notebooks/01_0_data_preparation_morse.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download data from Morse et al\n",
    "\n",
    "https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE128033\n",
    "\n",
    "There's a single ~500MB file with all counts and barcodes for analyzed samples. This should be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "/home/icb/ignacio.ibarra/miniconda3/envs/covid_macrophages_integration/lib/python3.8/site-packages/tables/hdf5extension.cpython-38-x86_64-linux-gnu.so: undefined symbol: H5Pset_fapl_direct",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-415bfdece144>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mscanpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mos\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mlistdir\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/covid_macrophages_integration/lib/python3.8/site-packages/scanpy/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# the actual API\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_settings\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msettings\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVerbosity\u001b[0m  \u001b[0;31m# start with settings as several tools are using it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtools\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpreprocessing\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotting\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/covid_macrophages_integration/lib/python3.8/site-packages/scanpy/tools/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_leiden\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mleiden\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_louvain\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mlouvain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_sim\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_score_genes\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mscore_genes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscore_genes_cell_cycle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_dendrogram\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdendrogram\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/covid_macrophages_integration/lib/python3.8/site-packages/scanpy/tools/_sim.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0manndata\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAnnData\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadwrite\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogging\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mlogg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_settings\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msettings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compat\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLiteral\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/covid_macrophages_integration/lib/python3.8/site-packages/scanpy/readwrite.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mimread\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0manndata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m from anndata import (\n",
      "\u001b[0;32m~/miniconda3/envs/covid_macrophages_integration/lib/python3.8/site-packages/tables/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;31m# Import the user classes from the proper modules\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mfile\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopen_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy_file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     63\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mnode\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mNode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mgroup\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGroup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/covid_macrophages_integration/lib/python3.8/site-packages/tables/file.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mhdf5extension\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutilsextension\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: /home/icb/ignacio.ibarra/miniconda3/envs/covid_macrophages_integration/lib/python3.8/site-packages/tables/hdf5extension.cpython-38-x86_64-linux-gnu.so: undefined symbol: H5Pset_fapl_direct"
     ]
    }
   ],
   "source": [
    "import scanpy as sc\n",
    "from os.path import join, exists\n",
    "from os import listdir\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "d = '../data/morse'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data loaded successfully\n"
     ]
    }
   ],
   "source": [
    "print('data loaded successfully')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done...\n"
     ]
    }
   ],
   "source": [
    "print('done...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660641_SC14NOR_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660642_SC31NOR_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660643_SC31DNOR_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660644_SC45NOR_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660645_SC56NOR_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660646_SC59NOR_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660647_SC155NORLOW_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660648_SC156NORUP_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660649_SC228NORbal_frozen_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 265K Mar  8  2019 GSM3660650_SC249NORbal_fresh_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660651_SC87IPFLOW_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660652_SC88IPFUP_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660653_SC89IPFLOW_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660654_SC93IPFLOW_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660655_SC94IPFUP_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660656_SC95IPFLOW_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660657_SC153IPFLOW_genes.tsv.gz\n",
      "-rw-r--r--. 1 ignacio.ibarra OG-ICB-User 259K Mar  8  2019 GSM3660658_SC154IPFUP_genes.tsv.gz\n"
     ]
    }
   ],
   "source": [
    "!ls -ltrh data/morse | grep genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from os.path import join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = []\n",
    "for f in ['CCA_3nor3ipf_WorkspaceV4_newipfupdated_ClusterIdent.csv',\n",
    "          'CCA_NorControls_WorkspaceV2_activeIdent.csv']:\n",
    "    df2 = pd.read_csv(join('data', f), sep=',', index_col=0)\n",
    "    df2.columns = ['annotation']\n",
    "    df2['patient'] = df2.index.str.split('_').str[0]\n",
    "    df.append(df2)\n",
    "df = pd.concat(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7f170143cad0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(df.groupby(['patient', 'annotation']).size().reset_index().pivot('patient', 'annotation', 0).fillna(0).transpose(), cmap='Reds',\n",
    "              yticklabels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0 FABP4hi Macrophage': 1801, '0-FABP4hi Macrophage': 1599, '4 Endothelial': 765, '3-Endothelial': 669, '1-SPP1hi Macrophage': 590, '2 T-Cell': 560, '1 SPP1hi Macrophage': 434, '7 Fibroblast/SMC/pericyte': 397, '2-T Helper/T Reg': 394, '3 FCN1hi monocyte': 351, '6-FCN1hi Monocyte': 340, '8 AT1': 310, '5-Fibroblast': 273, '4-Cytotoxic T Cell': 235, '9 Ciliated Cell': 234, '7-AT1/Club': 201, '6 AT2': 187, '12-AT2': 164, '8-Ciliated': 156, '5 T-cell/NK cell': 151, '17-Lymphatic Endothelial': 147, '11 Lymphatic endothelial': 147, '12 Club': 141, '10-Smooth Muscle/Pericyte': 140, '9-Goblet': 137, '13 Mast cell': 135, '13-Mast Cell': 134, '10 Dendritic cell': 132, '16-Dendritic Cell': 103, '15 B-cell/plasma cell': 99, '19-B Cell': 89, '14 Proliferating cell': 88, '15-Proliferating Cell': 88, '11-NK Cell': 80, '20-Low Quality Ciliated': 33, '22-Plasma Cell': 30, '18-Mast Cell': 13, '14-Basal': 5}\n",
