n_list_markers` criteria. Note that the markers for cell type A are ranked higher compared to the markers of the other cell types as those cell types are already captured by the `tree_rank=1` genes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### v. Selection with expression constraints\n",
    "\n",
    "**Why epxression constraints?**\n",
    "\n",
    "For different technologies there can be different constraints on the expression levels of genes, e.g. to prevent optical crowding for methods with combinatorial barcoding (MERFISH, ISS, ..). \n",
    "\n",
    "**How expression constraints work**\n",
    "\n",
    "We support expression constraints by setting thresholds for gene expression levels. In practice it is really difficult to set exact thresholds, therefore we introduce smoothed penalty functions that penalize genes based on their expression. I.e. if a gene is just a bit above the threshold but is very important for the selection it will still be selected.\n",
    "\n",
    "In general you can apply any function to penalize genes or just assign values to genes. The value for each gene needs to be given in `adata.obs[penalty_key]` and should be in [0,1] (0: maximal penalty, 1: no penalty). During the selection the penalties apply at 3 steps:   \n",
    "\n",
    "- PCA-based selection - for each gene the pca score is multipled with the penalty value   \n",
    "- DE-based selection - for each gene the DE score is multipled with the penalty value   \n",
    "- adding markers from a curated list (`marker_list`) - filter genes that have a penalty value below 1 (the threshold can be adjusted via `marker_selection_hparams['penalty_threshold'])`)   \n",
    "\n",
    "**High and low expression constraints**\n",
    "\n",
    "We will construct two constraints: one for too highly expressed genes and one for too lowly expressed genes. We will base them on quantiles of the genes' expression distributions.   \n",
    "\n",
    "- we consider a gene as too highly expressed if at least 1% of cells are above the threshold, i.e. `the 0.99 quantile > upper_threshold`   \n",
    "- we consider a gene as too lowly expressed if at least 10% of cells that do express the gene are below the threshold, i.e. `the 0.9 quantile over cells that express the gene < lower_threshold`   \n",
    "\n",
    "**How to construct the constraints**\n",
    "\n",
    "- First you need to find expression level thresholds from experimental experience. Ideally you have spatial measurements of some reference genes that are too highly (or too lowly) expressed (good candidates are abundant genes like MALAT1). Then you can estimate the thresholds based on the expression of those reference genes in a matched scRNA-seq data set. In our example we go with the tresholds 1 and 2 based on log-normalised data.    \n",
    "- With the thresholds we construct a smoothed penalty function which needs to be calibrated by testing it on example selections.     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set thresholds\n",
    "lower_th = 1.0\n",
    "upper_th = 2\n",
    "\n",
    "# Calculate quantiles\n",
    "sp.ut.get_expression_quantile(adata, q=0.99, normalise=False, log1p=False, zeros_to_nan=False)\n",
    "sp.ut.get_expression_quantile(adata, q=0.9, normalise=False, log1p=False, zeros_to_nan=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### DE ###\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 648x226.8 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 648x226.8 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 648x226.8 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnoAAADbCAYAAAD6dTxuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAA8K0lEQVR4nO3deZgU5bn38e/tsDogIOPCPuyIgmyyKEZQgsiiREwEjBETRRONJkdzYkzCMERN8JjzRhOjchRxG1xwAwRFIwgKIousIsgyAoIhIiAMMDDD/f7RNdgOs1TPVM3Ty/25rr6mu6q66tctfftUPVVPiapijDHGGGOSz0muAxhjjDHGmHBYQ88YY4wxJklZQ88YY4wxJklZQ88YY4wxJklZQ88YY4wxJklZQ88YY4wxJklZQ88YY4wxJklZQ89UiojkisgA1zmMMSaViEh/EZkrIvtEJLeE+Zne/IMi8qnV6dRlDT2TlETkTyKyWkQKRGR8CfNHi8jnIpInIq+JyKkOYhpjTKlEJK2M2XnAZOA3pcyfCnwMNAR+D0wTkdOCTWgSgTX0TEISkWrlLLIR+G/gjRLeezbwGHAtcAZwEPhn0BmNMYlJRFRE2kS9niIi93jP+4nIdhG5W0S+8no1rim27KMi8raI7BeR90SkRdT8Dt68r0VkvYj8qNh7HxGRWSKSB/QvLaOqfqSqzwCbS8jfDugGZKnqIVV9GVgNjKjcN2MSkTX0TCBEpKaI/E1EdniPv4lITW/eeyIywnt+gVdEh3ivLxGRFVHr+amIrBORPSLyVrECqSJyi4h8BnxWVh5VfUpVZwP7S5h9DTBDVeer6gHgj8CVIlK3kl+DMSY1nAlkAE2A64BJItI+av41wJ+8ZVYAzwGISDrwNpADnA6MBP4pIh2j3jsauBeoC7xfwXxnA5tVNbr+rfSmmxRjDT0TlN8DvYEuwLlAT+AP3rz3gH7e84uI7IF+L+r1ewAicgVwN3AlcBqwgEj3Q7ThQC+gIxV3NpGiB4CqbgKOAO0qsU5jTGr5o6rmq+p7RHoOfhQ17w1vRzKfSG3sIyLNgKFArqo+qaoFqvox8DLww6j3vq6qH6jqMVU9XMFsdYB9xabtI9J4NCnGGnomKNcAE1R1l6r+B8gm0jUKkYbcRd7z7wF/jnp9vKEH3Az8WVXXqWoBcB/QJfqonjf/a1U9VImsVgSNMZWxR1Xzol5/DjSOer2t6InXa/C1N78F0EtE9hY9iNTOM0t6byUcAE4pNu0USu7hMEnOGnomKI2JFLsi0YVvEdBORM4gcsTvaaCZiGQQOfI331uuBfBgVAH8GhAi3SNFrAgaY8J2EDg56vWZxeY38LphizQHdkS9blb0RETqAKd687cB76lq/ahHHVX9edR7NYD8a4FWxU5HOdebblKMNfRMUHYQaagVOV74VPUgsAy4HVijqkeAhcB/AZtU9SvvPduAm4oVwdqqujBqvUEVwXOLXohIK6AmsCGAdRtjEt8KYLSIpInIIL7tgYiWLSI1RORCIl2yL0XNGywifUWkBpFz9T5U1W3ATCI7vdeKSHXvcZ6InBVrQBE5SURqAdUjL6WWtz1UdYP3GbK86T8AOhPpJjYpxhp6JihTgT+IyGnekbpxwLNR898DbuXbbtp5xV4DPAr8zrsqFhGpJyLR56745hXQWkT+jVfzil3RUAXPAcNE5EJvr3wC8EqxE5eNManrdmAYsJdI1+prxeZ/CewhsjP7HHCzqn4aNT8HyCLSK9Ed+DGAV2MGErkIY4e3nolEdjRj9T3gEDCLyI71IWBO1PyRQA8v51+Aq7zTakyKEdUgDpCYVOUN1HkDkavD7ufbk4pfAv676GRiEbkUeBPop6rvicg5RC73H6mqL0St71oiw6K0IHLe3Nuq+lNvngJtVXWjj1xTiFwNF+16VZ3izR9NpPg1BN7x5n0d6+c3xqQWEekHPKuqTUuZPwXYrqp/KGm+MVXNGnrGGGOMT9bQM4mmvEFnjTEmbki2NCNyMc8ZRM7XnKRZ+mCxZQR4EBhM5KT6MZqly6s6qzGVJSJr+e65z0VuUtXnqjqPCVdY9c3O0TMJyTu/7kBJD9fZTKgKgDs0SzsSGbfxFsmW4mMqXga09R5jgUeqNqJJZqo6r7Sjed78MUEdzVPVs72rcos/rJGXnEKpb9bQMwlJVReUUgDruM5mwqNZurNo71WzdD+wju8OvwNwBfC0Zqlqln4I1JdsaVTFUY0xJiZh1Tdr6BljEpJkSybQFVhcbFYTvjve4nZOLJbGGBO3gqxvCX2O3kknnaS1a9d2HSMhFBYWkpaWVv6Cxjh28OBBBaLPOZmkqpOil5FsqUNkTLBfaZZ+U5X5qorVNxMUq//xwU9tg+DrW0I39GrXrk1eXl75Cxp27NhB48aNy1/QGMdE5JCq9ih1frZUJ1IEn9MsfaWERb4g6s4EQFNvWkKx+maCYvU/PpRX2yCc+mZdtyli0qQTdhqMSTjeFWdPAOs0S/+3lMWmAz+RbBHJlt7APs3SnVUWspJEZJiITCosLHQdxSQJq/+JIaz6ltBH9Ix/jRrZuegmKVwAXAuslmxZ4U27m8idAdAsfZTInQIGAxuJDD9wfdXHrDhVnQHMSE9Pv9F1FpMcrP4njFDqW0IPmJyenq7WteHPsmXL6N69u+sYxpRLRA6qanr5SyY3q28mKFb/44Or2mZdtylixowZriMYY4xxwOp/akvIrlsRGQYMq1mzIveBTk0DBw50HcEYY4wDVv9TW0Ie0VPVGao61i4X9ydn8Vaemr3IdQxjTEhyFm8lZ/FW1zFMnNqwYYPrCMahhGzomdh9tXO76wjGGGMcyM3NdR3BOGQNvRRx8fDRriMYY4xxYOzYsa4jGIesoZci3n0tx3UEY4wPNo6eCZqNo5faEvJiDFO+4ufrZDRq6ihJcgvrvKjRvZqHsl4T/2wcPRO0zMzMmN9jtS152BG9FNGoeUvXEUwVmTdvHj16RO6yk5ubS0ZGhuNExhiX2rVr5zpCYKy+xc4aeili9eIFriMYY4xxYM6cOa4jGIes6zZFdLtwgOsIJgTXXHMN69evJz8/nzZt2jB58mTXkYwxcWbYsGGuI1SI1bdg2BG9FLF53SobaysJPfjggyxdupTVq1dz9tlnM3HiRNeRjDFxZunSpa4jVIjVt2DYEb0UsferXa4jmBA8/fTTPPfccxw5coS8vDzatWvHoEGDXMcyxsSRnTt3uo5QIVbfgpGQR/Rs+IHY2Th6yWfBggU88sgjvPnmm6xevZp77rmHw4cPu45ljIkziTiOntW34CRkQ89ugVa60rpnbRy95LN3717q1atHw4YNyc/Pt/NXjDElSsRx9Ky+Bce6blPEmTa8Sihcjgk1aNAgnn32Wdq1a0dGRgbf+973+Oijj5zlMcbEp4oMr+J6vDurb8Gxhl6KaJBxhusIJmDVq1fnhRdeKHFe0cnXmZmZfPXVV1UZy1SSiAwDhtWsWdN1FJMkGjdu7DpCzKy+BSchu25N7NYt//D4c7v61pj4ZaemmKDNmzfPdQTjkDX0UsR5/S9zHcEYY4wDI0aMcB3BOGQNvRSxbvki1xGMMcY4YEf0Ups19FLEgX17XUcwxhjjwO7du11HMA5ZQy9F2Dh6xhiTmhJxHD0THGvopQgbR88YY1JTIo6jZ4Jjw6ukiGat27uOkJyWPhnOentcH856jTEpp1OnTrG/yWpb0rAjekmivCFTap2cXoVpTFXJzMykQ4cOnHvuubRp04YrrriChQsXAjBlyhTq169Ply5djj/uuuuuMtd355130rJlS0SENWvWfGfehg0b6NOnD+3ataNPnz589tlnoX0uY0xw6tSp4zpCzKy2Bccaeinis9XLXUcwIZk2bRorV65k48aNXHfddQwePJjFixcDMGDAAFasWHH88Ze//KXMdQ0fPpz58+fTokWLE+bdfPPN3HLLLWzYsIFbbrmFm266KZTPY4wJ1qJFiTnqgtW2YFhDL0X0GXi56wimClx55ZXcfPPNPPDAAxV6f9++fWnWrNkJ03ft2sXy5csZNWoUAKNGjWL58uX85z//qVReY0z4in63icxqW8XFVUNPRIaLyP+JyAsiMtB1nmTy8fv/ch3BVJFevXqxdu1aAN55553vdG88/vjjFVrntm3baNKkCUV3a0hLS6Nx48Zs27YtsNzGmHDMnDnTdYRAWG2rmNAvxhCRycBQYJeqnhM1fRDwIJAGPK6qf1HV14DXRKQB8AAwJ+x8qaLg6JETpkWf0+f6BtYmOKp6/PmAAQOYNm2awzTGxKaoLllNCk5+fr7rCIGw2lYxVXFEbwowKHqCiKQBDwOXAR2BUSLSMWqRP3jzTUDOH3iF6wimiixZsoRzzjmn/AVj0KxZM7744gsKCwsBKCwsZMeOHSV2hZgTWW+FcWn06OQYR9VqW8WE3tBT1fnA18Um9wQ2qupmVT0CPA9cIRETgdmqWuLVAyIyVkSWisjSgoKCcMMnkflv2J5PKnj99dd55JFHuOOOOwJd7+mnn06XLl2YOnUqAFOnTqVr166cdtppgW4nkYjIZBHZJSJrik0fJCLrRWSjiNwFoKqvqeqNwM3A1S7ymtQ1ZcoU1xEqzWpbxbkaR68JEN0Bvh3oBfwSGADUE5E2qvpo8Teq6iRgEkB6eroWn29K1rJDBcZRMuWLgzGhrrrqKmrWrEleXh4dO3Zk1qxZ9OrVi3Xr1h0/j6VIjx49yjyX5bbbbuOVV17hyy+/ZMCAATRs2PD4OTGPPvoo1113HRMmTKBBgwY8/fTTYX+0eDcF+Adw/IuI6q34PpG6tkREpqvqJ94i1lvhU0nDRVl3bsV079499jdZbUsaEt3nHdpGRDKBmUXn6InIVcAgVb3Be30t0EtVb41lvenp6ZqXlxd03IRU1hh6AMsXvEO3CweUOt8KqIkXInJQVUsd+FGyo877zdIT+nEkW/oBrwNbvEmvaJZOCCFqSbWtDzBeVS/1Xv/OW/Qv3uNtVX3Hz7pjqW/JdF5bWbUsGT6fCzNmzGDYsGGuY6Q8V7Wt3CN6ItIa2K6q+SLSD+gMPK2qe8t7bxm+AKI7wJt603wRkWHAsJo1a1YiQmrZ8unqMht6xiSQKRQ7klaCBZqlQ6smzndUuLcCIqemAGMBatSoEXLU+FLezqqpuGXLlllDLzFMIYTa5qfr9mWgh4i0IdJl+jqQAwyOZUPFLAHaikhLIg28kYDvs0VVdQYwIz09/cZKZEgp3xtylesIJk5MmDCBV1555YTpc+bM4fTTT3eQKDaapfMlWzJd54iFqj4EPORjOTs1xQRuzJgxriNUCattJfPT0DumqgUi8gPg76r6dxH52O8GRGQq0A/IEJHtQJaqPiEitwJvERleZbKqrq1AfuPTwjmvc8WYmHrGTZIaN24c48aNcx2jLNVEZGnU60leAygWfSRbVgI7gDs1q8rqS6V6K8B6LEzwcnJyuPvuu13HCJ3VtlI26mOlR0VkFHAdUHTst7rfRKpa4pDcqjoLmOV3PdGsEMauWvXU6gYyCa1AVXtU4v3LgRaapQckWwYDrwFtA0lWvkr1VoD1WJjg2f8r44aT2uZneJXrgT7Avaq6xStgz1QiaKWp6gxVHVs0krUpX9e+l7iOYEyV0Cz9RrP0gPd8FlBdsiUj6O14vRWLgPYisl1EfqaqBUBRb8U64EXrrShbzuKtdn5eyIYOdXG6qglaRWtbuQ09b1iA3xJpSaKqW1R1YiXzmiq2aM501xGMqRKSLWdKtoj3vCeROrc76O2o6ihVbaSq1VW1qao+4U2fpartVLW1qt4b63pFZJiITCoawNV8yxqFFVM0RpxJbBWtbX6uuh1G5HZkNYCWItIFmKCql1cqcSVY123s2nbq5jqCMYGQ7KjzfrMj5/3inU6iWfoocBXwc8mWAuAQMFKzqmAcqYBY160JWp8+fVxHMD6EVdv8nKM3nsidLOYBqOoKEWkV+0cIjhXC2B0+WPZ4XMk0DpcJx5QpU5g5cybTpk0jNzeXOXPmMHbs2CrPoVkln/cbNf8fRIYoMMYABw4ccB0hriV7bfNzjt5RVd1XbNqxWDdk3Nq2ab3rCCaJ5ObmMmlSrBeLGT+s67ZyrHv3RKtXr3YdIWEkY23zc0RvrYiMBtJEpC1wG7Aw3FgmaBcPT46bWseTX735K1Z8uSKUdXc5swt/G/S3cpcTEcaNG8frr7/OoUOHuO+++xgxYgQAixcv5q677uKbb74BImNMDRkyhNzcXHr06MFNN93ErFmzOHjwIE888QR9+/aloKCAIUOGsHv3bg4dOkTPnj157LHHThi895ZbbmHLli106dKFNm3acPXVVzNlyhTeeOMNAPLz88nMzGTx4sU0b25HiWNhPRYmaLEenbLally1zc8RvV8CZwP5wFTgG+BXIWYql+3xxu7d13JcRzAhSUtLY8WKFUyfPp2xY8eya9cu9u7dy80330xOTg7Lli1j5syZ3HTTTezduxeA3bt306dPHz7++GPGjRvHb3/72+PrysnJYenSpaxZs4bCwkImT558wjYffvhhOnbsyIoVK5g2bRo/+MEPWLNmDVu2RO7M8+KLL9K7d++EKYTGJLNEPUJltS0Y5R7RU9WDwO+9R1ywPd7Y1alX33WEpONnr7Qq/OxnPwOgffv2dOvWjQ8//JBq1aqxZcsWLrvssuPLiQgbN24kIyODOnXqHB9yoXfv3txxxx0AHDt2jAceeIDZs2dTWFjInj17OPnkk8vNUK1aNW666SYeffRRJk6cyMMPP8w999wTwqc1xsSqYcOGMS1vte1byVDb/Fx12w64E8iMXl5VLw4vlgnaWd3sqqtUoqp07tyZ+fPnnzAvNzf3OwOopqWlUVBQAERG0H///fdZsGABdevW5b777mPDhg2+tjl27Fi6du3K5Zdfzt69e7nkEhu7sSJsVIHy2cVjsenXr5/rCIGx2hY7P123LwEfA38AfhP1MAlkydzZriOYkDz55JMAfPbZZ3z88cf07t2b888/n88++4y5c+ceX27JkiVoOVfi7927l4yMDOrWrcu+ffvIySm5y/+UU05h377vXqOVkZHBgAEDGDlyJL/4xS+QyHBPJkY2ILx/RRde2MUXZXv55ZddR6gQq23B8NPQK1DVR1T1I1VdVvQIPVkZ7By92J3VrbfrCCYkBQUFdO3alaFDh/LYY49x+umn06BBA6ZPn052djbnnnsuZ511FuPHjy+3GP7kJz9h//79dOjQgWHDhnHhhReWuFznzp1p374955xzDlddddXx6TfccAN79uzhuuuuC/QzGmMqLlGP6FltC4aU9+WIyHhgF/AqkQsyAFDVr0NN5kN6errm5ZU9PlyqKG+P9oO3XuOCS4eXux7rCkksIsL+/fupU6eO6ygA3HPPPezcuZOHH364wusQkYOqmh5grIQUS31LlK7MknIGdTSuaJ2J8l1UpZycHEaPTqyRF6y2BcfP8CpFzdfo7loFnA6abGLz5dYtriOYJHf22WdTrVo13nrrLddRTJwLo6vVum9L5/dcNFOyRK9tfq66bVkVQUzF+C1uNo5ecirviHxVWrt2resIScEuxjBBc3GXh8qy2haccs/RE5GTReQPIjLJe91WRIaGH80EycbRMyYx2MUYJmiJOo6eCYafizGeBI4A53uvvwASaxAZQ/2M030tZ1ewGWNMcmnUqJHrCMYhP+fotVbVq0VkFEQGUBbH1xZb10bsWp3V2XUEY0yKsp1Ht3r06OE6gnHIzxG9IyJSm8gFGIhIa6KuvnXBujZit3zBO64jGGOMcWDGjBmuIxiH/BzRywLeBJqJyHPABcCYMEOZ4HXqVfKYQcaY+GI9FiZoAwcOdB3BOFTuET1VfRu4kkjjbirQQ1XnhRvLBG2nDa9iTEKwHotg2XnHNrxKqvNzr9tu3tOd3t/mIlIP+FxVC0JLZgL11c7triMYY4xxIDc313UE45Cfrtt/At2AVYAA5wBrgXoi8nNVnRNiPhMQG0fPGGNSUyKOo2eC4+dijB1AV1Xtoardga7AZuD7wP1hhjPBsXH0jDGppKwu21TrzrVx9FKbnyN67VT1+LDQqvqJiHRQ1c2uRlmxk5Vjl9GoaUzLl1UE7R6SxphEk0oNu+IyMzNdRzAO+WnorRWRR4DnvddXA5+ISE3gaGjJyqCqM4AZ6enpN7rYfjyItWg1am53sjPGmFTUrl071xGMQ366bscAG4FfeY/N3rSjQP9wYpmgrV68wHUEY4wPIjJMRCYVFha6jmKSxJw5dip9Kiv3iJ6qHgL+6j2KOxB4IhOKbhcOcB3BGONDovVYFPUulHRKRyp3l8aTYcOGuY5gHPJzRM8kgc3rVrmOYIwxxoGlS5e6jmAcsoZeitj71S7XEYwxJq6kytW3O3fuLH8hk7RKbeiJyDPe39urLo4Ji42jZ4wxqcnG0UttZR3R6y4ijYGfikgDETk1+lFVAU0wbBw9Y0yYio6OpcIRskRj4+iltrIuxngU+BfQClhG5K4YRdSbbhLEmQEOr1LWydfGGGPiiw2vktpKPaKnqg+p6lnAZFVtpaotox7WyEswDTLOcB3BGGOMA40bN3YdwThU7sUYqvpzETlXRG71Hp2rIpgJ1rrlH7qOYIwxxoF58+a5jmAcKnccPRG5DRgLvOJNek5EJqnq30NNVnYmuwVajM7rf5nrCMYEQrJlMjAU2KVZek4J8wV4EBgMHATGaJYur9qUxsSPESNGuI5gfAirtvkZXuUGoJeqjlPVcUBvwOlAnqo6Q1XHpqWluYyRUNYtX+Q6gjFBmQIMKmP+ZUBb7zEWeKQKMgXG7oxhgmZH9BLGFEKobX7udStAdMUp5LsXZpg4oqocKjzA3sO72JP/b/bm7+JQwQFWFL7L4U0HKTh2BEUD2db6g/UCWY9Jfjd2v5GmpzQNZF2apfMlWzLLWOQK4GnNUgU+lGypL9nSSLM0IQYTi/XOGPfMv4flW78CvvubFDmxTEsJpbuk5WJZdtX2fSdM25JfHxFh5ba9J6y1Mtsv+f0lvt3/+xG+PNbA1/ur6jstOXvFt//W7reovbw26dXTObn6yaTX8P5GvW5QqwG1q9cucbumaoRV2/w09J4EFovIq97r4cATPt5nQlZw7AhbvlnD5n0r2X7gM7448BlfHNjAgaN7T1y4Aaz6bH6g2391U6CrM0lsSLshsTT0qolI9FD+k1Q1lvEhmgDbol5v96YlREMvVn9+/88cPHoQiJ/f5EufuU4Qm+fWu04QMoE3ZrxR7mI102pyau1TaVC7AafWPjXyvFYDTjv5NM6scyZn1jmTRnUbRf7WaUT9WvVLbdSaEjmpbX7udfu/IjIP6OtNul5VP44hmAnI0cKjLNy2kDc3vsmrn7zL5n0rOXosH4CTq51C0zrt6HnGYM5Ib0GDmmdQv+YZNKh5OrWr1eXNZycz4vpfU/2kmoH9MG14FROSAlXt4TpEosi7O8/XkEeqJx7JL+3ovt9lVZWpH504bt7Ins0AeD5qXmnv9yu29/vM7037UY9m5a7X7/aD+E5LfH8ltv/AAw9w+69vJ+9oHgePHiTviPf3aB55R/LIO5rH3sN7+frQ1+w5tIevD0f+fr73c1YcXsGuvF0cLjh8wnprpNWgUZ1GNK/XnBb1W5BZLzPyt34mLeq1oHm95tSsZufSR3FS2/wc0UNVlwN2MrMD+/P38/r615m+fjpzNs1hX/4+qp1UjRZ1OzGg2Y9p16AHbep3o0HNM8pswLVudS410mpVYXJjnPkCiP4/d1NvWkqLpYvQz8k5RY3LaidVP2FejbQa3rwa/gM6NHPFXiB5d177dOpDs3rNyl+wFKrKN/nf8OWBL9l5YGfk7/7I3x0HdvD53s+Z//l8cr7J4Zge+857G9VpROtTW9O+YXvaNWxH+4btaZ/RnlYNWh3/d2J8q1Bt89XQM1UrvyCf2Rtnk7M6hxkbZnC44DBn1jmTEWeNYEi7IQxoNeB4YfKr1snp4YQ1Jv5MB26VbHke6AXsS5Tz84wJQ506dSr1fhGhXq161KtVj/YZ7Utd7mjhUb7Y/wW5e3P5fO/nfL7vc7bs3cKmrzcxY8MMduV9e8/1NEmjVYNWtGvYjg4ZHeh0eic6n9GZs047i1rV7KBEKSpU26yhF0c2fr2RScsm8eSKJ/nq4FecdvJp/KzrzxjdaTS9m/bmJIm+SHpvTOv+bPVyOve+KNC8xrgg2TIV6AdkSLZsB7KA6gCapY8Cs4gMP7CRyBAE17tJakx8WLRoEZdeemno26meVp3M+plk1s8scf7ew3tZ/9V6NuzewPrd6yOPr9bzzuZ3yC+MnIaUJmm0a9iOTmd0ovPpnSN/z+hMi3otkv58wLBqm5R1joSIpAHvqGr/yoQPS3p6uubl5bmOUSmqyuyNs/nbh3/j7c1vkyZpDO8wnBu63cCAVgOodlLJbfFY7ye54/NNNG7ROojIJ0jW7g7jhogcVNWUPwQdS31zcVvCsmpQUY5Eu+9tstay9evX07596UfiXCs4VsDGrzey+t+rWfXvVazeFfm7Ze+W48s0qNWAHo17HH+c1/g8mp7SNKEaf65qW5lH9FS1UESOiUg9VT3xGnpTYUcLj/LC2he4/4P7Wb1rNU1Pacqf+v+Jn3b9KY3rBn+7mo/f/1doDT1jjDHxa+bMmXHd0Kt2UjU6ZHSgQ0YHfnj2D49P35+/nzW71rDq36tYtnMZS3cs5X8W/g8FxwoAOD399EjDr1EPzmtyHuc1Po8z6tjtPovz03V7AFgtIm8Dx3cvVfW20FIlscMFh5m0bBJ/XfRXtu7bytmnnc1Tw59i1DmjqJ524knNQSk4eiS0dRtjkkPxI4MujhSa4OXn57uOUCF1a9alT7M+9GnW5/i0wwWHWfXvVSz5YglLdy5l6Y6lvLnxzeMXgbRu0JoLml/ABc0u4Pxm59PxtI7FTntKPX4aeq/w7e3PTAUdLTzKlBVTmDB/Atu/2U7f5n15ePDDDG47uEr+EZ4/8IrQt2GMMSb+jB492nWEwNSqVoueTXrSs0nP49PyjuTx8Zcf8+H2D/lg2wfM/mw2T698GoD6terTp2mf4w2/nk16kl4jtc4M8TOO3lMiUhtorqrJPqxk4I7pMV5Y8wLj5o1j49cb6d20N08Nf4qLW17sex1B7FXPf2MaI278dYXfb4wxkHjn3VVU8c+ZyEc1p0yZwvjx413HCE16jXT6Nu9L3+Z9uZM7UVU27dnEB1s/4INtH7Bw20Jmb5wNRLqJz2t8Hv0z+9O/ZX/Ob3Y+J1c/2fEnCFe5DT0RGQY8ANQAWopIF2CCql4eZBARaQX8HqinqlcFuW5X5n8+n9vfvJ0VX66g0+mdmD5yOkPbDXVy8mjLDp2qfJvGGGPc6969u+sIVUpEaHNqG9qc2obrulwHwJ5De1i0fRELPl/AvM/nMfGDidz3/n1UP6k6vZr2ijT8MvvTp1mfpBvexU/X7XigJzAPQFVXeI2yconIZGAosEtVz4maPgh4EEgDHlfVv6jqZuBnIjItpk8Qh7bu28pv3v4NL659kWanNOPZHzzLqE6jUv48AWNMYqjsUbtUOepnEkeD2g0Y3HYwg9sOBiIXery/9X3m5c5jbu5c7l1wL3+a/ydqptWkT7M+9GvRj++3/j49m/QsdfSLROEn/VFV3VfsKNSx0hYuZgrwD+DpognekC0PA98ncp+2JSIyXVU/8bnOuHXw6EHu/+B+Jn4wEYCsi7L47wv+Oy4OC2/5dDXdLhwQyrqji3rx7g07mduY5OyxMIlj2bJlDBs2zHWMuFK3Zl0ua3sZl7W9DIB9h/exYOsC5m6Zy9zcuWS/l83498ZzSs1TuKTlJQxsPZBLW19KywYtHSePnZ+G3loRGQ2kiUhb4DZgoZ+Vq+p8EcksNrknsNE7goeIPA9cASR0Q+/Vda9y+5u3s+2bbVx99tXc//37aV4vfho33xti/28xJkip2mORzJL1SOSYMWNcR4h79WrVY2i7oQxtNxSArw99zbtb3mXOpjm8tektXv30VQDanNqGga0GMrD1QPq37M8pNU9xGdsXP32JvwTOBvKBqcA3wK8qsc0mwLao19uBJiLSUEQeBbqKyO9Ke7OIjBWRpSKytKCgoBIxgrH9m+0Mf344V754JQ1qN+C9Me/x/FXPB9LIy1m8NbDCs3DO64Gsxxhz3BRgUPSEqB6Ly4COwCgR6Vj10Yz5Vk5OjusICefU2qdyVcermDRsErm35/LpLZ/y0KCH6JDRgadWPsXwF4bT8P6GXD410MsVQuHnqtuDwO9FZGLkpe4PI4iq7gZu9rHcJGASREaODyOLH4XHCvnnkn9y97t3U3iskIkDJvLr3r8OdSy8yqhW3W4ebUyQwuixEJGxwFiAGjXi5zebrEe6UkXNmjVdR0hoIkL7jPa0z2jPL3v9kiOFR1i0bRFzNs05Pn5fPPNz1e15wGSgrvd6H/BTVV1WwW1+ATSLet3Um5YwVn65krEzx/LRFx9xaetL+eeQf9Kqga/rU5zp2vcS1xGMSQUl9Vj0EpGGwL14PRaq+ueS3hwvO7ImuQwdOtR1hKRSI60GF2VexEWZiXH/eD9dt08Av1DVTFXNBG4BnqzENpcAbUWkpYjUAEYC02NZgYgME5FJhYWFlYgRuyOFRxg/bzw9/q8HuXtzybkyh9nXzI77Rh7AojkxfcXGmACp6m5VvVlVW5fWyDMmLFOnTnUdwTjk52KMQlVdUPRCVd8XEV8nx4nIVKAfkCEi24EsVX1CRG4F3iJysvJkVV0bS2hVnQHMSE9PvzGW91XGyi9XMub1Maz4cgU/7vxjHhz0IKfWPrWqNl9pbTt1cx3BmFRQ6R4Lb+zSYdbdFp/KGmUgXvXp06f8hUzSKrWhJyJFLYP3ROQxIhdiKHA13ph65VHVUaVMnwXMiimpI0cLjzLxg4lMeG8CDWo34NWrX2V4h+GuY8Xs8MG88hcyxlTW8R4LIg28kUBM959ysSNrktuBAwdcRzAOlXVE76/FXmdFPXd67khV7fGu2bWGMa+NYdnOZYw8ZyR/v+zvZJycEeo2y1KZE6K3bVpPz4sHB5imbGGfvJ2Ie9UmuYTVY2FM0FavXs2IESNcxzCOlNrQU9X+VRkkFmHv8R7TYzyw8AH+OPeP1KtZj2k/nMaIjon9I7l4ePLc1NqYeBBWj4V13ZriKrtjO3bs2CDjmART7sUYIlJfRG4Tkf8VkYeKHlURzoVt+7Yx4OkB/Pad3zKk7RDW/mJtwjfyAN59zcZRMiYRqOoMVR2blpbmOopJEpMmTXIdwTjk52KMWcCHwGr83/osVGHt8b609iXGzhzL0cKjPHH5E1zf5XqK3fotYdWpV79KthNLl63dHs0YY8LXsGFD1xGMQ34aerVU9b9CTxKDoLtu9+fv55ezf8lTK5+iZ5OePPuDZ2nbsG0Qq44bZ3Wzq66MSQTWdZs4iu+sxuvOa79+/VxHMA75GUfvGRG5UUQaicipRY/Qk1WRD7d/SJfHuvDMqmf4w4V/4P3r30+6Rh7AkrmzXUcwxvhgXbcmaC+//LLrCMYhP0f0jgD/A/yeb6+2VSD+RwkuQ8GxAu5bcB8T3ptA01Oa8t6Y9+jbvK/rWKE5q1tv1xGMMcY4YEf0Upufht4dQBtV/SrsMH5Vtmtj857NXPvqtSzctpBrOl3Dw4Mfpl6tesGGjDN7vvp3YOtqvfUlADY1/2Fg64xW0hVm8dolYowx8W7Hjh2uIxiH/DT0NgIHww4Si4qeo6eqPLPqGW6ddSsiwnNXPsfoTqkx7MiXW7e4jmCM8cHO0UtOldlZLdq5BqDXHTG/f8OGDTG/xyQPPw29PGCFiMwF8osmquptoaUKwZ5De/j5Gz/nhbUvcGHzC3nmB8/Qon4L17GqjI2jZ0xisDtjmKDZOHqpzc/FGK8B9wILgWVRj4QxL3ce5z56Li+ve5l7L76XudfNTalGHtg4esYYk6psHL3UVu4RPVV9qiqCxCLWro1/bf4XtarVYuFPF3Jek/PCDRen6mecfsK06O6AsM63q6zi4/KFfWs1Y4xJNo0aNXIdwThUbkNPRLZQwr1tVdXZVbexdm2Mu2gcv+37W+rUqBNysvjV6qzOriMYY3ywc/RMLPyc+9ejRw8AFr8UuYV9rx/Gfp6fSVx+um57AOd5jwuBh4BnwwwVtOpp1VO6kQewfME7riMYY3ywcfRM0GbMmOE6gnGo3Iaequ6Oenyhqn8DhoQfzQSpU68LXUcwxhjjwMCBA11HMA6V29ATkW5Rjx4icjP+rtY1cWSnDa9ijDEpyYZXSW1+Gmx/jXpeAOQCPwoljQnNVzu3u45gTCAkWwYBDwJpwOOapX8pNn8Mkbv5fOFN+odm6eNVGtKYOJKbm+s6gvEpjPrm56rb/hVKGyI7WTl2FR1Hr6wrc8u6Q0bxeWVdLRvLlbR+7spR2ROO7S4c8UuyJQ14GPg+sB1YItkyXbP0k2KLvqBZemuVBzQmDtk4eokhrPrm56rbmsAIIDN6eVWd4HcjQbMBRWP37ms5jLjx165jGFNZPYGNmqWbASRbngeuAIoXwoRlO7LxqaQd0uM7wt7dKope5xDZES1pZ7H4jmRJt3wsvmzrSmbeMHsy48ePr+BaTBUKpb75uer2dW9DBUTuklH0MAkko1FT1xGMCUITYFvU6+3etOJGSLaskmyZJtnSrGqiBcOuujVBy8zMdB3B+BNKffNzjl5TVR3kM6SJU42at3QdwRi/qonI0qjXk1Q1lqH9ZwBTNUvzJVtuAp4CLg40oTEJpF27dq4jmIjK1jaoQH3z09BbKCKdVHV1jGFMHFm9eAHtOvdwHcMYPwpUtbR/rF8A0XuwTfn2pGQANEt3R718HLg/2HjGJJY5c+Zw/vnnu45hyq5tEFJ989N12xdYJiLrRWSViKwWkVU+3mfiSLcLB7iOYEwQlgBtJVtaSrbUAEYC06MXkGyJvt/T5cC6KsxnTNwZNmyY6wjGn1Dqm58jepfFktLEp83rVtGyQyfXMYypFM3SAsmWW4G3iAw/MFmzdK1kywRgqWbpdOA2yZbLiZxX/DUwxllgY+LA0qVL6d69u+sYphxh1Tc/w6t8XqnkIUj2q9JiGW7Ej9ZbX+LdTXOBa8pcBsoetiR6qJUwlTWkS4lZvCveSrtCrWi4lej1lTV0SvEr6Ux80SydBcwqNm1c1PPfAb+r6lzGxKudO3e6jmB8CqO+JeQdLmx4ldgNH9Cbb1yHMMaUq6p2ZIPeofTDzw5lUNsoEsu2/OxklqS0ncyKfsfH1+dj57qkHdPi7+/nYBy9yo5naoLj5xw9kwRee+dD1xGMMT7Y8ComaJMmxXphp0km1tBLEc0bn+Y6gjHGGAdseJXUZg29FJHR4BTXEYwxxjjQuHFj1xGMQ9bQSxHL125yHcEYY4wD8+bNcx3BOGQNvRTRv3dn1xGMMcY4MGLECNcRjEPW0EsRdkTPGGNSkx3RS23W0EsR+/bnuY5gjDHGgd27d5e/kEla1tBLEcMH9HYdwRhjjANjHYyjZ+JHQg6YnOx3xgjDa+98yMVdbgCCv8NFWYOMFh8gNdZt+1m+tEFJo++IccL6it31InodRYOeFn9/WQN/Fs8QfeeNsuaVltkGGTXGBGXSpEmMHz/edQzjSEI29OzOGLFr3bxR+QsZY5yryI6snx2YImXtaAStKu6EURGx3PIx6OzF/1tF78zmUP4OcdHO4PGdZx/b7NSpcvc5L74DGssOqe28umddtyni5Fp29NOYRGB3xjBBq1OnjusIxiFr6KWI1RtyXUcwxhjjwKJFi1xHMA5ZQy9FDOzb1XUEY4wxDowaNcp1BOOQNfRSxPvLPnEdwRhjjAMzZ850HcE4ZA29FHH0aKHrCMYYYxzIz893HcE4ZA29FGFdt8YYk5pGjx7tOoJxyBp6KeKNeUtcRzDGGOPAlClTXEcwDllDL0V0aNXUdQRjjDEOdO/e3XUE45A19IwxxhhjkpQ19FLEp5u3u45gjDHGgWXLlrmOYByyhl6KGNLvPNcRjDHGODBmzBjXEYxD1tBLEXPe/9h1BGOMMQ7k5OS4jmAcquY6QBERSQf+CRwB5qnqc44jJZXq1e2+mca4YvXNuFSzpt3rPJWFekRPRCaLyC4RWVNs+iARWS8iG0XkLm/ylcA0Vb0RuDzMXKmob/eOriMYk1SsvplEMXToUNcRjENhd91OAQZFTxCRNOBh4DKgIzBKRDoCTYFt3mJ2G4eAWdetMYGbgtU3kwCmTp3qOoJxKNSGnqrOB74uNrknsFFVN6vqEeB54ApgO5FiGHquVNSpXabrCMYkFatvJlH06dPHdQTjkItz9Jrw7Z4tRApgL+Ah4B8iMgSYUdqbRWQsMBagRo0aIcYMR87iraGst/XWl77zelPzH37n9cHD+Scs42c9lc1RFYq2Wfwzl2XxS3/97jpifE/xbRX/3N/5z+wjV/E8JSn6t1O0rV4/vMP3ektatqx55S0T/e94dK/mMW87icV9fQu6BoVVM6J/Y8V/4yW9p7Tff1n1oSI10c96Stpm2PU3uoYUr2fL5+RQ/5s1JS5bXCy/Vz91q6z3FW2rpFoRVP3ws60glo1ncXMxhqrmAdf7WG4SMAkgPT1dw86VLDZt3cnFvTu7jmFMSrL6Zlyy+p/aXDT0vgCaRb1u6k3zTUSGAcPsSiL/hg/o7TqCManA6puJO1b/U5uLht4SoK2ItCRSAEcCo2NZgarOAGakp6ffGEK+pPTaOx9y448udR3DmEqTbBkEPAikAY9rlv6l2PyawNNAd2A3cLVmaW4VxbP6ZuKO1f/EEUZ9C3t4lanAIqC9iGwXkZ+pagFwK/AWsA54UVXXhpnDQL266a4jGFNpkl3CVa3ZUnzsoJ8BezRL2wD/D5gYSharbyZBWP1PDGHVt1CP6KnqqFKmzwJmVXS91rURu25n+7ncwJi4F7mqNUs3A0i2FF3V+knUMlcA473n04B/SLaIZmmg57xZfTOJwup/wgilviXkZf6qOkNVx6al2d0e/Jr74SrXEYwJQklXtTYpbRnN0gJgH9CwStIFwOqbCZrV/4QRSn0TDXYnt0qJyDHgkM/FqwEFIcZJlAwQHzksg2UoLUNtYHnUvEne1ahItlwFDNIsvcF7fS3QS7P01qKFJVvWeMts915v8pb5KvyPEZwEq2+utx8PGVxvPx4yuN5+PGQoa/ul1jYIr77FzfAqFaGqvo9IishSVe0RZp5EyBAvOSyDZahgBj9XtRYts12ypRpQj8hJywklkeqb6+3HQwbX24+HDK63Hw8ZKrn9UOpbQjf0jDEpJ3JVa3aZV7VOB64jcqHEVcC7QZ+fZ4wxIQilviXkOXrGmNTknZPy3atas3StZMsEyZbLvcWeABpKtmwE/gu4y01aY4zxL6z6lkpH9CaVv0jo4iEDxEcOyxBhGSJ8Z9CsE69q1SwdF/X8MOD/fnjJwfV/Q9fbB/cZXG8f3GdwvX1wn6FS2w+jviX0xRjGGGOMMaZ01nVrjDHGGJOkkq6hJyKDRGS9iGwUkRP6rkWkpoi84M1fLCKZDjKMEZH/iMgK73FDCBkmi8guEVlTynwRkYe8jKtEpJuDDP1EZF/U9zCupOUqmaGZiMwVkU9EZK2I3F7CMqF+Fz4zhPpdiEgtEflIRFZ6GbJLWCbU34bPDKH/NhKZ6/rmura5rmtW06yexbD9+Kllqpo0DyL3htsEtAJqACuBjsWW+QXwqPd8JPCCgwxjgH+E/F18D+gGrCll/mBgNiBAb2Cxgwz9gJkhfw+NgG7e87rAhhL+e4T6XfjMEOp34X22Ot7z6sBioHexZcL+bfjJEPpvI1EfrutbPNQ213XNaprVsxi2Hze1LNmO6EVuH6K6WVWPAEW3D4l2BfCU93wacImISBVnCJ2qzge+LmORK4CnNeJDoL6INKriDKFT1Z2qutx7vp/IlUzFRxoP9bvwmSFU3mc74L2s7j2Kn6Ab6m/DZwZTOtf1zXltc13XrKZZPYth+3Ej2Rp6sd0+REO5PZKfDAAjvEPq00SkWQnzw+Y3Z9j6eIe/Z4vI2WFuyDt035XI3le0KvsuysgAIX8XIpImIiuAXcDbqlrq9xDSb8NPBnD/24hXrutbItS2eKhrKVPTUrmeJVItS7aGXqKYAWSqamfgbb7d60g1y4EWqnou8HfgtbA2JCJ1gJeBX6nqN2FtpxIZQv8uVLVQVbsQGW29p4icE/Q2Ashgv43Elur//VKmpqV6PUukWpZsDb1Ybh+CSCi3Ryo3g6ruVtV87+XjQPcAt++Xn+8qVKr6TdHhb1WdBVQXkYygtyMi1YkUpOdU9ZUSFgn9uygvQ1V9F9769wJzgUHFZoX92yg3Q5z8NuKV6/qWCLXNaV1LlZpm9az87cfBb+G4ZGvoRW4fItJSRGoQOQFzerFlim4fAkW3D1ENsm+93AzFzpW4nMg5DlVtOvAT7+qs3sA+Vd1ZlQFE5MyicyZEpCeRf4+B/hC99T8BrFPV/y1lsVC/Cz8Zwv4uROQ0EanvPa8NfB/4tNhiof42/GSIk99GvHJd3xKhtjmta6lQ06yeJV4tS6o7Y6hqgYgU3T4kDZisqmtFZAKwVFWnE/kH+oyIbCRyUu1IBxluE5HLgQIvw5ggMwCIyFQiVz5liMh2IIvICaOo6qNERt4eDGwEDgLXO8hwFfBzESkADgEjA250A1wAXAus9s6nALgbaB6VI+zvwk+GsL+LRsBTIpJGpOi+qKozq/K34TND6L+NROW6vsVDbXNd16ym+d5+stezhKpldmcMY4wxxpgklWxdt8YYY4wxxmMNPWOMMcaYJGUNPWOMMcaYJGUNPWOMMcaYJGUNPWOMMcaYJGUNPWOMMcaYJGUNPVMhIvIrETk56vWsqAEkD5T6xrLXOUhE1ovIRhG5q5RlWojIvyRy/8B5ItK0Qh/AARH5nffZ1ovIpa7zGGPCF4+1UkQmisga73F1RTKYxGHj6JkKEZFcoIeqflXCvAOqWifG9aUBG4iMML6dyCj8o1T1k2LLvQTMVNWnRORi4HpVvbaCHyMmIpKmqoVRrxuo6h6f7+0ITAV6Ao2Bd4B20eszxiSfeKuVIjIE+BVwGVATmAdc4uoe4CZ8dkQvCYnI70Vkg4i8LyJTReROb/o8EenhPc/wChAikikiC0Rkufc435vez3vPNBH5VESek4jbiDRW5orIXG/ZXCnhXoYi8hsRWeLtVWaXEbsnsFFVN6vqEeB54IoSlusIvOs9n1vKMojIj0XkIxFZISKPiUiaiJzn5aglIukislZEzvE+53wRecPbS35URE7y1nNARP4qIiuBPsU28xtvGzeJyCllfDa8nM+rar6qbiEyYn3Pct5jjAlZSfUyyWtlR2C+qhaoah6wihPvE2uSiDX0koyIdCdyq5cuRG6Bc56Pt+0Cvq+q3YCrgYei5nUlsvfXEWgFXKCqDwE7gP6q2r+MLAOBtkQKUxegu4h8r5TFmwDbol5v96YVtxK40nv+A6CuiDQstt2zvM9xgap2AQqBa1R1CZH7H94D3A88q6prvLf1BH7pfc7WUdtIBxar6rmq+n70dlT1biK3AmoFLBeRJ0WkbyU/nzGmilSgXiZDrVwJDBKRk70GZ3+gWWnZTOKzhl7yuRB4VVUPeofii9/0vCTVgf8TkdXAS0QKVZGPVHW7qh4DVgCZMWQZ6D0+BpYDHYgUs8q4E7hIRD4GLgK+INKQi3YJ0B1YIpF7MV5CpPACTCDS5dGDSGOvyEfeHnIhkS7WogZbIfByaWFUdb2q/hZoD/wLeENEHipteWNMXIm1XiZ8rVTVOUTuhbuQSK1bxIk11CSRaq4DmCpVwLeN+1pR038N/Bs415t/OGpeftTzQmL7NyPAn1X1MR/LfsF39yqbetO+Q1V34O2likgdYISq7i1hu0+p6u9K2E5DoA6Rgl0LyCtadfFNeX8Pl3UenYgIkT3inxLZG38IeLyERX19PmNMXEjqWqmq9wL3evNyiJzzZ5KUHdFLPvOB4SJSW0TqAsOi5uUSOdIFcFXU9HrATm9P9Fogzcd29gN1y1nmLeCnXpFBRJqIyOmlLLsEaCsiLUWkBpHulBP2rr3zZYr+3f4OmFzCuv4FXFW0LRE5VURaePMeA/4IPAdMjHpPT2/bJxHpkvlON21JROQa4FPgFiAHOEtV/6iqn5ew+HRgpIjUFJGWRPbWPypvG8aYUJVWL3NJ0lrpna/c0HveGegMzPHxOUyCsiN6SUZVl4vIC0TOw9hFpCgUeQB4UUTGAm9ETf8n8LKI/AR4k2+PcpVlEvCmiOwo7dwTVZ3jnS+3KHLgiwPAj71cxZctEJFbiRS8NGCyqq4FEJEJwFJVnQ70A/4sIkqkSN9Swro+EZE/AHO8QncUuEVELgKOqmqORK5cWyiRq9GOed/TP4A2RE5cftXHd/A50FdV/1Pegqq6VkReBD4hcrTgFrvi1hi3yqiXyVwrqwMLvO18A/xYVQt8fA6ToGx4lSQnIuOBA6r6gOss8UpE+gF3qupQx1GMMQ5ZvTTJyLpujTHGGGOSlB3RM1XKOzfkXyXMukRVd1d1HmOMiUdWK01QrKFnjDHGGJOkrOvWGGOMMSZJWUPPGGOMMSZJWUPPGGOMMSZJWUPPGGOMMSZJWUPPGGOMMSZJ/X+bFvmckUacEAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 648x226.8 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### PCA ###\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 648x226.8 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 648x226.8 