      "{'1-SPP1hi Macrophage': 1091, '1 SPP1hi Macrophage': 1057, '0 FABP4hi Macrophage': 751, '0-FABP4hi Macrophage': 718, '6-FCN1hi Monocyte': 665, '3 FCN1hi monocyte': 662, '2 T-Cell': 379, '2-T Helper/T Reg': 266, '4 Endothelial': 245, '3-Endothelial': 220, '4-Cytotoxic T Cell': 212, '5 T-cell/NK cell': 208, '8 AT1': 132, '7 Fibroblast/SMC/pericyte': 127, '6 AT2': 126, '9 Ciliated Cell': 125, '10 Dendritic cell': 119, '11-NK Cell': 107, '12-AT2': 104, '17-Lymphatic Endothelial': 99, '7-AT1/Club': 98, '11 Lymphatic endothelial': 97, '16-Dendritic Cell': 86, '14 Proliferating cell': 83, '15-Proliferating Cell': 80, '5-Fibroblast': 76, '8-Ciliated': 72, '12 Club': 59, '13 Mast cell': 58, '10-Smooth Muscle/Pericyte': 57, '9-Goblet': 49, '19-B Cell': 44, '13-Mast Cell': 36, '15 B-cell/plasma cell': 27, '20-Low Quality Ciliated': 19, '18-Mast Cell': 16, '14-Basal': 4, '22-Plasma Cell': 2, '21-Low Quality Basal': 1}\n",
      "{'1-SPP1hi Macrophage': 1218, '2-T Helper/T Reg': 855, '5-Fibroblast': 691, '3-Endothelial': 463, '4-Cytotoxic T Cell': 367, '18-Mast Cell': 331, '0-FABP4hi Macrophage': 222, '6-FCN1hi Monocyte': 209, '10-Smooth Muscle/Pericyte': 155, '11-NK Cell': 150, '7-AT1/Club': 136, '9-Goblet': 123, '15-Proliferating Cell': 102, '16-Dendritic Cell': 100, '19-B Cell': 93, '13-Mast Cell': 85, '17-Lymphatic Endothelial': 82, '8-Ciliated': 76, '14-Basal': 52, '20-Low Quality Ciliated': 47, '22-Plasma Cell': 44, '12-AT2': 32, '21-Low Quality Basal': 18}\n",
      "{'1 SPP1hi Macrophage': 360, '5 T-cell/NK cell': 156, '0 FABP4hi Macrophage': 155, '2 T-Cell': 139, '3 FCN1hi monocyte': 110, '6 AT2': 101, '4 Endothelial': 34, '12 Club': 22, '10 Dendritic cell': 20, '13 Mast cell': 19, '9 Ciliated Cell': 17, '15 B-cell/plasma cell': 15, '14 Proliferating cell': 12, '8 AT1': 11, '7 Fibroblast/SMC/pericyte': 8, '11 Lymphatic endothelial': 6}\n",
      "{'1 SPP1hi Macrophage': 1810, '2 T-Cell': 364, '0 FABP4hi Macrophage': 264, '5 T-cell/NK cell': 238, '6 AT2': 186, '3 FCN1hi monocyte': 127, '9 Ciliated Cell': 84, '10 Dendritic cell': 52, '4 Endothelial': 50, '7 Fibroblast/SMC/pericyte': 49, '8 AT1': 39, '13 Mast cell': 39, '14 Proliferating cell': 32, '12 Club': 27, '11 Lymphatic endothelial': 16, '15 B-cell/plasma cell': 12}\n",
      "{'1 SPP1hi Macrophage': 337, '0 FABP4hi Macrophage': 121, '2 T-Cell': 110, '3 FCN1hi monocyte': 107, '5 T-cell/NK cell': 101, '6 AT2': 89, '4 Endothelial': 42, '14 Proliferating cell': 28, '9 Ciliated Cell': 24, '13 Mast cell': 18, '10 Dendritic cell': 15, '15 B-cell/plasma cell': 15, '8 AT1': 12, '12 Club': 11, '7 Fibroblast/SMC/pericyte': 10, '11 Lymphatic endothelial': 3}\n",
      "{'0 FABP4hi Macrophage': 1278, '0-FABP4hi Macrophage': 1251, '1-SPP1hi Macrophage': 586, '1 SPP1hi Macrophage': 561, '5 T-cell/NK cell': 282, '4-Cytotoxic T Cell': 256, '4 Endothelial': 212, '3 FCN1hi monocyte': 191, '2 T-Cell': 177, '6-FCN1hi Monocyte': 174, '3-Endothelial': 172, '2-T Helper/T Reg': 145, '11 Lymphatic endothelial': 89, '6 AT2': 88, '10 Dendritic cell': 88, '17-Lymphatic Endothelial': 87, '7 Fibroblast/SMC/pericyte': 78, '12-AT2': 76, '11-NK Cell': 74, '9 Ciliated Cell': 61, '16-Dendritic Cell': 61, '15 B-cell/plasma cell': 47, '8 AT1': 47, '8-Ciliated': 45, '19-B Cell': 44, '5-Fibroblast': 41, '7-AT1/Club': 34, '12 Club': 30, '15-Proliferating Cell': 29, '14 Proliferating cell': 28, '9-Goblet': 26, '13-Mast Cell': 25, '13 Mast cell': 17, '10-Smooth Muscle/Pericyte': 14, '20-Low Quality Ciliated': 6, '22-Plasma Cell': 6, '21-Low Quality Basal': 2, '18-Mast Cell': 1}\n",
      "{'3-Endothelial': 575, '1-SPP1hi Macrophage': 501, '5-Fibroblast': 318, '7-AT1/Club': 299, '14-Basal': 237, '8-Ciliated': 233, '9-Goblet': 171, '21-Low Quality Basal': 166, '10-Smooth Muscle/Pericyte': 118, '2-T Helper/T Reg': 103, '20-Low Quality Ciliated': 97, '13-Mast Cell': 80, '4-Cytotoxic T Cell': 74, '6-FCN1hi Monocyte': 50, '16-Dendritic Cell': 47, '15-Proliferating Cell': 45, '17-Lymphatic Endothelial': 25, '12-AT2': 22, '0-FABP4hi Macrophage': 20, '19-B Cell': 16, '22-Plasma Cell': 13, '11-NK Cell': 10}\n",
      "{'0-FABP4hi Macrophage': 625, '1-SPP1hi Macrophage': 224, '3-Endothelial': 216, '2-T Helper/T Reg': 182, '19-B Cell': 131, '6-FCN1hi Monocyte': 128, '11-NK Cell': 112, '12-AT2': 111, '4-Cytotoxic T Cell': 84, '10-Smooth Muscle/Pericyte': 63, '15-Proliferating Cell': 60, '8-Ciliated': 37, '9-Goblet': 35, '5-Fibroblast': 33, '7-AT1/Club': 30, '16-Dendritic Cell': 27, '17-Lymphatic Endothelial': 21, '13-Mast Cell': 11, '22-Plasma Cell': 5, '14-Basal': 4, '20-Low Quality Ciliated': 4, '18-Mast Cell': 2, '21-Low Quality Basal': 1}\n",
      "{'2 T-Cell': 770, '4-Cytotoxic T Cell': 666, '0 FABP4hi Macrophage': 654, '5 T-cell/NK cell': 620, '0-FABP4hi Macrophage': 594, '2-T Helper/T Reg': 582, '4 Endothelial': 566, '1-SPP1hi Macrophage': 524, '3 FCN1hi monocyte': 502, '1 SPP1hi Macrophage': 486, '3-Endothelial': 476, '6-FCN1hi Monocyte': 470, '6 AT2': 332, '12-AT2': 299, '11-NK Cell': 148, '10-Smooth Muscle/Pericyte': 144, '7 Fibroblast/SMC/pericyte': 141, '10 Dendritic cell': 103, '13-Mast Cell': 91, '7-AT1/Club': 70, '16-Dendritic Cell': 68, '8 AT1': 51, '5-Fibroblast': 47, '9 Ciliated Cell': 46, '13 Mast cell': 45, '14 Proliferating cell': 44, '15-Proliferating Cell': 38, '12 Club': 36, '8-Ciliated': 29, '15 B-cell/plasma cell': 23, '9-Goblet': 22, '20-Low Quality Ciliated': 17, '11 Lymphatic endothelial': 17, '17-Lymphatic Endothelial': 16, '19-B Cell': 14, '22-Plasma Cell': 10, '14-Basal': 6, '18-Mast Cell': 3}\n",
      "{'1-SPP1hi Macrophage': 1052, '5-Fibroblast': 603, '2-T Helper/T Reg': 548, '3-Endothelial': 363, '13-Mast Cell': 268, '9-Goblet': 253, '4-Cytotoxic T Cell': 235, '0-FABP4hi Macrophage': 232, '6-FCN1hi Monocyte': 192, '18-Mast Cell': 160, '17-Lymphatic Endothelial': 121, '15-Proliferating Cell': 102, '7-AT1/Club': 101, '10-Smooth Muscle/Pericyte': 95, '19-B Cell': 88, '16-Dendritic Cell': 88, '8-Ciliated': 83, '11-NK Cell': 81, '14-Basal': 51, '22-Plasma Cell': 44, '12-AT2': 36, '20-Low Quality Ciliated': 19, '21-Low Quality Basal': 13}\n",
      "{'3-Endothelial': 462, '1-SPP1hi Macrophage': 403, '14-Basal': 321, '5-Fibroblast': 268, '7-AT1/Club': 250, '8-Ciliated': 219, '9-Goblet': 170, '10-Smooth Muscle/Pericyte': 98, '13-Mast Cell': 79, '21-Low Quality Basal': 77, '2-T Helper/T Reg': 74, '6-FCN1hi Monocyte': 42, '20-Low Quality Ciliated': 41, '4-Cytotoxic T Cell': 40, '15-Proliferating Cell': 38, '16-Dendritic Cell': 36, '12-AT2': 22, '0-FABP4hi Macrophage': 15, '22-Plasma Cell': 13, '19-B Cell': 13, '17-Lymphatic Endothelial': 11, '11-NK Cell': 7, '18-Mast Cell': 1}\n",
      "{'1-SPP1hi Macrophage': 998, '3-Endothelial': 554, '4-Cytotoxic T Cell': 461, '0-FABP4hi Macrophage': 361, '2-T Helper/T Reg': 281, '10-Smooth Muscle/Pericyte': 206, '5-Fibroblast': 176, '6-FCN1hi Monocyte': 171, '11-NK Cell': 142, '9-Goblet': 101, '19-B Cell': 77, '15-Proliferating Cell': 74, '16-Dendritic Cell': 68, '7-AT1/Club': 61, '13-Mast Cell': 52, '18-Mast Cell': 51, '8-Ciliated': 43, '17-Lymphatic Endothelial': 41, '12-AT2': 40, '14-Basal': 29, '20-Low Quality Ciliated': 18, '22-Plasma Cell': 11, '21-Low Quality Basal': 3}\n",
      "{'2 T-Cell': 238, '0 FABP4hi Macrophage': 183, '5 T-cell/NK cell': 85, '1 SPP1hi Macrophage': 68, '3 FCN1hi monocyte': 52, '15 B-cell/plasma cell': 32, '7 Fibroblast/SMC/pericyte': 12, '14 Proliferating cell': 11, '4 Endothelial': 11, '12 Club': 6, '10 Dendritic cell': 3, '6 AT2': 3, '9 Ciliated Cell': 1, '11 Lymphatic endothelial': 1}\n",
      "{'1-SPP1hi Macrophage': 871, '0-FABP4hi Macrophage': 676, '3-Endothelial': 666, '2-T Helper/T Reg': 420, '11-NK Cell': 240, '5-Fibroblast': 222, '4-Cytotoxic T Cell': 213, '7-AT1/Club': 174, '6-FCN1hi Monocyte': 172, '13-Mast Cell': 133, '15-Proliferating Cell': 128, '12-AT2': 128, '9-Goblet': 128, '8-Ciliated': 94, '18-Mast Cell': 88, '17-Lymphatic Endothelial': 85, '16-Dendritic Cell': 85, '10-Smooth Muscle/Pericyte': 81, '14-Basal': 37, '19-B Cell': 35, '20-Low Quality Ciliated': 26, '22-Plasma Cell': 11, '21-Low Quality Basal': 4}\n",