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnoAAADbCAYAAAD6dTxuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAABCLElEQVR4nO3dd5gUVfb/8fdhiA4IAgYQkCAoOUowEESRKAqoiCthhQHTrqvrz+w4xsXvqquiAquIqAQBQZKKIslVkCFJkjwEwWVFQMkMnN8fXYPNOKGmu2uqu+e8nqcfu7uqqz7dOsdbdW/dElXFGGOMMcbEn0J+BzDGGGOMMd6whp4xxhhjTJyyhp4xxhhjTJyyhp4xxhhjTJyyhp4xxhhjTJyyhp4xxhhjTJyyhp4xxhhjTJyyhp4JiYikicg1fucwxpiCREQeFJHVIvKbiGwVkQdzWb+9iPwgIodFZK6IXJRfWU10sIaeiRsicp6IjBORXSJyQET+IyItclhfRGSoiOx1HkNFRPIzszHGZCYiCTktBvoC5wAdgXtEpHc22ykPfAw8AZQFUoEJkU1rop019ExMEZHCOSwuCSwBmhIoau8BM0WkZDbrJwE3AA2BBkA3YHDEwhpjYoqIqIhcHPR6tIg86zxvKyI7ReRREfnZ6dW4LdO6w0XkC+ds2/zgs2cicqmz7BcRWS8iN2f67FsiMktEDgHtssuoqi+q6jJVTVfV9cAnwBXZrN4DWKOqE1X1KPAU0FBELg3tFzKxyBp6JiwiUkxE/uWcRdvlPC/mLJsvIj2d51c4RbSL87q9iKwI2s6fRWSdiOwTkc8zFUgVkbtFZCOwMbssqrpFVV9W1d2qelJVRwJFgUuy+Ug/4CVV3amqPwIvAf3D+kGMMfHsAqA8cCGB+jFSRILry23AM846K4APAUQkEfgCGAucB/QG3hSROkGf7QM8B5QCvnYTxumBuApYk80qdYGVGS9U9RCw2XnfFBDW0DPhegxoCTQicGasOfC4s2w+0NZ53gbYArQOej0fQES6A48SOPo8F1gIjMu0nxuAFkAdXBKRRgQaepuyWeWMIug8twJojMnJE6p6TFXnAzOBm4OWzVTVBap6jEBtbCUilYGuQJqqvuuciVsOTAZuCvrsJ6r6H1U95Zx9c+MpAv8ffzeb5SWBA5neO0CgMWkKCGvomXDdBjytqntU9X9ACnC7s2w+gQYdBBp4LwS9Pt3QA4YAL6jqOlVNB54HGmUaNPyCqv6iqkfchBKRs4H3gRRVzVzoMmQuggeAkjZOzxiTjX3OWbEM24CKQa93ZDxR1YPAL87yi4AWIrI/40Ggdl6Q1WfdEJF7CIzV6+I0LLNyEDg703tnA7/lZV8mtllDz4SrIoFilyG48H0L1BKR8wmc8RsDVHYGCDcHFjjrXQS8GlQAfyEw4PjCoO26LoIiUgKYDixS1RdyWDVzETwbOKiq6nZfxpi4chg4K+j1BZmWn+N0w2aoAuwKel0544kzNriss3wHMF9VywQ9SqrqnUGfdV13ROTPwMNAe1XdmcOqawj0tGR8LhGoQfZdvSYOWUPPhGsXgYZahtOFT1UPA0uBvwKrVfU48A1wP7BZVX92PrMDGJypCJZQ1W+CtuuqCDrjA6cCO8n9wooziqDz3AqgMQXXCqCPiCSISEd+74EIliIiRUXkKgJdshODlnUWkStFpCiBsXqLVHUHMIPAQe/tIlLEeVwmIrXzGtC5AOR54FpV3ZLL6lOAeiLSU0SKA08C36vqD3ndr4ld1tAz4RoHPC4i5zpn6p4EPghaPh+4h9+7aedleg0wHHhEROoCiEhpEQkeu+KKiBQBJgFHgH6qeiqXj4wB7heRC0WkIvAAMDqv+zXGxI2/Erj6fj+BrtWpmZb/BOwjcDD7ITAkU6NpLJBMoFeiKfAnAFX9DehA4CKMXc52hgLFQsj4LFAOWCIiB53H8IyFIrIm42pgZzhNTwIXeewjMM45y6lYTPzKaaoKY9x4lkCX5/fO64nOexnmA4/wezftfAIDgTNeo6pTnG6O8c64vAMErlALPlJ243ICR9hHgP1BQ+06qepC5wj8U1XNmG5lBFAdWOW8ftt5zxhTAKlqKrlckKWqzxFoOGXlZ1Udks3n1gNdslnWPw8Zq+WyvG6m118CNp1KASY2HMkYY4zJmYi0BT5Q1UrZLB8N7FTVx7Naboxf7IyeMSZmSIpUJtDlfj6BcZsjNVlfzbSOAK8CnQkMru+vybosv7MaEyoRWcOZY58zDFbVD/M7j8kfXtU3G6NnYoqIXBU0LuWMh9/ZTL5IBx7QZK1DYP7GuyVFMs+t2Amo6TySgLfyN6KJR6o6L7uzec7y/pE6m6eqdZ2rcjM/rJEX3zypb9bQMzFFVRdmUwCzu82ZiSOarLszjl41WX8D1nHmNDwA3YExmqyqyboIKCMpUiGfoxpjTJ54Vd+soWeMiUmSIlWBxsDiTIsu5Mx5F3fyx2JpjDFRK5L1LabH6BUqVEhLlCjhd4yYcvLkSRISEvyOYUy2Dh8+rEDwmJORzn2LT5MUKUngFlL3abL+mp/58ovVNxMqq/PRyU1tg8jXt5hu6JUoUYJDhw7lvqI5bdeuXVSsWDH3FY3xiYgcUdVm2S5PkSIEiuCHmqwfZ7HKjwTdoQCo5LwXU6y+mVBZnY9OudU28Ka+WddtATNy5B8OHoyJGc4VZ+8A6zRZX85mtWlAX0kRkRRpCRzQZN2dbyHDJCLdRGTkyZMn/Y5iYpTV+djkVX2L6TN6Ju8qVLAx6SamXQHcDqySFFnhvPcogVvvock6HJhFYOqBTQSmHxiQ/zFDp6rTgemJiYmD/M5iYpPV+ZjlSX2L6QmTExMT1bo28mbp0qU0bdrU7xjGZEtEDqtqYu5rxjerbyZUVuejk1+1zbpuC5jp06f7HcEYY4yHrM6bYDHZdSsi3YBuxYqFcj/ogq1Dhw5+RzDGGOMhq/MmWEye0VPV6aqaZJePuzN28XbGLt4OwIYNG3xOY4yJtOC/cWOszptgMdnQM6FLS0vzO4IxxhgPWZ03wayhV8AkJSX5HcEYY4yHrM6bYNbQK2BsfiVjopvNo2fCZXXeBIvJizFM7rIbr1O1atX8DRKHvBoL1adFFU+2a2KLzaNnwhVOnbf6Fn/sjF4BU6tWLb8jGA/MmzePZs0Cd9ZJS0ujfPnyPicyxvgl3uq81bfwWEOvgJk9e7bfEYwxxnjI6rwJZl23BUy3bt38jmDCdNttt7F+/XqOHTvGxRdfzKhRo/yOZIyJIrFc562+RZ6d0StAxi7ezpsT7Ugv1r366qukpqayatUq6taty9ChQ/2OZIyJIqmpqX5HCJnVt8izM3oFzP6f9/gdwYRpzJgxfPjhhxw/fpxDhw5Rq1YtOnbs6HcsY0yU2L17t98RQmb1LfJi8oyeTT8Quqtv6ON3BBOGhQsX8tZbb/HZZ5+xatUqnn32WY4ePep3LGNMFInVefSsvnkjJht6dgu07OV2K6Svpo7NxzQm0vbv30/p0qUpV64cx44ds/Erxpg/iNV59Ky+ecO6bguYC6pU8ztCzPNzPqiOHTvywQcfUKtWLcqXL0/r1q357rvvfMtjjIk+4UyvYvUt/lhDr4A5p/z5fkcwYShSpAgTJkzIclnGAOyqVavy888/52csE0Ei0g3oVqxYMb+jmBhVsWJFvyOExOqbN2Ky69aEbt2yRbl27xpj/GNDU0y45s2b53cEE0WsoVfAXNauk98RjDHGeKhnz55+RzBRxBp6Bcy6Zd/6HcEYY4yH7IyeCWYNvQLm4IH9fkcwxhjjob179/odwUQRa+gVMDaPnjHGxLdYnUfPeMMaegWMzaNnjDHxLVbn0TPesOlVCpjKNS7xO0LsS33Xm+02G+DNdo0xBUr9+vVD/7DVt7hjZ/TihNspU4qflZgPaYyXqlatyqWXXkrDhg2pV68e48ePB2DDhg3ceOONVK9enWbNmnHFFVcwderUMz7bokULGjVq5Go/d9999+n9XHHFFTneKP2ZZ56hRo0a1KhRg2eeeSbUr2aMiYCSJUv6HSEkVtu8YQ29AmbjqmV+RzARMGnSJFauXMn777/PgAED2LFjB61bt6ZHjx5s2bKF1NRUJk+ezK+//nr6M2vWrOGnn37i0KFDLF26NNd9dOrUiVWrVrFy5UoeeeQRbrnllizXW7BgARMnTmT16tWsXr2aiRMnsmDBgoh9V2NM3nz7bezOrmC1LfKsoVfAtOpwvd8RTAQ1btyYUqVKMWTIENq1a8ftt99+etkFF1xA3759T78eNWoUffv2pV+/fq7uIdm1a1eKFCkCQKtWrdi5cyenTp36w3oTJkygb9++lChRghIlStC3b99sZ7c3xnjv1ltv9TtC2Ky2RU5UNfRE5AYR+beITBCRDn7niUfLv57jdwQTQXPnzuXo0aOoKi1atMh2vRMnTvDhhx/Sv39/+vXrx4QJEzh69Kjr/QwbNowuXbpQqNAfS8b27du56KKLTr+uUqUKO3bsyNsXMcZEzIwZM/yOEDarbZHj+cUYIjIK6ArsUdV6Qe93BF4FEoC3VfUfqjoVmCoi5wD/BGZ7na+gST9x/PTz4DF9ft7I2uRdr169KF68OGeffTaTJ0/mlVdeyXH9GTNmUKtWLWrUqAEEjpanTJni6sh//PjxjB07Nia7LEzsyahLVpNCd+zYMb8jhMxqW+Tlx1W3o4FhwJiMN0QkAXgDuBbYCSwRkWmqutZZ5XFnuYmwyzt09zuCiYBJkyZRr97p4ybmz5/Pd999l+36o0aNYu3atVStWhWAQ4cOMWrUqFyL4ZQpU3jssceYM2cO559/fpbrVKlShW3btp1+vX37dipXrpyHbxP/ROQGoAtwNvCOqtpBrPFMnz6xO1+q1bbI87zrVlUXAL9kers5sElVt6jqcWA80F0ChgKfqmqWVw2ISJKIpIpIanp6urfh49CCmZP8jmA8cNdddzFnzhzGjv19nsQ9e/YwZswYfvrpJ+bPn8/WrVtJS0sjLS2NHTt2kJqayvbt2V+pPWPGDO6//34+//zz00U0KzfddBNjxozhyJEjHDlyhDFjxnDzzTdH8utFJREZJSJ7RGR1pvc7ish6EdkkIg8DqOpUVR0EDAGyHvltTISMHj3a7wgRY7UtfH7No3chENzRvRNoAdwLXAOUFpGLVXV45g+q6khgJEBiYqLmQ9a4Uu3SMOZXMgFROB9UxYoVmT9/Pg899BCPP/44iYmJlCxZkocffpj33nuPTp06UapUqdPrFy9enBtvvJF3332X5OTkLLc5YMAAihYtSq9evU6/N2fOHMqVK8fAgQO5/vrruf7662nbti09evSgbt26APTt25c2bdp4+4Wjw2ist8IzWU0XZd257jRt2jT0D0dZfbPaFj5R9b6tJCJVgRkZY/REpBfQUVUHOq9vB1qo6j152W5iYqIeOnQo0nFjkps59ACWLfySJldd84f3rYCaaCEih1U12wkfJSVo3G/y7+N+g5a3BT4BtjpvfazJ+rQHUbOqba2Ap1T1Ouf1I86q/3AeX6jql262nZf6Fk/j2nKqZfHw/fLD9OnT6datm98xTCZ+1bZcz+iJSA1gp6oeE5G2QANgjKruz+2zOfgRCO7oruS854qIdAO6FStWLIwIBdPWH1Zl2dAzJoaMJtOZtCws1GTtmj9xzhBybwUEhqYASQBFixb1OGp0cXuwanK3dOlSa+jFptF4UNvcjNGbDJwUkYsJdJlWBsK9YeoSoKaIVBORokBvYJrbD6vqdFVNSkhICDNGwdO6S6/cVzIFxqxZs2jUqNEfHrNmzfI7WrY0Octxv1FNVV9T1aaqOiS7Rp6z3khVbaaqzQoXtjtUmtD079/f7wi+s9r2OzeV5JSqpovIjcDrqvq6iCx3uwMRGQe0BcqLyE4gWVXfEZF7gM8JTK8ySlXXhJDf5NE3sz+he/889ZCbONa5c2c6d+7sd4zMCotI8D2JRjpjc/OilaTISmAX8HdNzrf6ElZvBViPhQnf2LFjefTRR/2O4SurbUE7dbHREyJyK9APyDgXXMRtIlXN8hpnVZ0FhNS0tkIYusJFClZ3kIlJ6araLIzPLwMu0mQ9KCnSGZgK1IxIstyd7q0g0MDrDeRprgtVnQ5MT0xMHORBPlMA2P8bo5Yvtc1N1+0AoBXwnKpudQrY+2EEDZt13Yau8ZXt/Y5gjKc0WX/VZD3oPJ8FFJEUKR/p/Ti9Fd8Cl4jIThG5Q1XTgYzeinXAR9ZbkbOxi7fb+LwI69rVj+Gpxmuh1rZcG3rOtAAPEWhJoqpbVXVomHmNT76d7XoopDExSVLkAkkRcZ43J1Dn9kZ6P6p6q6pWUNUiqlpJVd9x3p+lqrVUtYaqPpfX7YpINxEZefLkyUhHjnnWKHRn3LhxfkcwHgi1trm56rYbgduRFQWqiUgj4GlVvT6sxGGwrtvQ1azfxO8IxoRFUoLG/aYExv3iDCfRZB0O9ALulBRJB44AvTU5H+aRihDrujXhatWqld8RTAi8qm1uxug9ReBOFvMAVHWFiFTP+1eIHCuEoTt6OOt5ueJpHi4TOaNHj2bGjBlMmjSJtLQ0Zs+eTVJSkq+ZNDnrcb9By4cRmKLAmALp4MGDfkeIegWptrkZo3dCVQ9keu9UXndkosOOzev9jmBiVFpaGiNH5vUCMZNX1nUbHuvehVWrVvkdIabEe21zc0ZvjYj0ARJEpCbwF+Abb2MZr1x9Q+ze7Doa3PfZfaz4aYUn2250QSP+1fFfua4nIjz55JN88sknHDlyhOeff56ePXsCsHjxYh5++GF+/fVXAJ5++mm6dOlCWloazZo1Y/DgwcyaNYvDhw/zzjvvcOWVV5Kenk6XLl3Yu3cvR44coXnz5owYMeIPE/befffdbN26lUaNGnHxxRdzyy23MHr0aGbOnAnAsWPHqFq1KosXL6ZKFTszHCrrsTDhCvXMlN/1zWqbN9yc0bsXqAscA8YBvwL3eZgpV3bEG7qvpoY717WJBgkJCaxYsYJp06aRlJTEnj172L9/P0OGDGHs2LEsXbqUGTNmMHjwYPbv3w/A3r17adWqFcuXL+fJJ5/koYceOr2tsWPHkpqayurVqzl58iSjRo36wz7feOMN6tSpw4oVK5g0aRI33ngjq1evZuvWwN14PvroI1q2bBmThdCYeBLLZ6estkVermf0VPUw8JjziAp2xBu6kqXL+B0hprk545Yf7rjjDgAuueQSmjRpwqJFiyhcuDBbt26lU6dOp9cTETZt2kT58uUpWbLk6WkXWrZsyQMPPADAqVOn+Oc//8mnn37KyZMn2bdvH2eddVauGQoXLszgwYMZPnw4Q4cO5Y033uDZZ5/14NsaY/KiXLlyIX0uGuqb1bbIc3PVbS3g70DV4PVV9WrvYhmv1G5iV2PFK1WlQYMGLFiw4A/L0tLSzphENSEhgfT0dCAwi/7XX3/NwoULKVWqFM8//zwbNmxwtc+kpCQaN27M9ddfz/79+2nf3uZpDJfNKpA7u3gsZ23btvU7QkRZbQuPm67bicBy4HHgwaCHiUFL5n7qdwQTAe+++y4AGzduZPny5bRs2ZLLL7+cjRs3Mnfu3NPrLVmyBM3l6vv9+/dTvnx5SpUqxYEDBxg7Nuvu/bPPPpsDB868Lqt8+fJcc8019O7dm7vuugsJTPFkwmATwruXceFFQb/4IrPJkyf7HSFkVtsiz01DL11V31LV71R1acbD82Q5sDF6oavdpKXfEUwEpKen07hxY7p27cqIESM477zzOOecc5g2bRopKSk0bNiQ2rVr89RTT+VaDPv27ctvv/3GpZdeSrdu3bjqqquyXK9BgwZccskl1KtXj169ep1+f+DAgezbt49+/fpF9DsaY0ITy2f0rLZFnuT2Q4nIU8AeYAqBCzIAUNVfPE3mQmJioh46lPW8cAWN2yPa/3w+lSuuuyHb5dYVEv1EhN9++42SJUv6HQWAZ599lt27d/PGG29EZHsiclhVEyOysRiWl/oWK12ZWeWM1Nm4jG3Gym/hpbFjx9KnT+zNsGC1zRtuplfJaMoGd9cq4OukySY0P23f6ncEE0fq1q1L4cKF+fzzz/2OYmKIF12t1n37O7fj0Ez24qm2ubnqtlp+BDGhyWtxs3n0Yl9uZ+Hz05o1a/yOEHfsYgwTLr/v8BAqq23eyHWMnoicJSKPi8hI53VNEenqfTTjBZtHz5joZhdjmHDF8jx6JvLcXIzxLnAcuNx5/SMQuxPKFHBlyp+X43K7gs0YY2JbhQoV/I5gooibMXo1VPUWEbkVAhMoi8/XGVvXRuiq127gdwRjTAFjB4/5q1mzZn5HMFHEzRm94yJSgsAFGIhIDYKuvvWDdW2EbtnCL/2OYIwxxkPTp0/3O4KJIm7O6CUDnwGVReRD4Aqgv5ehjHfqt8h6HiFjTHSwHgsTrg4dOvgdwUSRXM/oqeoXQA8CjbtxQDNVnedtLOOV3Ta9ijFRzXosIqsgjju26VVMMDf3um3iPN3t/LOKiJQGtqlqumfJjCd+3r3T7wjGGGM8lJaW5ncEE0XcdN2+CTQBvgcEqAesAUqLyJ2qOtvDfCbCbB49Y4yJb7E6j57xhpuLMXYBjVW1mao2BRoDW4BrgRe9DGciz+bRM8YUBDl12cZ7d67No2eCuTmjV0tVT08RraprReRSVd3i1ywrNlg5dOUrVHK1Xk5FsCDfQ9IYE5viuWGXWdWqVf2OYKKIm4beGhF5CxjvvL4FWCsixYATniXLgapOB6YnJiYO8mP/0SDUolWhit3Rzhhj4lmtWrX8jmCiiJuu2/7AJuA+57HFee8E0M6bWMYrqxYv9DuCMSYHItJNREaePHnS7ygmRs2ebUPnze9yPaOnqkeAl5xHZgcjnsh4qslV1/gdwRiTg1jrscjoXchqSEdB6i6NJt26dfM7gokibs7omTiyZd33fkcwxhjjodTUVL8jmChiDb0CZv/Pe/yOYIwxUSFer77dvXt37iuZAiPbhp6IvO/886/5F8d4zebRM8aY+Gbz6JlgOZ3RayoiFYE/i8g5IlI2+JFfAU1k2Tx6xhgvZJwdi8czZLHG5tEzwXK6GGM4MAeoDiwlcFeMDOq8b2LMBRGYXiWnwdfGGGP8ZdOrmGDZntFT1ddUtTYwSlWrq2q1oIc18mLUOeXP9zuCMcYYD1WsWNHvCCaK5HoxhqreKSINReQe59EgP4IZb6xbtsjvCMYYYzw0b948vyOYKJLrPHoi8hcgCfjYeetDERmpqq97miznTHYLtBBd1q6T3xGMCYukyCigK7BHk7VeFssFeBXoDBwG+muyLsvflMb4p2fPnn5HMCHwqra5mV5lINBCVZ9U1SeBloCvE3mq6nRVTUpISPAzRkxat+xbvyMYE67RQMcclncCajqPJOCtfMgUMXZnDBMuO6MXs0bjQW1zc69bAYIrzknOvDDDRLl9R//Ltt/WsPvQFpadmsOhTb9ySsP/n8j6w6UjkM4UBIOaDqLS2ZUisi1N1gWSIlVzWKU7MEaTVYFFkiJlJEUqaLLGxORieb0zxrMLnmXZ9p+BrP8mRXIu15JLOc/t86t2HvjDe5uPlgHg+537IYz955Ytt/8Vuf3uO06UCevzoew/3N89p8/P3jubcqvKcV7ieZyXeB4VS1WkXIlyuW7T+Mur2uamofcusFhEpjivbwDecfE546PjJ48yb+d4vt41lc0Hlv++oAys3fRNRPYxZXNENmMKgC61uuSloVdYRIKn9h+pqnmZL+JCYEfQ653OezHR0MurF75+gcMnDgPR8zc5aZPfCfJm/Aa/E0SYwGcff3bGW+cUP4dLyl/CJeUuoeH5DWlVuRWNL2hMscI2BCof+VLb3Nzr9mURmQdc6bw1QFWX5/AR46NTepIvt3/A1M2vc+D4/7ioVB1urvn/uLRscyomXsxn771Dz4H3U0jCvymKTa9iPJKuqs38DhErDj16KOQpj1Q15+XkslyVcd/9cd68W5sHcoxdvC2M7Yefza1bLqsc8e3n9PlI/O45ee4fz3HHPXew59Ae9hzaw45fd7D+5/Ws37uezzd/znsr3wOgaEJRGl/QmPbV2tO5ZmdaVGpB4UJuzv+YEPlS21z9G1XVZYANZo5yuw5uZuTqB9m4fym1y7bk3ovfoHbZFmesc1GNOhFp5BkTxX4Egv/PXcl5z2QSTvdgRuOykPxxrHRCoQTnn7HRaJiy7H9A/By8tqjfInD2rvwlWS7f9dsuFu1cxKKdi/jPjv8w9D9Def7r5ylTvAzX1biOm+veTOeanSleuHg+Jze5CKm2xcZfocnVkv9+xvDv76dwoSIMqf8KV1a8McsiXvysRB/SGZOvpgH3SIqMB1oAB2JlfJ4xkVCyZMkcl1csVZEetXvQo3YPAPYf3c8Xm7/gs02fMWPjDCasmUCpoqW4sfaN/Kn+n2hfvb2dIIgOIdU2a+jFOFVl2pY3+Wjji9Qo3Yi/Nh5OueIVsl1/46plNGjZJh8TGhNZkiLjgLZAeUmRnUAyUARAk3U4MIvA9AObCExBMMCfpMb449tvv+W6665zvX6Z4mW4qe5N3FT3JtJPpTN361zGrR7Hx+s+ZszKMVQ/pzqDmw5mQKMBnJt4rofJCzavapvk1NcvIgnAl6raLpzwXklMTNRDhw75HcMXYxdvD4yPWf88M9NGcnmF7gyq9yJFE3I+1b5r22YqXlQjolnipbvDRAcROayqBf7Uc17qmx+3JczpnrYZOWLtvrfxUsvWr1/PJZdk3W2bF8fSjzHlhym8lfoWC7YtoEihIvSu15sHL3+Q+ufXj0DSgsWv2pbjuVhVPQmcEhGbRyPKqCof/PA0M9NGcm2VvtzZ4F+5NvIAln89Jx/SGWOM8cuMGTMisp1ihYvRu15v5vefz5q71jCk2RA+XvcxDYY3oPOHnZmXNi9PF70Yf7jpdD8IrBKRd0TktYyH18FMziZveoXPto2i40V/pl/tp12Pn0g/cdzjZMaYWDV28fYzzsJlfm1iw7FjxyK+zTrn1uG1Tq+x/W/beabdM6TuSqXde+1oM7oNC7ctjPj+TOS4aR18DDwBLACWBj2MT0YuHcmUza/S5sKbuO3SJ/I0CeblHbp7mMwYY4zf+vTp49m2y5Yoy+OtH2fbfdt4vdPrbPxlI61Ht+a6D65jyY9LPNuvCV2uDT1VfQ/4CFikqu9lPLyPZjIEH1XP3TqXu2fdTYPybbij7j/yfCXUgpmTvIhojCkgCspZvozvGYvfd/To0Z7vo0SREtzT/B42/2Uz/3ft/7F011Kav92cGyfcyMa9Gz3fv3Ev11aCiHQDVgCfOa8bici0SAcRkepO97C1RLKx6ZdN9JrYi5pla3Jvw2EhzVFV7VIbQGuMMfGsadOm+bavs4qcxd8v/ztb/7qVp9s+zZwtc6j7Zl0e+PwB9h/dn285TPbcnA56CmgO7AdQ1RVAdTcbF5FRIrJHRFZner+jiKwXkU0i8rCz3S2qekceshcoR9MPcf246wGYfut0zipyts+JjDHxKtyzWLF4FsyEp1SxUjzR5gk23LuBvg378sqiV6j5ek1GpI7g5Knw761uQuemoXdCVTPftfqUy+2PBjoGv+FM2fIG0AmoA9wqInVcbq9AUlVGrX2M9XvXM/GmidQoG/r0KFt/WBXBZAE5dW9YwTfGeixM/lq61L9h9BeUvIC3r3+b1KRUapevzZCZQ2j272Y2fs9Hbhp6a0SkD5AgIjVF5HXgGzcbV9UFwC+Z3m4ObHLO4B0HxgN2hUAOFvw4if/smkJym2SurnZ1WNtq3aVXhFIZU7BZj0X8idUxeZn179/f7wg0qdCE+f3nM6HXBP578L+0eLsFf/n0L/x67Fe/oxU4bhp69wJ1gWPAOOBX4L4w9nkhsCPo9U7gQhEpJyLDgcYi8kh2HxaRJBFJFZHU9PT0MGJEv7GLt/PinC95b90T1CnbiseueizsbX4z+5MIJDPGYD0WJkqNHTvW7whA4F7KN9e9mXV3r+Ouy+5i2HfDqP1GbT5e97HNv5ePch3Nr6qHgcdEZGjgpf7mRRBV3QsMcbHeSGAkBGaO9yJLtDhx6hjDVtxLsYSzuKvBq6dvFB6OwkWKRiCZMUZVF4hI1Uxvn+6xABCRjB6LtW62KSJJQBJA0aLR87ca62e4CppixYr5HeEMpYuXZljnYfRt2Jek6Un0/Kgn3Wp1480ub1Lp7Ep+x4t7bq66vUxEVgHfE5g4eaWIhHNJz49A5aDXlZz3TCYfb3qVHQd/YFC9/+Oc4udHZJuNr2wfke0YY7IUVo+Fqo5U1Waq2qxwYbsVuQlN165d/Y6QpeYXNic1KZV/XvtP5mydQ7036zFm5Rg7u+cxN1237wB3qWpVVa0K3A28G8Y+lwA1RaSaiBQFegN5mq5FRLqJyMiTJ+P3Sp5FOxcxfctbtLnwZpqcF7nG2bezIz4zjjEmF6q6V1WHqGoNVX3B7zwmvo0bN87vCNkqXKgwD1z+ACuHrKT++fXpN7Uf3cd356eDP/kdLW65aeidVNXT9zdR1a8BV4PjRGQc8C1wiYjsFJE7VDUduAf4HFgHfKSqa/ISWlWnq2pSQkL4XZnR6PCJw/Sb2o+yxSvwp9pPRnTbNes3iej2jDFnCLvHoiAcyMayWLhgo1WrVn5HyNXFZS9mXr95vNzhZb7Y8gV136zLuFXj7OyeB7Jt6IlIExFpAswXkREi0lZE2ojIm8A8NxtX1VtVtYKqFlHVSqr6jvP+LFWt5RzdPheRbxJHHpvzGBv2biCp/oucVbhURLd99PChiG7PGHOGsHss4v1A1njv4MGDfkdwJaFQAn9r9TeWD15OzbI16fNxH26aeBN7Du3xO1pcyWkQyEuZXicHPfe1ye3craNbtA04jYQF2xbw6uJXufuyu6lX7sozlkXiCHLH5vU0v7pzWNuosX0iAJur3JTtOl4f7QZvv0+LKp7uy5isOD0WbYHyIrITSFbVd0Qko8ciARiV1x4LY8K1atUqevbs6XcM1y4tfylf//lrXvrmJZ6c9yQLti3g3e7v0qVWF7+jxYVsG3qq2i4/g+SFqk4HpicmJg7yO0skHU0/ysBpA6l2TjWGXjOUT5bvjfg+rr7Bu5tdG1OQqOqt2bw/C5gV6nbj+UDWhCavB7ZJSUlexvFE4UKFeejKh+hSqwt9Jveh67iu3HPZPbx47YuUKFLC73gxzc1Vt2VE5C8i8rKIvJbxyI9wBc1zC55j4y8bGdF1BIlFEz3Zx1dTo2N+JWNM1qzr1oRr5MiRfkcIWb3z6vHdoO+4r8V9DFsyjMv+fRnf//d7v2PFNDfX788CFgGrcH/rM0/F4xHvmj1r+Md//sHtDW7nmurXeLafkqXLhPzZjC7bnOSlyzZjXet6NcaYyClXrpzfEcJSvHBxXun4CtddfB39p/an+b+bM/Saodzb4l4KiZtrSE0wNw294qp6v+dJ8iDeum5P6SmSZiRRulhpXuqQeWhkZNVuEv1XYxlTkMXjgWy8ynyw6tXB6xkH2S0eyHX9tm3bRnT/ful4cUdW3bmKP0/7M/d9fh+fbvqU0TeM5oKSF/gdLaa4aRq/LyKDRKSCiJTNeHierAAZuXQk3+z4hpc6vMS5ied6uq8lcz/1dPvGmPBY160J1+TJk/2OEDHnJp7LtN7TeLPzm8zfNp/6b9VnxoYZfseKKW4aeseB/yMwH95S55HqZaiCZNdvu3joy4e4utrV9G3Y1/P91W7S0vN9GGOM8U+8nNHLICLcedmdLE1ayoWlLqTbuG7cM+sejpw44ne0mOCm6/YB4GJV/dnrMG7FU9fGXz/7K8fSjzG8y3BExPP97fv5v57vA9xNwXK6OyJTV0RWV5jZeD5jjHFn165dfkfwRJ1z67B44GIemfMIryx6hfnb5jO+53jqnlfX72hRzc0ZvU3AYa+D5EW8dG3M2DCDSWsn8WSbJ6lZrma+7POn7VvzZT/GmNDYnTFiV43tE7O9aG3xxJdYPNHbMdgZNmzYkC/78UOxwsV4+bqX+fS2T9lzaA/N/t2M4anD7Y4aOXDT0DsErHDujmHTq0TIweMHuWvmXdQ9ty5/v/zv+bZfm0fPmOgWLweyxj+xOI9eXnW8uCPfD/meNhe14c6Zd9Ljox7sPRz5uWfjgZuG3lTgOeAbfh+jt9TDTAXCE189wc5fd/Lvbv+maELRfNuvzaNnjDHxLZbn0cuL80uez6zbZvFSh5eYuWEmDYc3ZF7aPL9jRZ1cx+ip6nv5ESQvYn2MXuquVF777jWGNBtCq8r5O91JmfLn5Wl9N3PneSnzvHzRfCNxY4yJBhUqVPA7Qr4pJIW4v9X9tK3allsn38rV713No1c9SnKbZIokFPE7XlRwc2eMrSKyJfMjP8JlJ5a7NtJPpZM0PYnzE8/nhfYv5Pv+q9dukO/7NMa4Z2P0TF5kNfavWbNmrteNF00qNGFp0lL6N+rPcwufo/Xo1mzdZ2PSwV3XbTPgMudxFfAa8IGXoeLZvxb9i+U/Lef1Tq9TunjpfN//soVf5vs+jTHuxfKBrIkO06dP9zuCL0oWLcmo7qMY33M8a/+3lkYjGjFu1Ti/Y/ku14aequ4Nevyoqv8CungfLf5s2beFJ+c+SfdLutOjdg9fMtRvcZUv+zXGGJM/OnTo4HcEX91S7xZWDllJ3XPr0ufjPgz4ZAAHjx/0O5Zv3HTdNgl6NBORIbibf88EUVXunHknCYUSGNZ5WL7MmZeV3Ta9ijHGxLV4nl7FraplqrJgwAKeaP0EY1aOocmIJizdVTCvI3XTYAvu0E8H0oCbPUkTxz5c9SGzN8/m9U6vU+nsSr7l+Hn3Tt/2bUwkSIp0BF4FEoC3NVn/kWl5fwJ38/nReWuYJuvb+RrSGB+lpaX5HSEqFC5UmKfbPc011a/hto9vo9U7rXi+/fPc3+p+CombkWv5z4v65uaq23YhpfVQrF11+/Phn/nb53+jZaWW3NnsTl+zeDGPXvCVudndCSOrq2Vr5LAst33ldMeNcO+iYXfhiF6SIgnAG8C1wE5giaTINE3WtZlWnaDJek++BzQmChSEefTyovVFrVk5ZCWDpg/iwS8e5IstX/DeDe9xQckL/I52Bq/qm5uu22Ii0kdEHhWRJzMeeUofYbE2WPmB2Q+w/+h+RnYdSUIhfzPbPHomxjUHNmmybtFkPQ6MB7r7nCmi7Krb6DR28XbXB6U5rZv5yteM11ldDRvqVbIZ8+jF81W2eVW2RFkm3TSJ4V2Gs3DbQhq81YBZG2f5HSszT+qbm3OXnzg7Sidwl4yMh3Hhyy1fMmblGB664iHqn1/f7ziUr+Bft7ExEXAhsCPo9U7nvcx6Sop8LykySVKkcv5Ei4xYO5A10adq1ap+R4hKIsLgZoNJTUrlgpIX0GVsF+777D6OpR/zO1oGT+qbm4ZeJVW9RVVfVNWXMh4uQxdoh08cZvCMwdQsW5PHWz/udxwAKlSp5ncEY3JTWERSgx557YeaDlTVZG0AfAFE3aTvxnipVq1afkeIanXOrcN3g77j3ub38uriV2nxdgvW/W9dfuw63NoGIdQ3Nw29b0TE/1NRMeipeU+xZd8WRnQdQfHCxf2OA8CqxQv9jmBMbtJVtVnQI/h+Tj8CwUewlfh9UDIAmqx7NVkzDtHfBpp6G9eY6DJ79my/I0S94oWL81qn15h+63R+/O1Hmo5syr+X/htV9XK3OdU28Ki+uWnoXQksFZH1IvK9iKwSke9dfK5AW7RzES99+xJ3NL6DdtWi53qWJldd43cEY8KxBKgpKVJNUqQo0BuYFryCpEjw/Z+uB/LlUN2YaNGtWze/I8SMrrW6snLISi6vfDlJM5Lo+VFP9hza41ccT+qbm4ZeJ6Am0AHoBnR1/mmyceTEEfpN7ceFpS7k5ete9jvOGbassza6iV2arOnAPcDnBArcR5qsayRFnpYUud5Z7S+SImskRVYCfwH6+5PWGH+kpqb6HSGmVCxVkdm3z+bFa15k5saZ1H2zLpPWTsr3HF7VNzfTq2wLPbY3on16lce/epwNezfwxe1fcHaxs/P8+bxMN5JX+3+O3JFK8LQqkZTVdC2Z93XG6xYPAL//bqeXOe8HX3WWsb2cpk7J/HkTXTRZZwGzMr33ZNDzR4BH8juXMdFi9+7dfkeIOYWkEA9e8SCda3am/yf9uWniTdxS9xbe6PwG5c4ql285vKhvMXmHC1WdDkxPTEwc5HeWzL7e/jWvLHqFO5vdyTXVo6+b1It59IwxkZNfB7JeHlDGs6wORDNPYZKxTqg/cbhTojSrcMr3aVUy9t/iptg6YK57Xl2+veNbhn49lJT5KcxLm8eIriPofmnszuIUnVNDx6hDxw/Rf2p/qpapyovXvuh3nCzZPHrGRDebXsWEa+qXi/yOENMKFyrMY60fIzUplQqlKnDDhBvoO6Uv+47s8ztaSKyhF0EPzH6Azfs28273dylZtKTfcbJ0gU2vYowxca1KxXP9jhAXGpzfgMUDF5PcJplxq8dR9826TFk3xe9YeWYNvQiZvHYyI5aO4P9d/v9oU7WN33GydU758/2OYIwxxkPlz8n72HCTtaIJRXmq7VMsHriY8xLPo8dHPegxoQe7ftvldzTXrKEXAdv2b2Pg9IFcVvEynrn6Gb/j5GjdMjulb4wx8WzZms1+R4g7TSo0YcmgJfyj/T/4dNOn1H6jNsNTh3NKT/kdLVfW0AtT+ql0bvv4Nk6eOsm4nuMomlDU70g5uqxdJ78jGGOM8VC7lg38jhCXiiQU4aErH2L1nau5rOJl3DnzTtqPac/JU9F9X2pr6IXp6flP858d/2F41+HUKFvD7zi5WrfsW78jGGOM8ZCd0fNWjbI1+OL2LxjdfTQdqncgoVB0XzgVk9OrRIsvNn/Bcwufo1/DfvSpHxvTlhw8sN/vCMYYYzx04LdDfkeIeyJCv0b9/I7hip3RC9HWfVvpPbk3dc6tw7DOw/yO45rNo2eMMfHthmta+h3BRJGYPKPn950xDp84TI+PenBKTzHllilRO5VKVr6aOpaeg/72h/cz33kiYyLQvMruDhY5bS+nO2y4uftGdhO/ZjVhaHZ3vQjeRkYHfObP5zTxZ+YMwXfeyGlZdpljbZJRY0z0mPrlIgbdfJ3fMUyUiMmGnp93xlBVBs8YzMqfVjKjzwwuLntxfkcIS+Ual/gdwRiTg0geyGZ1EJTTgYbJm0jfBtLN3SzcrFOjSoVc18mLzAegeTkgtYNX/1nXbR49s+AZPvj+A55p9wyda3b2O06eFT8r0e8Ixpgc2J0xTLjOKh6d94E3/rCGXh6MWTmG5HnJ9GvYj0evetTvOCHZuGqZ3xGMMcZ4aNWGNL8jmChiDT2X5m6dy8BpA7m62tWM7DYSEfE7Ukhadbje7wjGGGM81OHKxn5HMFHEGnoufPfjd3Qf352a5Woy+ebJUT8pck6Wfz3H7wjGGGM89PXStX5HMFHEGnq5WPnTSq774DrOTTyX2X+aTZniZfyOFJb0E8f9jmCMMcZDJ05E950aTP6yhl4O1v5vLde+fy0li5ZkTt85XHj2hX5HCtvlHbr7HcEYY4yHrOvWBLOGXjaW7lpKm9FtSCiUwFd9v6Jqmap+R4qIBTMn+R3BGGOMh2bOW+J3BBNFrKGXhXlp82j3XjtKFi3JwgELqVmupt+RIqbapfX9jmCMMcZDl1av5HcEE0WsoZfJxDUT6fhBRyqXrszXA76OuQmRjTHGGGMyWEPPcUpPkTw3mZsn3UzTik1Z0H9BXIzJy2zrD6v8jmCMMcZDP2zZ6XcEE0Vi8hZokXbg6AEGfDKAKT9MYUCjAbzV5S2KFY7PmcVbd+nldwRjjDEe6tL2Mr8jmChS4M/ofbPjGxqNaMS09dN4ucPLvHP9O3HbyAP4ZvYnfkcwxhjjodlfL/c7gokiUXNGT0QSgTeB48A8Vf3Qy/0dP3mc5xc+z7MLnqVK6SosHLCQVpVbebnLqFC4SOxO9mxMrMrv+mYKtiJF7D7J5neentETkVEiskdEVmd6v6OIrBeRTSLysPN2D2CSqg4CPL1P1/y0+TQa3oiU+SncWv9WVgxZUSAaeQCNr2zvdwRj4kK01jdjrmxax+8IJop43XU7GugY/IaIJABvAJ2AOsCtIlIHqATscFbzZFrvLfu28KeP/0Tb99pyNP0oM/vM5P0b3+fsYmd7sbuo9O3saX5HMCZejCaK6psxGazr1gTztOtWVReISNVMbzcHNqnqFgARGQ90B3YSKIYriHAD9KeDP/HsgmcZuXQkCYUSePTKR3ms9WOcVeSsSO4mJtSs38TvCMbEhWipb8ZkVr9WVb8jmCjixxi9C/n9yBYCBbAF8BowTES6ANOz+7CIJAFJAEWLuhtv9vhXjzN6xWgGNhnIE62f8HXalLGLt/u2b4Cjhw+5Wq/G9ome7N+L7WZsc3OVm1x/ZvHEl87chovP5PTvLvP3OmNVF7ky58lp/xn7anHTA663m9W6OS3LbZ3g36JPiyp53nccy/f6llf5UYNC+ZsM5TNeyvw3HS25shNcQw4fPZbtsqxeB8vL36ubupXT5zL2lVWtiFT9cLOvSKwbzaLmYgxVPQQMcLHeSGAkQGJiorrZ9lNtn+KhKx6KqztchGrH5vU0v7qz3zGMKVC8rG/GZLZ5+26ubtnA7xgmSvjR0PsRqBz0upLznmsi0g3oVqyYu2lQKp1tt4PJcPUNffyOYEw8y/f6ZkxmN1zT0u8IJor40dBbAtQUkWoECmBvIE+tD1WdDkxPTEwc5EG+uPbV1LH0HPQ3v2MYEzJJkY7Aq0AC8LYm6z8yLS8GjAGaAnuBWzRZ0/IpntU347upXy5i0M3X+R3DhMCL+ub19CrjgG+BS0Rkp4jcoarpwD3A58A64CNVXeNlDvO7kqXL+B3BmJBJShZXtaZI5rkk7gD2abJeDLwCDPUki9U3E6VKl0r0O4IJgVf1zeurbm/N5v1ZwKxQt2tdG6Gr3aRgzBdo4lbgqtZk56rWlNNXta4NWqc78JTzfBIwTFJENFkjOubN6puJVk3qurm8zEQhT+pbTF7mr6rTVTUpIcFm/86rJXM/9TuCMeHI6qrWzJfRn15HkzUdOACUy5d0EWD1zYRr7qLv/Y5gQuNJfRON7EFuvhKRU8ARl6sXBtI9jBMrGSA6clgGy5BdhhLAsqBlI52rUZEU6QV01GQd6Ly+HWihyXpPxsqSIquddXY6rzc76/zs/deInBirb37vPxoy+L3/aMjg9/6jIUNO+8+2toF39S1qplcJhaq6PiMpIqmq2szLPLGQIVpyWAbLEGIGN1e1ZqyzU1KkMFCawKDlmBJL9c3v/UdDBr/3Hw0Z/N5/NGQIc/+e1LeYbugZYwqcwFWtKTle1ToN6EfgQolewFeRHp9njDEe8KS+xeQYPWNMweSMSTnzqtZkXSMp8rSkyPXOau8A5SRFNgH3Aw/7k9YYY9zzqr4VpDN6I3NfxXPRkAGiI4dlCLAMAa4zaPIfr2rVZH0y6PlRILrvVxV5fv879Hv/4H8Gv/cP/mfwe//gf4aw9u9FfYvpizGMMcYYY0z2rOvWGGOMMSZOxV1DT0Q6ish6EdkkIn/ouxaRYiIywVm+WESq+pChv4j8T0RWOI+BHmQYJSJ7RGR1NstFRF5zMn4vIk18yNBWRA4E/Q5PZrVemBkqi8hcEVkrImtE5K9ZrOPpb+Eyg6e/hYgUF5HvRGSlkyEli3U8/dtwmcHzv41Y5nd987u2+V3XrKZZPcvD/qOnlqlq3DwI3BtuM1AdKAqsBOpkWucuYLjzvDcwwYcM/YFhHv8WrYEmwOpslncGPgUEaAks9iFDW2CGx79DBaCJ87wUsCGLfx+e/hYuM3j6WzjfraTzvAiwGGiZaR2v/zbcZPD8byNWH37Xt2iobX7XNatpVs/ysP+oqWXxdkYvcPsQ1S2qehzIuH1IsO7Ae87zSUB7EZF8zuA5VV0A/JLDKt2BMRqwCCgjIhXyOYPnVHW3qi5znv9G4EqmzDONe/pbuMzgKee7HXReFnEemQfoevq34TKDyZ7f9c332uZ3XbOaZvUsD/uPGvHW0Mvb7UPUk9sjuckA0NM5pT5JRCpnsdxrbnN6rZVz+vtTEanr5Y6cU/eNCRx9Bcu33yKHDODxbyEiCSKyAtgDfKGq2f4OHv1tuMkA/v9tRCu/61ss1LZoqGsFpqYV5HoWS7Us3hp6sWI6UFVVGwBf8PtRR0GzDLhIVRsCrwNTvdqRiJQEJgP3qeqvXu0njAye/xaqelJVGxGYbb25iNSL9D4ikMH+NmJbQf/3V2BqWkGvZ7FUy+KtoZeX24cg4sntkXLNoKp7VfWY8/JtoGkE9++Wm9/KU6r6a8bpb1WdBRQRkfKR3o+IFCFQkD5U1Y+zWMXz3yK3DPn1Wzjb3w/MBTpmWuT130auGaLkbyNa+V3fYqG2+VrXCkpNs3qW+/6j4G/htHhr6AVuHyJSTUSKEhiAOS3TOhm3D4GM24eoRrJvPdcMmcZKXE9gjEN+mwb0da7OagkcUNXd+RlARC7IGDMhIs0J/PcY0T9EZ/vvAOtU9eVsVvP0t3CTwevfQkTOFZEyzvMSwLXAD5lW8/Rvw02GKPnbiFZ+17dYqG2+1rWCUNOsnsVeLYurO2OoarqIZNw+JAEYpaprRORpIFVVpxH4D/R9EdlEYFBtbx8y/EVErgfSnQz9I5kBQETGEbjyqbyI7ASSCQwYRVWHE5h5uzOwCTgMDPAhQy/gThFJB44AvSPc6Aa4ArgdWOWMpwB4FKgSlMPr38JNBq9/iwrAeyKSQKDofqSqM/Lzb8NlBs//NmKV3/UtGmqb33XNaprr/cd7PYupWmZ3xjDGGGOMiVPx1nVrjDHGGGMc1tAzxhhjjIlT1tAzxhhjjIlT1tAzxhhjjIlT1tAzxhhjjIlT1tAzxhhjjIlT1tAzIRGR+0TkrKDXs4ImkDyY7Qdz3mZHEVkvIptE5OFs1rlIROZI4P6B80SkUkhfwAci8ojz3daLyHV+5zHGeC8aa6WIDBWR1c7jllAymNhh8+iZkIhIGtBMVX/OYtlBVS2Zx+0lABsIzDC+k8As/Leq6tpM600EZqjqeyJyNTBAVW8P8WvkiYgkqOrJoNfnqOo+l5+tA4wDmgMVgS+BWsHbM8bEn2irlSLSBbgP6AQUA+YB7f26B7jxnp3Ri0Mi8piIbBCRr0VknIj83Xl/nog0c56XdwoQIlJVRBaKyDLncbnzflvnM5NE5AcR+VAC/kKgsTJXROY666ZJFvcyFJEHRWSJc1SZkkPs5sAmVd2iqseB8UD3LNarA3zlPJ+bzTqIyJ9E5DsRWSEiI0QkQUQuc3IUF5FEEVkjIvWc77lARGY6R8nDRaSQs52DIvKSiKwEWmXazYPOPgaLyNk5fDecnONV9ZiqbiUwY33zXD5jjPFYVvUyzmtlHWCBqqar6iHge/54n1gTR6yhF2dEpCmBW700InALnMtcfGwPcK2qNgFuAV4LWtaYwNFfHaA6cIWqvgbsAtqparscsnQAahIoTI2ApiLSOpvVLwR2BL3e6byX2Uqgh/P8RqCUiJTLtN/azve4QlUbASeB21R1CYH7Hz4LvAh8oKqrnY81B+51vmeNoH0kAotVtaGqfh28H1V9lMCtgKoDy0TkXRG5MszvZ4zJJyHUy3iolSuBjiJyltPgbAdUzi6biX3W0Is/VwFTVPWwcyo+803Ps1IE+LeIrAImEihUGb5T1Z2qegpYAVTNQ5YOzmM5sAy4lEAxC8ffgTYishxoA/xIoCEXrD3QFFgigXsxtidQeAGeJtDl0YxAYy/Dd84R8kkCXawZDbaTwOTswqjqelV9CLgEmAPMFJHXslvfGBNV8lovY75WqupsAvfC/YZArfuWP9ZQE0cK+x3A5Kt0fm/cFw96/2/Af4GGzvKjQcuOBT0/Sd7+mxHgBVUd4WLdHznzqLKS894ZVHUXzlGqiJQEeqrq/iz2+56qPpLFfsoBJQkU7OLAoYxNZ96V88+jOY2jExEhcET8ZwJH468Bb2exqqvvZ4yJCnFdK1X1OeA5Z9lYAmP+TJyyM3rxZwFwg4iUEJFSQLegZWkEznQB9Ap6vzSw2zkSvR1IcLGf34BSuazzOfBnp8ggIheKyHnZrLsEqCki1USkKIHulD8cXTvjZTL+u30EGJXFtuYAvTL2JSJlReQiZ9kI4AngQ2Bo0GeaO/suRKBL5oxu2qyIyG3AD8DdwFigtqo+oarbslh9GtBbRIqJSDUCR+vf5bYPY4ynsquXacRprXTGK5dznjcAGgCzXXwPE6PsjF6cUdVlIjKBwDiMPQSKQoZ/Ah+JSBIwM+j9N4HJItIX+Izfz3LlZCTwmYjsym7siarOdsbLfRs48cVB4E9OrszrpovIPQQKXgIwSlXXAIjI00Cqqk4D2gIviIgSKNJ3Z7GttSLyODDbKXQngLtFpA1wQlXHSuDKtW8kcDXaKed3GgZcTGDg8hQXv8E24EpV/V9uK6rqGhH5CFhL4GzB3XbFrTH+yqFexnOtLAIsdPbzK/AnVU138T1MjLLpVeKciDwFHFTVf/qdJVqJSFvg76ra1ecoxhgfWb008ci6bo0xxhhj4pSd0TP5yhkbMieLRe1VdW9+5zHGmGhktdJEijX0jDHGGGPilHXdGmOMMcbEKWvoGWOMMcbEKWvoGWOMMcbEKWvoGWOMMcbEKWvoGWOMMcbEqf8PhmN7we3pnBoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 648x226.8 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 648x226.8 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calibrate constraints #\n",
    "\n",
    "# Try different factors (also note that we fixed different base variances 0.1 and 0.5 for lower and upper penalties)\n",
    "FACTORS = [0.1,1.0,2.0,10]\n",
    "\n",
    "penalty_fcts = {}\n",
    "test_selections = {}\n",
    "for f in FACTORS:\n",
    "    # Get penalty functions for given factor\n",
    "    penalty_fcts[f\"lower_{f}\"] = sp.ut.plateau_penalty_kernel(var=0.1 * f, x_min=lower_th, x_max=None)\n",
    "    penalty_fcts[f\"upper_{f}\"] = sp.ut.plateau_penalty_kernel(var=0.5*f, x_min=None, x_max=upper_th)\n",
    "    # Calculate each gene's penalty value\n",
    "    adata.var[f\"expr_penalty_lower_{f}\"] = penalty_fcts[f\"lower_{f}\"](adata.var['quantile_0.9 expr > 0'])\n",
    "    adata.var[f\"expr_penalty_upper_{f}\"] = penalty_fcts[f\"upper_{f}\"](adata.var['quantile_0.99'])\n",
    "    # PCA and DE selections with penalties\n",
    "    penalty_keys = [f\"expr_penalty_lower_{f}\",f\"expr_penalty_upper_{f}\"]\n",
    "    test_selections[f\"PCA_{f}\"] = sp.se.select_pca_genes(adata, n=100, penalty_keys=penalty_keys, inplace=False)\n",
    "    test_selections[f\"DE_{f}\"] = sp.se.select_DE_genes(adata, n=100, obs_key=\"celltype\", penalty_keys=penalty_keys, inplace=False)\n",
    "    \n",
    "# Plot selections for different factors\n",
    "for method in [\"DE\", \"PCA\"]:\n",
    "    print(f\"### {method} ###\")\n",
    "    for f in FACTORS:\n",
    "        used_penalties = {f\"lower_{f}\": penalty_fcts[f\"lower_{f}\"], f\"upper_{f}\": penalty_fcts[f\"upper_{f}\"]}\n",
    "        sp.pl.selection_histogram(\n",
    "            adata,\n",
    "            {f\"{method}_{f}\": test_selections[f\"{method}_{f}\"]},\n",
    "            x_axis_keys={f\"lower_{f}\": 'quantile_0.9 expr > 0', f\"upper_{f}\": 'quantile_0.99'},\n",
    "            background_key=\"all\",\n",
    "            penalty_kernels={f\"{method}_{f}\": used_penalties},\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this setting we choose the factor 0.1 to not select too many highly expressed genes. Lowly expressed genes are not selected anyway for the given selection thresholds. \n",
    "\n",
    "We apply the expression penalties on multiple selection steps (see below). The \"m_penalties<...>\" would not be necessary since we don't provide a marker list. For the case that a marker list is given the penalties should be applied as below: no lower penalty for `m_penalties_list_celltypes` since we don't expect cells from that cell type expressing those markers in the given data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c93d3f3a6fd34fb68e6924777de0c3a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: The following celltypes' test set sizes for forest training are below min_test_n (=20):\n",
      "\t Dendritic cells : 9\n",
      "\t Megakaryocytes  : 3\n",
      "The genes selected for those cell types potentially don't generalize well. Find the genes for each of those cell types in self.genes_of_primary_trees after running self.select_probeset().\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run selection with the calibrated expession constraints\n",
    "\n",
    "FACTOR = 0.1\n",
    "\n",
    "adata.var[\"expr_penalty_lower\"] = adata.var[f\"expr_penalty_lower_{FACTOR}\"]\n",
    "adata.var[\"expr_penalty_upper\"] = adata.var[f\"expr_penalty_upper_{FACTOR}\"]\n",
    "\n",
    "# create an instance of the ProbesetSelector class\n",
    "selector = sp.se.ProbesetSelector(\n",
    "    adata,\n",
    "    n=None,\n",
    "    celltype_key=\"celltype\",\n",
    "    verbosity=1,\n",
    "    save_dir=None,\n",
    "    pca_penalties=[\"expr_penalty_lower\", \"expr_penalty_upper\"],\n",
    "    DE_penalties=[\"expr_penalty_lower\", \"expr_penalty_upper\"],\n",
    "    m_penalties_adata_celltypes=[\"expr_penalty_lower\", \"expr_penalty_upper\"],\n",
    "    m_penalties_list_celltypes=[\"expr_penalty_upper\"],\n",
    ")\n",
    "\n",
    "\n",
    "# select probe set\n",
    "selector.select_probeset()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then investigate if the genes selected with spapros are within he expression constraints:\n",