      "{'1-SPP1hi Macrophage': 1081, '3-Endothelial': 496, '9-Goblet': 255, '7-AT1/Club': 216, '8-Ciliated': 187, '20-Low Quality Ciliated': 130, '4-Cytotoxic T Cell': 115, '10-Smooth Muscle/Pericyte': 106, '5-Fibroblast': 99, '21-Low Quality Basal': 99, '2-T Helper/T Reg': 90, '13-Mast Cell': 70, '14-Basal': 70, '6-FCN1hi Monocyte': 51, '15-Proliferating Cell': 46, '0-FABP4hi Macrophage': 36, '11-NK Cell': 35, '16-Dendritic Cell': 27, '17-Lymphatic Endothelial': 17, '19-B Cell': 14, '12-AT2': 13, '18-Mast Cell': 4, '22-Plasma Cell': 2}\n"
     ]
    }
   ],
   "source": [
    "for f in set(df['patient']):\n",
    "    print(df[df['patient'] == f]['annotation'].value_counts().to_dict())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metadata\n",
    "- Currently metadata is nowwhere to be found.\n",
    "- We use the LCA annotation metadata (atm, confirm labels).\n",
    "- Replace h5ad path with the latest LCA h5ad generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_annotated_lca_h5ad_path = ''\n",
    "if exists(raw_annotated_lca_h5ad_path):\n",
    "    lca = sc.read_h5ad(raw_annotated_lca_h5ad_path)\n",
    "    laf = lca[lca.obs['dataset'].isin({'Pittsburgh_Lafyatis_2019Morse_10Xv1',\n",
    "                                       'Pittsburgh_Lafyatis_2019Morse_10Xv2'}),:]\n",
    "    laf.write('../data/morse/morse_lca_with_metadata.h5ad', compression='lzf')\n",
    "    \n",
    "    laf_mac = laf[laf.obs['original_celltype_ann'].str.contains('Macrophage'),:]\n",
    "    laf_mac.write('../data/morse/morse_lca_with_metadata_macrophages.h5ad', compression='lzf')\n",
    "laf_mac = sc.read_h5ad('../data/morse/morse_lca_with_metadata_macrophages.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "laf_mac_ad_by_sample = {sample: laf_mac[laf_mac.obs['sample'] == sample,:] for sample in set(laf_mac.obs['sample'])}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read raw counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'GSM3660641_SC14NOR',\n",
       " 'GSM3660642_SC31NOR',\n",
       " 'GSM3660643_SC31DNOR',\n",
       " 'GSM3660644_SC45NOR',\n",
       " 'GSM3660645_SC56NOR',\n",
       " 'GSM3660646_SC59NOR',\n",
       " 'GSM3660647_SC155NORLOW',\n",
       " 'GSM3660648_SC156NORUP',\n",
       " 'GSM3660649_SC228NORbal',\n",
       " 'GSM3660650_SC249NORbal',\n",
       " 'GSM3660651_SC87IPFLOW',\n",
       " 'GSM3660652_SC88IPFUP',\n",
       " 'GSM3660653_SC89IPFLOW',\n",
       " 'GSM3660654_SC93IPFLOW',\n",
       " 'GSM3660655_SC94IPFUP',\n",
       " 'GSM3660656_SC95IPFLOW',\n",
       " 'GSM3660657_SC153IPFLOW',\n",
       " 'GSM3660658_SC154IPFUP'}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniq_k = {\"_\".join(k.split(\"_\")[:2]) for k in listdir(d) if k.endswith(\".mtx.gz\")}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True data/morse/by_patient/GSM3660648_SC156NORUP.h5ad\n",
      "False data/morse/by_patient/GSM3660644_SC45NOR.h5ad\n",
      "GSM3660644_SC45NOR False False\n",
      "True ../data/morse/GSM3660644_SC45NOR_matrix.mtx.gz\n",
      "True ../data/morse/GSM3660644_SC45NOR_genes.tsv.gz\n",
      "True ../data/morse/GSM3660644_SC45NOR_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/icb/ignacio.ibarra/miniconda3/envs/mypython3/lib/python3.7/site-packages/anndata/_core/anndata.py:1192: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if is_string_dtype(df[key]) and not is_categorical(df[key])\n",
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False data/morse/by_patient/GSM3660657_SC153IPFLOW.h5ad\n",
      "GSM3660657_SC153IPFLOW False False\n",
      "True ../data/morse/GSM3660657_SC153IPFLOW_matrix.mtx.gz\n",
      "True ../data/morse/GSM3660657_SC153IPFLOW_genes.tsv.gz\n",
      "True ../data/morse/GSM3660657_SC153IPFLOW_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False data/morse/by_patient/GSM3660645_SC56NOR.h5ad\n",
      "GSM3660645_SC56NOR False False\n",
      "True ../data/morse/GSM3660645_SC56NOR_matrix.mtx.gz\n",
      "True ../data/morse/GSM3660645_SC56NOR_genes.tsv.gz\n",
      "True ../data/morse/GSM3660645_SC56NOR_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False data/morse/by_patient/GSM3660646_SC59NOR.h5ad\n",
      "GSM3660646_SC59NOR False False\n",
      "True ../data/morse/GSM3660646_SC59NOR_matrix.mtx.gz\n",
      "True ../data/morse/GSM3660646_SC59NOR_genes.tsv.gz\n",
      "True ../data/morse/GSM3660646_SC59NOR_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False data/morse/by_patient/GSM3660641_SC14NOR.h5ad\n",
      "GSM3660641_SC14NOR False False\n",
      "True ../data/morse/GSM3660641_SC14NOR_matrix.mtx.gz\n",
      "True ../data/morse/GSM3660641_SC14NOR_genes.tsv.gz\n",
      "True ../data/morse/GSM3660641_SC14NOR_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False data/morse/by_patient/GSM3660656_SC95IPFLOW.h5ad\n",
      "GSM3660656_SC95IPFLOW False False\n",
      "True ../data/morse/GSM3660656_SC95IPFLOW_matrix.mtx.gz\n",
      "True ../data/morse/GSM3660656_SC95IPFLOW_genes.tsv.gz\n",
      "True ../data/morse/GSM3660656_SC95IPFLOW_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False data/morse/by_patient/GSM3660643_SC31DNOR.h5ad\n",
      "GSM3660643_SC31DNOR False False\n",
      "True ../data/morse/GSM3660643_SC31DNOR_matrix.mtx.gz\n",
      "True ../data/morse/GSM3660643_SC31DNOR_genes.tsv.gz\n",
      "True ../data/morse/GSM3660643_SC31DNOR_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False data/morse/by_patient/GSM3660652_SC88IPFUP.h5ad\n",
      "GSM3660652_SC88IPFUP False False\n",
      "True ../data/morse/GSM3660652_SC88IPFUP_matrix.mtx.gz\n",
      "True ../data/morse/GSM3660652_SC88IPFUP_genes.tsv.gz\n",
      "True ../data/morse/GSM3660652_SC88IPFUP_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False data/morse/by_patient/GSM3660649_SC228NORbal.h5ad\n",
      "GSM3660649_SC228NORbal False False\n",
      "False ../data/morse/GSM3660649_SC228NORbal_matrix.mtx.gz\n",
      "False ../data/morse/GSM3660649_SC228NORbal_genes.tsv.gz\n",
      "False ../data/morse/GSM3660649_SC228NORbal_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False data/morse/by_patient/GSM3660651_SC87IPFLOW.h5ad\n",
      "GSM3660651_SC87IPFLOW False False\n",
      "True ../data/morse/GSM3660651_SC87IPFLOW_matrix.mtx.gz\n",
      "True ../data/morse/GSM3660651_SC87IPFLOW_genes.tsv.gz\n",
      "True ../data/morse/GSM3660651_SC87IPFLOW_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False data/morse/by_patient/GSM3660653_SC89IPFLOW.h5ad\n",
      "GSM3660653_SC89IPFLOW False False\n",
      "True ../data/morse/GSM3660653_SC89IPFLOW_matrix.mtx.gz\n",
      "True ../data/morse/GSM3660653_SC89IPFLOW_genes.tsv.gz\n",
      "True ../data/morse/GSM3660653_SC89IPFLOW_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False data/morse/by_patient/GSM3660647_SC155NORLOW.h5ad\n",
      "GSM3660647_SC155NORLOW False False\n",
      "True ../data/morse/GSM3660647_SC155NORLOW_matrix.mtx.gz\n",
      "True ../data/morse/GSM3660647_SC155NORLOW_genes.tsv.gz\n",
      "True ../data/morse/GSM3660647_SC155NORLOW_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False data/morse/by_patient/GSM3660650_SC249NORbal.h5ad\n",
      "GSM3660650_SC249NORbal False False\n",
      "False ../data/morse/GSM3660650_SC249NORbal_matrix.mtx.gz\n",
      "False ../data/morse/GSM3660650_SC249NORbal_genes.tsv.gz\n",
      "False ../data/morse/GSM3660650_SC249NORbal_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n",
      "... storing 'readout' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False data/morse/by_patient/GSM3660658_SC154IPFUP.h5ad\n",
      "GSM3660658_SC154IPFUP False False\n",
      "True ../data/morse/GSM3660658_SC154IPFUP_matrix.mtx.gz\n",
      "True ../data/morse/GSM3660658_SC154IPFUP_genes.tsv.gz\n",
      "True ../data/morse/GSM3660658_SC154IPFUP_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False data/morse/by_patient/GSM3660655_SC94IPFUP.h5ad\n",
      "GSM3660655_SC94IPFUP False False\n",