    "(the `selector.plot_histogram` fct probably fails in spapros version 0.1.0, this will be fixed with the next release)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 648x453.6 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "selector.plot_histogram(\n",
    "    selections = [\"pca\", \"DE\"],\n",
    "    x_axis_keys={\"expr_penalty_lower\": 'quantile_0.9 expr > 0', \"expr_penalty_upper\": 'quantile_0.99'},\n",
    "    unapplied_penalty_keys={}\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### vi. Your use case is not captured?\n",
    "\n",
    "If you face a special use case and you're unsure if the described schemes would fit to this one, please raise an issue at [our github issue section](https://github.com/theislab/spapros/issues) and describe the use case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. What's next? \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- See our [basic evaluation tutorial](./spapros_tutorial_basic_evaluation.ipynb) and [advanced evaluation tutorial](./spapros_tutorial_advanced_evaluation.ipynb) for detailed instructions to evaluate the selected probesets.\n",
    "- Check out our [end to end selection tutorial](./spapros_tutorial_end_to_end_selection.ipynb) for combining gene selection and probe design."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Edit Metadata",
  "interpreter": {
   "hash": "b6b34c492024ffa29961e234fc8481d2351cf3e71b96e865103acdbed47bd476"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": [
     "%matplotlib inline\n"
    ]
   }
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "076c4a209671423c9a7ad85e2caa90b6": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_ad199d51c609412887e72aad6d496b55",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:02:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:24</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:15</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:32</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:36</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:36</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Marker selection..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:02:12\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:24\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:15\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:32\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:36\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:36\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mMarker selection..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "13da4765b2344cbdac1852144ab64ffc": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_ad217151c8d84fc6800a528d55f20ae8",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:52</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:17</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:03</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:31</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:35</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:35</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Marker selection..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:01:52\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:17\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:03\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:31\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:35\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:35\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mMarker selection..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "1519be3c0ea847cf9d4f51e790537a63": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "175d2a90f769422c82cfd9e59c4324b8": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "1824d67a9bd14fbc94c33138edc74ba8": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_af3624580bb6407c8ef39752632fe60c",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=1) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=2) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=3) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=1) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=2) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=3) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "18acd5f75b5842e7ae1eaa1bae486ee7": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "1a0ffece699c49b6aa7eec2e0b51872b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "1bc5d017b9434117a66cb806edc4a51d": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "1c540366e38d4142a234b69d65f3978c": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_afc4ad970f47492fa3476757a2f85214",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=1) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=2) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=3) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=1) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=2) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=3) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "1e7b101276834d53a29f7180406febb9": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_40f71902491c41a7b75dbcf1b9a410fd",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=1) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=2) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=3) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=1) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=2) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=3) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "2187bcec46b04612b3a30defff2e0c82": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_1a0ffece699c49b6aa7eec2e0b51872b",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=1) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=2) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=3) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=1) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=2) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=3) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "2c387553ef0543e890bc16f900b80361": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "31d2a39cdaeb470eb152274d27a698ee": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8ff0014719044fd384ff939d938e6914",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:02:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:18</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:10</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:42</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:43</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:43</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Marker selection..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:02:11\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:18\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:10\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:42\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:43\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:43\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mMarker selection..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "3bd9bbea482f4e6a9ec920c386b8fc62": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_44e866a47a4b41d8a77117811a1bd37b",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:02:52</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:36</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:40</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:34</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:36</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add genes from DE_baseline_forest...........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">12/12</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final trees on all celltypes......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:36</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:02:52\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:36\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:40\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:34\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:12\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:36\u001b[0m\n  \u001b[1;2;36mIteratively add genes from DE_baseline_forest...........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m12/12\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain final trees on all celltypes......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:36\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "40a7da23eb204f29bac0e4b5bed29caf": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_9c242708a40645f589c4db1972781dd1",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:57</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:20</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:02</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:34</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:36</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:36</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:01:57\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:20\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:02\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:34\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:36\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:36\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "40f71902491c41a7b75dbcf1b9a410fd": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "413f731c8bb444bcaf92365f2ed5ee06": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "44e866a47a4b41d8a77117811a1bd37b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "4a8abe1cab2f4b07ac5d6c6980757d01": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "4c5be865a81549d88ee79fe859cc079e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "547d44a17f7b47bebd7f306fc2fe875c": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_2c387553ef0543e890bc16f900b80361",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:52</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:17</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:03</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:31</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:36</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:36</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:01:52\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:17\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:03\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:31\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:36\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:36\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "558fb3ccfbb14a0189704c96d6b73b9f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "5e6d4ee81772499e9378ac9f234deddc": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "5f435903aa0a43958f422d07ddbbd30a": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_eea2b74602aa4396a00ed3f78e6967fc",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:35</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #f92672; text-decoration-color: #f92672\">━━━━━━╸</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 1/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:03:13</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:23</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:52</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:18</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:37</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:34</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add genes from DE_baseline_forest...........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">12/12</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:27</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final trees on all celltypes......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:35</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:01:35\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;249;38;114m━━━━━━\u001b[0m\u001b[38;2;249;38;114m╸\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━\u001b[0m  \u001b[35m 1/3\u001b[0m \u001b[33m0:03:13\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:23\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:52\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:18\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:37\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:34\u001b[0m\n  \u001b[1;2;36mIteratively add genes from DE_baseline_forest...........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m12/12\u001b[0m \u001b[33m0:00:27\u001b[0m\n  \u001b[1;2;36mTrain final trees on all celltypes......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:35\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "604ce7e51cfd4620aa81bccb1d2c861c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "625fe847f87d4326ab24e1daad78d872": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_916de62aa0af415ba70556f4b3c09390",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:03:50</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:52</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:02:20</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:38</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:40</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:40</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:03:50\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:52\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:02:20\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:38\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:40\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:40\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "6cf1b0302ceb4a6483139ee52899b008": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_18acd5f75b5842e7ae1eaa1bae486ee7",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:02:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:21</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:04</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:34</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:36</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:35</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:02:00\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:21\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:04\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:34\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:36\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:35\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "6ed0f1cfe60849f4aea24879eab63513": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_4c5be865a81549d88ee79fe859cc079e",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=1) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=2) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=3) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=1) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=2) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=3) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "70e14f7c6bed45118c80fdf11b11b5ca": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "7565149e6e0f4a7fa3f482e7d8f388d5": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_9e8ad9ee5e624f8c9e1a66ebba00a0cd",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:02:23</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:22</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:23</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:37</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:40</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:40</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Marker selection..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:02:23\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:22\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:23\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:37\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:40\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:40\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mMarker selection..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "79551f3612db4f7da0b7f07dc5bb82f5": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "7d448cb454054641a3e62dc8d091d9f2": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_1bc5d017b9434117a66cb806edc4a51d",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:53</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:20</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:02</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:31</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:35</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:35</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:01:53\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:20\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:02\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:31\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:35\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:35\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "822d68753feb4110893bb7bc9ed7f131": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_79551f3612db4f7da0b7f07dc5bb82f5",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:52</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:22</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:29</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:33</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:33</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Marker selection..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:01:52\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:22\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:00\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:29\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:33\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:33\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mMarker selection..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "851ff52a83d4478590cc360aa28902cb": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8b525e2e2444444d8d2dbf7e837a283e",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:02:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:24</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:02</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:32</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:02:00\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:24\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:02\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:32\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/3\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "8b525e2e2444444d8d2dbf7e837a283e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "8ff0014719044fd384ff939d938e6914": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "90a0fbed788b4321b2537a9aed56188a": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_d74f5d0c674d4393b10228153662f74d",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:57</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:21</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:03</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:32</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:23:21</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">  0%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:23:21</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Differentially expressed genes already selected...........................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Prior forest for DE_baseline forest already trained.......................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE genes already added....................................................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE_baseline forest already trained........................................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:19:45</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">  0%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:19:45</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Differentially expressed genes already selected...........................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Prior forest for DE_baseline forest already trained.......................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE genes already added....................................................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE_baseline forest already trained........................................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:18:50</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">  0%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:18:50</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Differentially expressed genes already selected...........................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Prior forest for DE_baseline forest already trained.......................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE genes already added....................................................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE_baseline forest already trained........................................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:14:25</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">  0%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:14:25</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Differentially expressed genes already selected...........................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Prior forest for DE_baseline forest already trained.......................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE genes already added....................................................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE_baseline forest already trained........................................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:08:30</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">  0%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:08:30</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Differentially expressed genes already selected...........................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Prior forest for DE_baseline forest already trained.......................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE genes already added....................................................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE_baseline forest already trained........................................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:07:17</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">  0%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:07:17</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Differentially expressed genes already selected...........................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Prior forest for DE_baseline forest already trained.......................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE genes already added....................................................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE_baseline forest already trained........................................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:06:05</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">  0%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:06:05</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Differentially expressed genes already selected...........................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Prior forest for DE_baseline forest already trained.......................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE genes already added....................................................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE_baseline forest already trained........................................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:05:41</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">  0%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:05:41</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Differentially expressed genes already selected...........................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Prior forest for DE_baseline forest already trained.......................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE genes already added....................................................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE_baseline forest already trained........................................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:04:13</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Differentially expressed genes already selected...........................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Prior forest for DE_baseline forest already trained.......................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE genes already added....................................................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE_baseline forest already trained........................................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:03</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Differentially expressed genes already selected...........................