      "True ../data/morse/GSM3660655_SC94IPFUP_matrix.mtx.gz\n",
      "True ../data/morse/GSM3660655_SC94IPFUP_genes.tsv.gz\n",
      "True ../data/morse/GSM3660655_SC94IPFUP_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False data/morse/by_patient/GSM3660642_SC31NOR.h5ad\n",
      "GSM3660642_SC31NOR False False\n",
      "True ../data/morse/GSM3660642_SC31NOR_matrix.mtx.gz\n",
      "True ../data/morse/GSM3660642_SC31NOR_genes.tsv.gz\n",
      "True ../data/morse/GSM3660642_SC31NOR_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False data/morse/by_patient/GSM3660654_SC93IPFLOW.h5ad\n",
      "GSM3660654_SC93IPFLOW False False\n",
      "True ../data/morse/GSM3660654_SC93IPFLOW_matrix.mtx.gz\n",
      "True ../data/morse/GSM3660654_SC93IPFLOW_genes.tsv.gz\n",
      "True ../data/morse/GSM3660654_SC93IPFLOW_barcodes.tsv.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n",
      "... storing 'symbol' as categorical\n"
     ]
    }
   ],
   "source": [
    "for code in uniq_k:\n",
    "    output_path = 'data/morse/by_patient/%s.h5ad' % code\n",
    "    print(exists(output_path), output_path)\n",
    "    if exists(output_path):\n",
    "        continue\n",
    "    # print('reading', code)\n",
    "    \n",
    "    print(code,\n",
    "          code.split('_')[1][:4] in set(df['patient']),\n",
    "          code.split('_')[1][:5] in set(df['patient']))\n",
    "    \n",
    "    # if code in ad_by_k:\n",
    "    #     continue\n",
    "    mtx_path = join(d, '%s_matrix.mtx.gz' % code)\n",
    "    var_path = join(d, '%s_genes.tsv.gz' % code)\n",
    "    obs_path = join(d, '%s_barcodes.tsv.gz' % code)\n",
    "\n",
    "    # print(exists(mtx_path), mtx_path)\n",
    "    # print(exists(var_path), var_path)\n",
    "    # print(exists(obs_path), obs_path)\n",
    "\n",
    "    if not exists(mtx_path):\n",
    "        mtx_path = join(d, '%s_frozen_matrix.mtx.gz' % code)\n",
    "        var_path = join(d, '%s_frozen_genes.tsv.gz' % code)\n",
    "        obs_path = join(d, '%s_frozen_barcodes.tsv.gz' % code)\n",
    "    if not exists(mtx_path):\n",
    "        mtx_path = join(d, '%s_fresh_matrix.mtx.gz' % code)\n",
    "        var_path = join(d, '%s_fresh_genes.tsv.gz' % code)\n",
    "        obs_path = join(d, '%s_fresh_barcodes.tsv.gz' % code)\n",
    "        \n",
    "    ad = sc.read_mtx(mtx_path, dtype='float32').transpose()\n",
    "    obs = pd.read_csv(obs_path, header=None, sep='\\t')\n",
    "    var = pd.read_csv(var_path, header=None, sep='\\t')\n",
    "    \n",
    "    var.columns = ['ensembl', 'symbol'] if len(var.columns) == 2 else ['ensembl', 'symbol', 'readout']\n",
    "    var.index = np.array(var.ensembl)\n",
    "    ad.var = var\n",
    "    obs.columns = ['barcode']\n",
    "    ad.obs.index = obs['barcode']\n",
    "    \n",
    "    ad.obs['GSM'] = code.split('_')[0]\n",
    "    ad.obs['sample.id'] = code.split('_')[1]\n",
    "    # ad_by_k[code] = ad\n",
    "    \n",
    "    ad.write(output_path, compression='lzf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/icb/ignacio.ibarra/miniconda3/envs/mypython3/lib/python3.7/site-packages/anndata/_core/anndata.py:1192: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if is_string_dtype(df[key]) and not is_categorical(df[key])\n",
      "... storing 'GSM' as categorical\n",
      "... storing 'sample.id' as categorical\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "var.columns = ['ensembl', 'symbol'] if len(var.columns) == 2 else ['ensembl', 'symbol', 'readout']\n",
    "var.index = np.array(var.symbol)\n",
    "ad.var = var\n",
    "obs.columns = ['barcode']\n",
    "ad.obs.index = obs['barcode']\n",
    "\n",
    "ad.obs['GSM'] = code.split('_')[0]\n",
    "ad.obs['sample.id'] = code.split('_')[1]\n",
    "# ad_by_k[code] = ad\n",
    "\n",
    "ad.write(output_path, compression='lzf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM3660643_SC31DNOR.h5ad\n",
      "GSM3660645_SC56NOR.h5ad\n",
      "GSM3660650_SC249NORbal.h5ad\n",
      "GSM3660652_SC88IPFUP.h5ad\n",
      "GSM3660641_SC14NOR.h5ad\n",
      "GSM3660658_SC154IPFUP.h5ad\n",
      "GSM3660654_SC93IPFLOW.h5ad\n",
      "GSM3660647_SC155NORLOW.h5ad\n",
      "GSM3660649_SC228NORbal.h5ad\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM3660648_SC156NORUP.h5ad\n",
      "GSM3660646_SC59NOR.h5ad\n",
      "GSM3660657_SC153IPFLOW.h5ad\n",
      "GSM3660656_SC95IPFLOW.h5ad\n",
      "GSM3660655_SC94IPFUP.h5ad\n",
      "GSM3660653_SC89IPFLOW.h5ad\n",
      "GSM3660642_SC31NOR.h5ad\n",
      "GSM3660644_SC45NOR.h5ad\n",
      "GSM3660651_SC87IPFLOW.h5ad\n"
     ]
    }
   ],
   "source": [
    "ad_by_code = {}\n",
    "by_patient_directory = 'data/morse/by_patient'\n",
    "for f in listdir(by_patient_directory):\n",
    "    h5ad_path = join(by_patient_directory, f)\n",
    "    ad = sc.read_h5ad(h5ad_path)\n",
    "    code = f.split('_')[1].split('.')[0]\n",
    "    print(f)\n",
    "    ad_by_code[f] = ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM3660643_SC31DNOR.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(491, 33694)\n",
      "normalizing\n",
      "HGV/pca/neighbors\n",
      "GSM3660645_SC56NOR.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3223, 33694)\n",
      "normalizing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HGV/pca/neighbors\n",
      "GSM3660650_SC249NORbal.h5ad (737280, 33538)\n",
      "(737280, 33538)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3205, 33538)\n",
      "normalizing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HGV/pca/neighbors\n",
      "GSM3660652_SC88IPFUP.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3881, 33694)\n",
      "normalizing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HGV/pca/neighbors\n",
      "GSM3660641_SC14NOR.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(298, 33694)\n",
      "normalizing\n",
      "HGV/pca/neighbors\n",
      "GSM3660658_SC154IPFUP.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3436, 33694)\n",
      "normalizing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HGV/pca/neighbors\n",
      "GSM3660654_SC93IPFLOW.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2899, 33694)\n",
      "normalizing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HGV/pca/neighbors\n",
      "GSM3660647_SC155NORLOW.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3486, 33694)\n",
      "normalizing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HGV/pca/neighbors\n",
      "GSM3660649_SC228NORbal.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(809, 33694)\n",
      "normalizing\n",
      "HGV/pca/neighbors\n",
      "GSM3660648_SC156NORUP.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4317, 33694)\n",
      "normalizing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HGV/pca/neighbors\n",
      "GSM3660646_SC59NOR.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2814, 33694)\n",
      "normalizing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HGV/pca/neighbors\n",
      "GSM3660657_SC153IPFLOW.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3069, 33694)\n",
      "normalizing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HGV/pca/neighbors\n",
      "GSM3660656_SC95IPFLOW.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2629, 33694)\n",
      "normalizing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HGV/pca/neighbors\n",
      "GSM3660655_SC94IPFUP.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2028, 33694)\n",
      "normalizing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HGV/pca/neighbors\n",
      "GSM3660653_SC89IPFLOW.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3879, 33694)\n",
      "normalizing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HGV/pca/neighbors\n",
      "GSM3660642_SC31NOR.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(475, 33694)\n",
      "normalizing\n",
      "HGV/pca/neighbors\n",
      "GSM3660644_SC45NOR.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2934, 33694)\n",
      "normalizing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HGV/pca/neighbors\n",
      "GSM3660651_SC87IPFLOW.h5ad (737280, 33694)\n",
      "(737280, 33694)\n",
      "QC filter\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4299, 33694)\n",
      "normalizing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HGV/pca/neighbors\n"
     ]
    }
   ],
   "source": [
    "for k in ad_by_code:\n",
    "    \n",
    "    ad = ad_by_code[k]\n",
    "\n",
    "    print(k, ad.shape)\n",
    "\n",
    "    ad.var.index = list(ad.var.symbol);\n",
    "\n",
    "    print(ad.shape)\n",
    "    print('QC filter');\n",
    "    # Quality control - calculate QC covariates\n",
    "    ad.obs['n_counts'] = ad.X.sum(1);\n",
    "    ad.obs['log_counts'] = np.log(ad.obs['n_counts']);\n",
    "    ad.obs['n_genes'] = (ad.X > 0).sum(1);\n",
    "    mt_gene_mask = [gene.startswith('MT-') for gene in ad.var['symbol']];\n",
    "    ad.obs['mt_frac'] = ad.X[:, mt_gene_mask].toarray().sum(1) / ad.obs['n_counts'];\n",
    "    \n",
    "    ad = ad[ad.X.sum(axis=1) > 1000,:];\n",
    "    ad = ad[ad.obs['mt_frac'] < 0.2,:];\n",
    "    ad = ad[ad.obs['n_genes'] > 700,:];\n",
    "\n",
    "    print(ad.shape)\n",
    "    ad.raw = ad.copy()\n",
    "    \n",
    "    print('normalizing')\n",
    "    sc.pp.normalize_per_cell(ad, counts_per_cell_after=1e6);\n",
    "    sc.pp.log1p(ad);\n",
    "\n",
    "    print('HGV/pca/neighbors')\n",
    "    sc.pp.highly_variable_genes(ad, n_top_genes=4000);\n",
    "    # Calculate the visualizations\n",
    "    sc.pp.pca(ad, n_comps=50, use_highly_variable=True, svd_solver='arpack');\n",
    "    sc.pp.neighbors(ad);\n",
    "\n",
    "    sc.tl.tsne(ad, n_jobs=12) #Note n_jobs works for MulticoreTSNE, but not regular implementation);\n",
    "    sc.tl.umap(ad);\n",
    "    sc.tl.diffmap(ad);\n",
    "    sc.tl.draw_graph(ad);\n",
    "\n",
    "    ad_by_code[k] = ad\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are the counts properly stored in the **raw** object?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "please check here...\n"
     ]
    }
   ],
   "source": [
    "print('please check here...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.322084"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad.X.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Include information from LCA in each case and then save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SC14NOR          706\n",
       "SC31NOR         1043\n",
       "SC31DNOR        1185\n",
       "SC94IPFUP       2146\n",
       "SC95IPFLOW      2700\n",
       "SC93IPFLOW      3220\n",
       "SC153IPFLOW     3259\n",
       "SC45NOR         3389\n",
       "SC154IPFUP      4019\n",
       "SC88IPFUP       4717\n",
       "SC89IPFLOW      4828\n",
       "SC87IPFLOW      5651\n",
       "SC59NOR         6429\n",
       "SC155NOR        8377\n",
       "SC56NOR         8770\n",
       "SC156NOR       11552\n",
       "Name: patient, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['patient'].value_counts().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'SC87IPFLOW'}\n"
     ]
    }
   ],
   "source": [
    "ad.obs['patient.id'] = ad.obs['sample.id'].str.replace('NORLOW', '')\n",
    "print(set(ad.obs['patient.id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SC156    11552\n",
       "SC56      8770\n",
       "SC155     8377\n",
       "SC59      6429\n",
       "SC87      5651\n",
       "SC89      4828\n",
       "SC88      4717\n",
       "SC154     4019\n",
       "SC45      3389\n",
       "SC153     3259\n",
       "SC93      3220\n",
       "SC95      2700\n",
       "SC94      2146\n",
       "SC31D     1185\n",
       "SC31      1043\n",
       "SC14       706\n",
       "Name: patient.id, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['patient.id'] = df['patient'].str.replace('.h5ad', '').str.replace('NOR', '').str.replace('bal', '').str.replace('IPF', '').str.replace('LOW', '').str.replace('UP', '')\n",
    "df.index = df['patient.id'] + \"_\" + df.index.str.split('_').str[1]\n",
    "df['patient.id'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'patient.id' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'cell.type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM3660643_SC31DNOR.h5ad [173, 66, 58, 47, 46, 21, 16, 12, 9, 8, 5, 3, 2, 2, 1, 1] 21\n",
      "(239, 33694)\n",
      "done...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'patient.id' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'cell.type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM3660645_SC56NOR.h5ad [629, 460, 454, 448, 388, 308, 167, 99, 84, 30, 29, 29, 28, 15, 11, 7, 3, 2, 1] 31\n",
      "(1080, 33694)\n",
      "done...\n",
      "GSM3660650_SC249NORbal.h5ad [] 3205\n",
      "(0, 33538)\n",
      "done...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'patient.id' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'cell.type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM3660652_SC88IPFUP.h5ad [850, 669, 588, 219, 175, 138, 121, 119, 104, 100, 97, 93, 85, 84, 69, 61, 48, 36, 29, 22, 10, 4] 160\n",
      "(1519, 33694)\n",
      "done...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'patient.id' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'cell.type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM3660641_SC14NOR.h5ad [149, 54, 27, 22, 6, 5, 2, 2, 2, 1, 1] 27\n",
      "(203, 33694)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done...\n",
      "GSM3660658_SC154IPFUP.h5ad [962, 492, 361, 252, 186, 171, 156, 132, 103, 71, 67, 67, 53, 43, 40, 39, 38, 37, 29, 19, 5, 5] 108\n",
      "(1323, 33694)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'patient.id' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'cell.type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'patient.id' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'cell.type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM3660654_SC93IPFLOW.h5ad [519, 490, 312, 278, 234, 231, 164, 81, 74, 66, 58, 49, 44, 31, 22, 20, 19, 18, 13, 12, 6, 3] 155\n",
      "(509, 33694)\n",
      "done...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'patient.id' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'cell.type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM3660647_SC155NORLOW.h5ad [992, 733, 532, 249, 152, 148, 109, 108, 79, 78, 77, 74, 39, 32, 29, 18, 8] 29\n",
      "(1733, 33694)\n",
      "done...\n",
      "GSM3660649_SC228NORbal.h5ad [] 809\n",
      "(0, 33694)\n",
      "done...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'patient.id' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'cell.type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM3660648_SC156NORUP.h5ad [1697, 461, 402, 288, 255, 242, 167, 161, 123, 121, 79, 75, 73, 60, 50, 35, 4, 1, 1] 22\n",
      "(2104, 33694)\n",
      "done...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'patient.id' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'cell.type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM3660646_SC59NOR.h5ad [1220, 521, 211, 137, 122, 107, 86, 80, 77, 64, 51, 28, 28, 22, 21, 13, 4, 1, 1, 1, 1] 18\n",
      "(1743, 33694)\n",
      "done...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'patient.id' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'cell.type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM3660657_SC153IPFLOW.h5ad [1063, 483, 242, 202, 186, 97, 85, 68, 60, 60, 44, 43, 41, 36, 28, 26, 26, 13, 12, 9, 2, 2, 1] 240\n",
      "(1099, 33694)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'patient.id' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done...\n",
      "GSM3660656_SC95IPFLOW.h5ad [450, 400, 320, 267, 239, 217, 167, 68, 66, 59, 42, 36, 35, 32, 22, 15, 13, 10, 8, 7, 7, 6, 1] 142\n",