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Prior forest for DE_baseline forest already trained.......................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE genes already added....................................................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE_baseline forest already trained........................................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:01:57\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:21\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:03\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:32\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/3\u001b[0m \u001b[33m0:23:21\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m  0%\u001b[0m \u001b[33m0:23:21\u001b[0m\n\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mDifferentially expressed genes already selected...........................................\u001b[0m \n  \u001b[1;2;36mPrior forest for DE_baseline forest already trained.......................................\u001b[0m \n  \u001b[1;2;36mDE genes already added....................................................................\u001b[0m \n  \u001b[1;2;36mDE_baseline forest already trained........................................................\u001b[0m \n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/3\u001b[0m \u001b[33m0:19:45\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m  0%\u001b[0m \u001b[33m0:19:45\u001b[0m\n\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mDifferentially expressed genes already selected...........................................\u001b[0m \n  \u001b[1;2;36mPrior forest for DE_baseline forest already trained.......................................\u001b[0m \n  \u001b[1;2;36mDE genes already added....................................................................\u001b[0m \n  \u001b[1;2;36mDE_baseline forest already trained........................................................\u001b[0m \n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/3\u001b[0m \u001b[33m0:18:50\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m  0%\u001b[0m \u001b[33m0:18:50\u001b[0m\n\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mDifferentially expressed genes already selected...........................................\u001b[0m \n  \u001b[1;2;36mPrior forest for DE_baseline forest already trained.......................................\u001b[0m \n  \u001b[1;2;36mDE genes already added....................................................................\u001b[0m \n  \u001b[1;2;36mDE_baseline forest already trained........................................................\u001b[0m \n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/3\u001b[0m \u001b[33m0:14:25\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m  0%\u001b[0m \u001b[33m0:14:25\u001b[0m\n\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mDifferentially expressed genes already selected...........................................\u001b[0m \n  \u001b[1;2;36mPrior forest for DE_baseline forest already trained.......................................\u001b[0m \n  \u001b[1;2;36mDE genes already added....................................................................\u001b[0m \n  \u001b[1;2;36mDE_baseline forest already trained........................................................\u001b[0m \n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/3\u001b[0m \u001b[33m0:08:30\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m  0%\u001b[0m \u001b[33m0:08:30\u001b[0m\n\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mDifferentially expressed genes already selected...........................................\u001b[0m \n  \u001b[1;2;36mPrior forest for DE_baseline forest already trained.......................................\u001b[0m \n  \u001b[1;2;36mDE genes already added....................................................................\u001b[0m \n  \u001b[1;2;36mDE_baseline forest already trained........................................................\u001b[0m \n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/3\u001b[0m \u001b[33m0:07:17\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m  0%\u001b[0m \u001b[33m0:07:17\u001b[0m\n\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mDifferentially expressed genes already selected...........................................\u001b[0m \n  \u001b[1;2;36mPrior forest for DE_baseline forest already trained.......................................\u001b[0m \n  \u001b[1;2;36mDE genes already added....................................................................\u001b[0m \n  \u001b[1;2;36mDE_baseline forest already trained........................................................\u001b[0m \n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/3\u001b[0m \u001b[33m0:06:05\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m  0%\u001b[0m \u001b[33m0:06:05\u001b[0m\n\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mDifferentially expressed genes already selected...........................................\u001b[0m \n  \u001b[1;2;36mPrior forest for DE_baseline forest already trained.......................................\u001b[0m \n  \u001b[1;2;36mDE genes already added....................................................................\u001b[0m \n  \u001b[1;2;36mDE_baseline forest already trained........................................................\u001b[0m \n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/3\u001b[0m \u001b[33m0:05:41\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m  0%\u001b[0m \u001b[33m0:05:41\u001b[0m\n\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mDifferentially expressed genes already selected...........................................\u001b[0m \n  \u001b[1;2;36mPrior forest for DE_baseline forest already trained.......................................\u001b[0m \n  \u001b[1;2;36mDE genes already added....................................................................\u001b[0m \n  \u001b[1;2;36mDE_baseline forest already trained........................................................\u001b[0m \n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/3\u001b[0m \u001b[33m0:04:13\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mDifferentially expressed genes already selected...........................................\u001b[0m \n  \u001b[1;2;36mPrior forest for DE_baseline forest already trained.......................................\u001b[0m \n  \u001b[1;2;36mDE genes already added....................................................................\u001b[0m \n  \u001b[1;2;36mDE_baseline forest already trained........................................................\u001b[0m \n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/3\u001b[0m \u001b[33m0:00:03\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mDifferentially expressed genes already selected...........................................\u001b[0m \n  \u001b[1;2;36mPrior forest for DE_baseline forest already trained.......................................\u001b[0m \n  \u001b[1;2;36mDE genes already added....................................................................\u001b[0m \n  \u001b[1;2;36mDE_baseline forest already trained........................................................\u001b[0m \n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/3\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "916de62aa0af415ba70556f4b3c09390": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "9601bfa477c544a3aeb068ff334d1a73": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_fa3494eadb3e40d88012c156e3676da1",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:58</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:21</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:03</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:32</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:36</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:36</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Marker selection..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:01:58\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:21\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:03\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:32\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:36\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:36\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mMarker selection..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "9846f6ccb2694acd99b094fc739e6491": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_5e6d4ee81772499e9378ac9f234deddc",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:50</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:21</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:58</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:30</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:33</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:33</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:01:50\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:21\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:58\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:30\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:33\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:33\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "9c242708a40645f589c4db1972781dd1": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "9ced57c0557b4c329507cc4e13cf3aea": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_eb4e74ecccf44026af9084fe45c4013f",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=1) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=2) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=3) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=1) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=2) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=3) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "9db0e3aacf6a40a8b4685a5cc7fb98bf": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_1519be3c0ea847cf9d4f51e790537a63",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:02:47</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:36</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:37</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:32</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:09</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:35</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add genes from DE_baseline_forest...........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">12/12</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final trees on all celltypes......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:34</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:02:47\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:36\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:37\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:32\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:09\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:35\u001b[0m\n  \u001b[1;2;36mIteratively add genes from DE_baseline_forest...........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m12/12\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain final trees on all celltypes......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:34\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "9e8ad9ee5e624f8c9e1a66ebba00a0cd": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "a40995eb667e4f228ee59da2ff9d26fe": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "a800cb3213964b3599c789710f22e11f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_558fb3ccfbb14a0189704c96d6b73b9f",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:56</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:21</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:03</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:31</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:35</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:35</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:01:56\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:21\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:03\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:31\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:35\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:35\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "ad199d51c609412887e72aad6d496b55": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "ad217151c8d84fc6800a528d55f20ae8": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "af3624580bb6407c8ef39752632fe60c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "afc4ad970f47492fa3476757a2f85214": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b26b0df2d2c14661b2b8630295439a24": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b8f100c8d05445619e1a5c83d97df69e": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_f57f8418c9314d6a88151392146be67b",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:47</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:17</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:58</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:30</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:33</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:33</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Marker selection..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:01:47\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:17\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:58\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:30\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:33\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:33\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mMarker selection..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "bde27fc92728461b8528b365f00fba3a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "c16c7f249d734de09819efbfc0c40d6b": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_ccb53b6da498491a83891322538f806e",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n",
          "text/plain": ""
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "c63b0ddce4144a708ef8dad4e984f459": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_bde27fc92728461b8528b365f00fba3a",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=1) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=2) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=3) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=1) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=2) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=3) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "c7d67d7f70604e218f9ccd8d1af09f84": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_4a8abe1cab2f4b07ac5d6c6980757d01",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:54</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:21</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:30</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:34</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:34</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Marker selection..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:01:54\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:21\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:01\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:30\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:34\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:34\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mMarker selection..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "ccb53b6da498491a83891322538f806e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "d74f5d0c674d4393b10228153662f74d": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "dacd44ff42b94503aa837bf1564f0a18": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_a40995eb667e4f228ee59da2ff9d26fe",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:02:49</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:33</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:41</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:33</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:35</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add genes from DE_baseline_forest...........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">12/12</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final trees on all celltypes......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:36</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:02:49\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:33\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:41\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:33\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:11\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:35\u001b[0m\n  \u001b[1;2;36mIteratively add genes from DE_baseline_forest...........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m12/12\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain final trees on all celltypes......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:36\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "e25041f324a3490495e174ecc80ef548": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_175d2a90f769422c82cfd9e59c4324b8",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:58</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:21</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:03</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:33</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:35</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:35</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">PCA genes already selected..................................................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Differentially expressed genes already selected...........................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Prior forest for DE_baseline forest already trained.......................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE genes already added....................................................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">DE_baseline forest already trained........................................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Tree on pre/prior/pca selected genes already trained......................................</span> \n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:01:58\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:21\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:03\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:33\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:35\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:35\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mPCA genes already selected..................................................................\u001b[0m \n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mDifferentially expressed genes already selected...........................................\u001b[0m \n  \u001b[1;2;36mPrior forest for DE_baseline forest already trained.......................................\u001b[0m \n  \u001b[1;2;36mDE genes already added....................................................................\u001b[0m \n  \u001b[1;2;36mDE_baseline forest already trained........................................................\u001b[0m \n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTree on pre/prior/pca selected genes already trained......................................\u001b[0m \n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "e5f218f2df344f67bd1c1fb6e9c19c4f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_b26b0df2d2c14661b2b8630295439a24",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=1) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=2) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random (seed=3) genes.........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=1) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=2) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random (seed=3) genes.........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "e69cc7d0049e41328d5da1eb6b6cdcc3": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_f5464b6b65044ac1900ea37d08e8c13b",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "eb4e74ecccf44026af9084fe45c4013f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "ec89e34cd99a4d2493b9229ed59aeb67": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_413f731c8bb444bcaf92365f2ed5ee06",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:57</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:21</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:02</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:32</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">  0%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:01:57\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:21\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:02\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:32\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/3\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m  0%\u001b[0m \u001b[33m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "eea2b74602aa4396a00ed3f78e6967fc": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "f5464b6b65044ac1900ea37d08e8c13b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "f57f8418c9314d6a88151392146be67b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "f74b129ccbe8467c932162283dfc5e83": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_604ce7e51cfd4620aa81bccb1d2c861c",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:02:43</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:33</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:36</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:33</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:34</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:34</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:02:43\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:33\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:36\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:33\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:34\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:34\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "f787e6fda67647f7a13c7b66bd848f76": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_70e14f7c6bed45118c80fdf11b11b5ca",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET SELECTION:                                                                 </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Select pca genes..........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train baseline forest based on DE genes...................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:04:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Select DE genes.........................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 8/8</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train prior forest for DE_baseline forest...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:49</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Iteratively add DE genes to DE_baseline forest..........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:02:17</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train final baseline trees on all celltypes.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:05</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Train final forests.......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:17</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Train forest on pre/prior/pca selected genes............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 3/3</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:17</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Initial results are good enough. No genes are added.......................................</span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Compile probeset list.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET SELECTION:                                                                 \u001b[0m \n\u001b[1;34mSelect pca genes..........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mTrain baseline forest based on DE genes...................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:04:12\u001b[0m\n  \u001b[1;2;36mSelect DE genes.........................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 8/8\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mTrain prior forest for DE_baseline forest...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:00:49\u001b[0m\n  \u001b[1;2;36mIteratively add DE genes to DE_baseline forest..........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:02:17\u001b[0m\n  \u001b[1;2;36mTrain final baseline trees on all celltypes.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:05\u001b[0m\n\u001b[1;34mTrain final forests.......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:17\u001b[0m\n  \u001b[1;2;36mTrain forest on pre/prior/pca selected genes............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 3/3\u001b[0m \u001b[33m0:01:17\u001b[0m\n  \u001b[1;2;36mInitial results are good enough. No genes are added.......................................\u001b[0m \n\u001b[1;34mCompile probeset list.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "fa3494eadb3e40d88012c156e3676da1": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}