      "(415, 33694)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'cell.type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'patient.id' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'cell.type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM3660655_SC94IPFUP.h5ad [623, 219, 164, 133, 110, 108, 107, 106, 74, 57, 33, 32, 30, 27, 22, 17, 13, 6, 4, 2, 1] 140\n",
      "(842, 33694)\n",
      "done...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'patient.id' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'cell.type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM3660653_SC89IPFLOW.h5ad [1000, 590, 349, 298, 223, 170, 169, 108, 88, 87, 84, 83, 78, 71, 64, 57, 50, 48, 47, 33, 25, 7] 150\n",
      "(1223, 33694)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'patient.id' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done...\n",
      "GSM3660642_SC31NOR.h5ad [158, 55, 52, 51, 49, 22, 19, 11, 10, 10, 7, 4, 4, 3, 3, 1] 16\n",
      "(213, 33694)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'cell.type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'patient.id' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'cell.type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM3660644_SC45NOR.h5ad [1696, 273, 239, 195, 124, 89, 68, 46, 30, 29, 28, 25, 21, 20, 11, 10] 30\n",
      "(1935, 33694)\n",
      "done...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'patient.id' as categorical\n",
      "Trying to set attribute `.obs` of view, copying.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "... storing 'cell.type' as categorical\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GSM3660651_SC87IPFLOW.h5ad [1152, 685, 431, 386, 219, 185, 142, 111, 107, 96, 92, 82, 78, 76, 76, 74, 51, 50, 50, 29, 24, 11, 1] 91\n",
      "(1371, 33694)\n",
      "done...\n"
     ]
    }
   ],
   "source": [
    "# ad_final = []\n",
    "by_patient_directory = 'data/morse/by_patient_scanpy_norm_mac'\n",
    "for k in ad_by_code:\n",
    "    patient_id = k.split('_')[1].replace('.h5ad', '').replace('NOR', '').replace('bal', '').replace('IPF', '').replace('LOW', '').replace('UP', '')\n",
    "    p = join(by_patient_directory, k)\n",
    "    ad = ad_by_code[k]\n",
    "    ad.obs['patient.id'] = patient_id\n",
    "    \n",
    "    ad.obs['cell.type'] = (ad.obs['patient.id'].astype(str)  + '_' + ad.obs.index.str.split('-').str[0]).map(df['annotation'].to_dict())\n",
    "    print(k, ad.obs['cell.type'].value_counts().tolist(), sum(pd.isnull(ad.obs['cell.type'])));\n",
    "    mask = np.array(ad.obs['cell.type'].str.contains('Macrophage')) == True\n",
    "    ad_sel = ad[mask,:]\n",
    "    print(ad_sel.shape);\n",
    "    ad_sel.obs.index = ad_sel.obs['sample.id'].astype(str) + '_' + ad_sel.obs.index.astype(str)\n",
    "    \n",
    "    ad_sel.write(p, compression='lzf')\n",
    "    print('done...')\n",
    "    if not exists(p):\n",
    "        continue\n",
    "    # ad_sel.obs.index = ad_sel.obs.index.str[:-2]\n",
    "    # ad_final.append(ad_sel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "barcode\n",
       "AAACCTGAGTAGCGGT-1    SC56N_AAACCTGAGTAGCGGT\n",
       "AAACCTGCAACACCTA-1    SC56N_AAACCTGCAACACCTA\n",
       "AAACCTGCACGGTAGA-1    SC56N_AAACCTGCACGGTAGA\n",
       "AAACCTGCAGCTGGCT-1    SC56N_AAACCTGCAGCTGGCT\n",
       "AAACCTGGTTCCGGCA-1    SC56N_AAACCTGGTTCCGGCA\n",
       "                               ...          \n",
       "TTTGTCACAGCTCGAC-1    SC56N_TTTGTCACAGCTCGAC\n",
       "TTTGTCACAGTATAAG-1    SC56N_TTTGTCACAGTATAAG\n",
       "TTTGTCACATGTCGAT-1    SC56N_TTTGTCACATGTCGAT\n",
       "TTTGTCAGTTGGGACA-1    SC56N_TTTGTCAGTTGGGACA\n",
       "TTTGTCATCGCCAAAT-1    SC56N_TTTGTCATCGCCAAAT\n",
       "Length: 3223, dtype: object"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad.obs['sample.id'].astype(str).str[:5]  + '_' + ad.obs.index.str.split('-').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = None\n",
    "for ai, ad in enumerate(ad_final):\n",
    "    break\n",
    "    print(ai)\n",
    "    if ad.shape[0] == 0:\n",
    "        continue\n",
    "    print(adata)\n",
    "    if adata is not None:\n",
    "        adata = adata.concatenate(ad)\n",
    "        adata.var.index = np.array(adata.var.ensembl)\n",
    "    else:\n",
    "        adata = ad.copy()\n",
    "        adata.obs.index = adata.obs.index.str[:-2]\n",
    "        adata.var.index = np.array(adata.var.ensembl)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ad_macrophages_by_code = {}\n",
    "for code in ad_by_k:\n",
    "    ad = ad_by_k[code]\n",
    "    \n",
    "    lca_k_sel = None\n",
    "    for k_lca in laf_mac_ad_by_sample.keys():\n",
    "        if k_lca in code:\n",
    "            lca_k_sel = k_lca\n",
    "\n",
    "    print(code, metadata.shape)\n",
    "    \n",
    "    metadata = pd.DataFrame(laf_mac_ad_by_sample[lca_k_sel].obs.copy())\n",
    "    metadata['10x.barcode'] = set(metadata.index.str.split('_').str[0] + '-1')\n",
    "    metadata.index = metadata['10x.barcode']\n",
    "    sel = ad[ad.obs.index.isin(metadata.index),:]\n",
    "    for c in metadata.reindex(sel.obs.index):\n",
    "        sel.obs[c] = metadata[c]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# concatenate\n",
    "ad_macrophages_by_code.keys()\n",
    "ad_mac = None\n",
    "for k in ad_macrophages_by_code:\n",
    "    print(k)\n",
    "    next_ad = ad_macrophages_by_code[k].copy()\n",
    "    next_ad.obs.index += '-' + k\n",
    "    print(list(next_ad.obs.index[:5]))\n",
    "    if ad_mac is not None:\n",
    "        ad_mac = ad_mac.concatenate(next_ad)\n",
    "        ad_mac.obs.index = ad_mac.obs.index.str[:-2]\n",
    "    else:\n",
    "        ad_mac = next_ad\n",
    "ad_mac.var.index = list(ad_mac.var.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ad.var.index = ad.var.symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ad_mac.write('../data/morse/morse_input_scvi.h5ad', compression='lzf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here attempt to do the normalization and other steps following the scran approach. Also. Plot marker genes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 9805 × 33694\n",
       "    obs: 'GSM', 'sample.id', 'dataset', 'last_author/PI', 'lung_vs_nasal', 'original_celltype_ann', 'sample', 'total_counts', 'log10_total_counts', 'n_genes_detected', 'mito_frac', 'ribo_frac', 'compl', 'ann_level_1', 'ann_level_2', 'ann_level_3', 'ann_level_4', 'ann_level_5', 'ann_highest_res', 'ann_new', \"3' or 5'\", 'BMI', 'Institute', 'Study_PI', 'age, in years', 'age, range', 'anatomical region coarse', 'anatomical region detailed', 'cause of death', 'cell ranger version', 'cell ranger version ', 'cell viability %', 'cells or nuclei', 'comments', 'condition', 'cultured', 'disease status', 'enrichment', 'enrichment ', 'ensembl release reference genome', 'ethnicity', 'fresh or frozen', 'known lung disease', 'library-construction batch', 'library_ID', 'mixed ethnicity', 'number of cells loaded', 'pre or postnatal', 'publication_ID', 'reference genome coarse', 'relative sample collection timepoint', 'repository_ID', 'sample type', 'sequencing platform', 'sex', 'single cell platform', 'smoking history', 'smoking status', 'subject type', 'subject_ID', 'subject_ID_as_published', 'tissue dissociation protocol', 'treatment status', 'year of sample collection', '10x.barcode', 'batch'\n",
       "    var: 'ensembl', 'symbol'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad = sc.read_h5ad('../data/morse/morse_input_scvi.h5ad')\n",
    "ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SC156    2235\n",
       "SC45     2074\n",
       "SC59     1839\n",
       "SC155    1808\n",
       "SC56     1140\n",
       "SC31      458\n",
       "SC14      251\n",
       "Name: sample, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad.obs['sample'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
      "  %reload_ext rpy2.ipython\n"
     ]
    }
   ],
   "source": [
    "import scvi\n",
    "import scanpy as sc\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "\n",
    "\n",
    "plt.rcParams['figure.figsize']=(6,6) #rescale figures\n",
    "\n",
    "from os.path import join, exists\n",
    "from os import listdir\n",
    "\n",
    "import anndata2ri\n",
    "import rpy2\n",
    "import numpy as np\n",
    "%load_ext rpy2.ipython\n",
    "\n",
    "import anndata2ri\n",
    "anndata2ri.activate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "library(scran)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the main working objects we need to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/icb/ignacio.ibarra/miniconda3/envs/mypython3/lib/python3.7/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Quality control - calculate QC covariates\n",
    "ad.obs['n_counts'] = ad.X.sum(1)\n",
    "ad.obs['log_counts'] = np.log(ad.obs['n_counts'])\n",
    "ad.obs['n_genes'] = (ad.X > 0).sum(1)\n",
    "mt_gene_mask = [gene.startswith('MT-') for gene in ad.var['symbol']]\n",
    "ad.obs['mt_frac'] = ad.X[:, mt_gene_mask].toarray().sum(1) / ad.obs['n_counts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/icb/ignacio.ibarra/miniconda3/envs/mypython3/lib/python3.7/site-packages/anndata/_core/anndata.py:1192: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if is_string_dtype(df[key]) and not is_categorical(df[key])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 483.52x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 483.52x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = [5, 5]\n",
    "rcParams['figure.dpi'] = 80\n",
    "\n",
    "# Quality control - plot QC metrics\n",
    "#Sample quality plots\n",
    "t1 = sc.pl.violin(ad, 'n_counts', groupby='original_celltype_ann', size=2, log=True, cut=0)\n",
    "t2 = sc.pl.violin(ad, 'mt_frac', groupby='original_celltype_ann')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 486.624x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/icb/ignacio.ibarra/miniconda3/envs/mypython3/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if not is_categorical(df_full[k]):\n",
      "/home/icb/ignacio.ibarra/miniconda3/envs/mypython3/lib/python3.7/site-packages/anndata/_core/anndata.py:1192: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if is_string_dtype(df[key]) and not is_categorical(df[key])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 486.624x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data quality summary plots\n",
    "p1 = sc.pl.scatter(ad, 'n_counts', 'n_genes', color='mt_frac')\n",
    "p2 = sc.pl.scatter(ad[ad.obs['n_counts'] < 10000], 'n_counts', 'n_genes', color='mt_frac')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Thresholding decision: counts\n",
    "p3 = sns.distplot(ad.obs['n_counts'], kde=False)\n",
    "plt.show()\n",
    "p4 = sns.distplot(ad.obs['n_counts'][ad.obs['n_counts']<4000], kde=False, bins=60)\n",
    "plt.show()\n",
    "p5 = sns.distplot(ad.obs['n_counts'][ad.obs['n_counts']>10000], kde=False, bins=60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform scran normalization in Morse to checkout behavior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform a clustering for scran normalization in clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Morse counts threshold filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/icb/ignacio.ibarra/miniconda3/envs/mypython3/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if not is_categorical(df_full[k]):\n",
      "/home/icb/ignacio.ibarra/miniconda3/envs/mypython3/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if not is_categorical(df_full[k]):\n",
      "/home/icb/ignacio.ibarra/miniconda3/envs/mypython3/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if not is_categorical(df_full[k]):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "View of AnnData object with n_obs × n_vars = 8780 × 33694\n",
       "    obs: 'GSM', 'sample.id', 'dataset', 'last_author/PI', 'lung_vs_nasal', 'original_celltype_ann', 'sample', 'total_counts', 'log10_total_counts', 'n_genes_detected', 'mito_frac', 'ribo_frac', 'compl', 'ann_level_1', 'ann_level_2', 'ann_level_3', 'ann_level_4', 'ann_level_5', 'ann_highest_res', 'ann_new', \"3' or 5'\", 'BMI', 'Institute', 'Study_PI', 'age, in years', 'age, range', 'anatomical region coarse', 'anatomical region detailed', 'cause of death', 'cell ranger version', 'cell ranger version ', 'cell viability %', 'cells or nuclei', 'comments', 'condition', 'cultured', 'disease status', 'enrichment', 'enrichment ', 'ensembl release reference genome', 'ethnicity', 'fresh or frozen', 'known lung disease', 'library-construction batch', 'library_ID', 'mixed ethnicity', 'number of cells loaded', 'pre or postnatal', 'publication_ID', 'reference genome coarse', 'relative sample collection timepoint', 'repository_ID', 'sample type', 'sequencing platform', 'sex', 'single cell platform', 'smoking history', 'smoking status', 'subject type', 'subject_ID', 'subject_ID_as_published', 'tissue dissociation protocol', 'treatment status', 'year of sample collection', '10x.barcode', 'batch', 'n_counts', 'log_counts', 'n_genes', 'mt_frac'\n",
       "    var: 'ensembl', 'symbol'\n",
       "    uns: 'original_celltype_ann_colors'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad = ad[ad.X.sum(axis=1) > 1000,:]\n",
    "ad = ad[ad.obs['mt_frac'] < 0.2,:]\n",
    "ad = ad[ad.obs['n_genes'] > 700,:]\n",
    "ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ad = ad.copy()\n",
    "sc.pp.normalize_per_cell(ad, counts_per_cell_after=1e6)\n",
    "sc.pp.log1p(ad)\n",
    "sc.pp.pca(ad, n_comps=15)\n",
    "sc.pp.neighbors(ad)\n",
    "sc.tl.louvain(ad, key_added='groups', resolution=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "ad.write('data/morse/morse_input_scvi_scanpy_norm.h5ad', compression='lzf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"Preprocess_scran_morse\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess variables for scran normalization\n",
    "- Important: **Here data_mat has to be an ndarray** otherwise an error is thrown and handling it is most likely hard/not possible (see anndata2ri docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_groups = ad_pp.obs['groups']\n",
    "data_mat = ad.X.T.toarray()\n",
    "# data_mat = ad.X.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R -i data_mat -i input_groups -o size_factors\n",
    "size_factors = computeSumFactors(data_mat, min.mean=.1) # clusters=input_groups, min.mean=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('done...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('done.2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Delete ad_pp\n",
    "del ad_pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize the estimated size factors\n",
    "ad.obs['size_factors'] = size_factors\n",
    "sc.pl.scatter(ad, 'size_factors', 'n_counts')\n",
    "sc.pl.scatter(ad, 'size_factors', 'n_genes')\n",
    "sns.distplot(size_factors, bins=50, kde=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Keep the count data in a counts layer\n",
    "ad.layers[\"counts\"] = ad.X.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalize adata \n",
    "ad.X /= ad.obs['size_factors'].values[:,None]\n",
    "sc.pp.log1p(ad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ad.X = np.array(ad.X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the full data set in 'raw' as log-normalised data for statistical testing\n",
    "ad.raw = ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(ad.raw.X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ad.X = np.array(ad.X)\n",
    "sc.pp.highly_variable_genes(ad, n_top_genes=4000)\n",
    "# print('\\n','Number of highly variable genes: {:d}'.format(np.sum(ad.var['highly_variable'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pl.highly_variable_genes(ad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the visualizations\n",
    "sc.pp.pca(ad, n_comps=50, use_highly_variable=True, svd_solver='arpack')\n",
    "sc.pp.neighbors(ad)\n",
    "\n",
    "sc.tl.tsne(ad, n_jobs=12) #Note n_jobs works for MulticoreTSNE, but not regular implementation)\n",
    "sc.tl.umap(ad)\n",
    "sc.tl.diffmap(ad)\n",
    "sc.tl.draw_graph(ad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pl.pca_scatter(ad, color='n_counts')\n",
    "sc.pl.tsne(ad, color='n_counts')\n",
    "sc.pl.umap(ad, color='n_counts')\n",
    "sc.pl.diffmap(ad, color='n_counts', components=['1,2','1,3'])\n",
    "sc.pl.draw_graph(ad, color='n_counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform clustering - using highly variable genes\n",
    "sc.tl.louvain(ad, key_added='louvain_r1')\n",
    "sc.tl.louvain(ad, resolution=0.5, key_added='louvain_r0.5', random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "condition_ad = {'SC14': 'healthy',\n",
    "                   'SC45': 'healthy',\n",
    "                   'SC56': 'healthy',\n",
    "                   'SC59': 'healthy',\n",
    "                   'SC155': 'healthy',\n",
    "                   'SC156': 'healthy'}\n",
    "ad.obs['condition.morse'] = ad.obs['sample'].map(condition_ad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ad.obs['louvain_r0.5'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Visualize the clustering and how this is reflected by different technical covariates\n",
    "sc.pl.umap(ad, color=['louvain_r1', 'louvain_r0.5'], palette=sc.pl.palettes.default_102)\n",
    "sc.pl.umap(ad, color=['original_celltype_ann', 'n_counts', 'condition.morse'])\n",
    "sc.pl.umap(ad, color=['log_counts', 'mt_frac', 'sample'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The following snippet of code needs to be put up otherwise this section will not run properly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ad.raw.X = np.array(ad.raw.X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Calculate marker genes\n",
    "sc.tl.rank_genes_groups(ad, groupby='louvain_r0.5', key_added='rank_genes_r0.5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot marker genes\n",
    "sc.pl.rank_genes_groups(ad, key='rank_genes_r0.5', groups=['0', '1', '2', '3'], fontsize=12)\n",
    "sc.pl.rank_genes_groups(ad, key='rank_genes_r0.5', groups=['4', '5', '6', '7'], fontsize=12)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Checking_marker_genes_saliba'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking marker genes proposed to be relevant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>ensembl</th>\n",
       "      <th>symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000243485</th>\n",
       "      <td>ENSG00000243485</td>\n",
       "      <td>RP11-34P13.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000237613</th>\n",
       "      <td>ENSG00000237613</td>\n",
       "      <td>FAM138A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000186092</th>\n",
       "      <td>ENSG00000186092</td>\n",
       "      <td>OR4F5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000238009</th>\n",
       "      <td>ENSG00000238009</td>\n",
       "      <td>RP11-34P13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000239945</th>\n",
       "      <td>ENSG00000239945</td>\n",
       "      <td>RP11-34P13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000277856</th>\n",
       "      <td>ENSG00000277856</td>\n",
       "      <td>AC233755.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000275063</th>\n",
       "      <td>ENSG00000275063</td>\n",
       "      <td>AC233755.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000271254</th>\n",
       "      <td>ENSG00000271254</td>\n",
       "      <td>AC240274.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000277475</th>\n",
       "      <td>ENSG00000277475</td>\n",
       "      <td>AC213203.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000268674</th>\n",
       "      <td>ENSG00000268674</td>\n",
       "      <td>FAM231B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33694 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         ensembl        symbol\n",
       "ENSG00000243485  ENSG00000243485  RP11-34P13.3\n",
       "ENSG00000237613  ENSG00000237613       FAM138A\n",
       "ENSG00000186092  ENSG00000186092         OR4F5\n",
       "ENSG00000238009  ENSG00000238009  RP11-34P13.7\n",
       "ENSG00000239945  ENSG00000239945  RP11-34P13.8\n",
       "...                          ...           ...\n",
       "ENSG00000277856  ENSG00000277856    AC233755.2\n",
       "ENSG00000275063  ENSG00000275063    AC233755.1\n",
       "ENSG00000271254  ENSG00000271254    AC240274.1\n",
       "ENSG00000277475  ENSG00000277475    AC213203.1\n",
       "ENSG00000268674  ENSG00000268674       FAM231B\n",
       "\n",
       "[33694 rows x 2 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad.var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'MP.markers': {'FTH1', 'CD63', 'FABP5', 'CTSL', 'GPNMB', 'APOE', 'SPP1', 'ACP5', 'LGALS3', 'CD9', 'CSTB', 'LPL', 'TREM2', 'CTSD', 'APOC1', 'CTSB', 'LIPA', 'FABP4', 'LGALS1', 'PSAP'}, 'MP.others': {'HEXB', 'CTSC', 'HEXA', 'IL7R', 'ADGRB1', 'MERTK', 'DOCK1', 'TYROBP', 'MFGE8', 'MMP12', 'FTL1'}}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'MP.markers': set(), 'MP.others': set()}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marker_genes = {}\n",
    "marker_genes['MP.markers'] = {\"TREM2\", \"CD9\", \"SPP1\", \"GPNMB\", \"LGALS3\", \"LGALS1\", \"FABP4\", \"FABP5\", \"ACP5\", \"PSAP\",\n",
    "                                 \"FTH1\", \"LIPA\", \"CTSD\", \"CTSB\", \"CSTB\", \"CTSL\", \"APOE\", \"APOC1\", \"CD63\", \"LPL\"}\n",
    "marker_genes['MP.others'] = {\"FTL1\", \"CTSC\", \"HEXA\", \"HEXB\", \"MMP12\", \"MERTK\", \"TYROBP\", \"MFGE8\", \"DOCK1\", \"ADGRB1\", \"IL7R\"}\n",
    "print(marker_genes)\n",
    "\n",
    "for k in marker_genes:\n",
    "    marker_genes[k] = marker_genes[k].intersection(set(ad.var.symbol))\n",
    "    print(k, len(marker_genes[k]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "marker_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ad.var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_annotation = sc.tl.marker_gene_overlap(ad, marker_genes, key='rank_genes_r0.5', top_n_markers=100)\n",
    "cell_annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cell_annotation_norm = sc.tl.marker_gene_overlap(ad, marker_genes, key='rank_genes_r0.5', normalize='reference', top_n_markers=200)\n",
    "sns.heatmap(cell_annotation_norm, cbar=False, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import colors\n",
    "#Define a nice colour map for gene expression\n",
    "colors_red = plt.cm.Reds(np.linspace(0, 1, 128))\n",
    "colors_blue = plt.cm.Blues(np.linspace(0, 1, 128))\n",
    "colors3 = plt.cm.Greys_r(np.linspace(0.7,0.8,20))\n",
    "colorsComb = np.vstack([colors3, colors_red])\n",
    "mymap_red = colors.LinearSegmentedColormap.from_list('my_colormap', colorsComb)\n",
    "colorsComb = np.vstack([colors3, colors_blue])\n",
    "mymap_blue = colors.LinearSegmentedColormap.from_list('my_colormap', colorsComb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ad.var_names_make_unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in marker_genes:\n",
    "    print(k)\n",
    "    sc.tl.score_genes(ad, list(marker_genes[k])) #  ctrl_size=50, gene_pool=None, n_bins=25, score_name='score', random_state=0, copy=False, use_raw=None)\n",
    "    ad.obs['score.' + k] = ad.obs['score']\n",
    "    del ad.obs['score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pl.umap(ad, color=['louvain_r0.5', 'condition.morse', 'sample'], cmap=mymap) # ['original_celltype_ann', 'n_counts', 'condition.morse']\n",
    "sc.pl.umap(morse, color=['score.mac.signature', 'score.mac.others'], cmap=mymap_blue) # ['original_celltype_ann', 'n_counts', 'condition.ad']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pl.umap(ad, color=marker_genes['mac.signature'], cmap=mymap) # ['original_celltype_ann', 'n_counts', 'condition.morse']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pl.umap(ad, color=marker_genes['mac.signature'], cmap=mymap) # ['original_celltype_ann', 'n_counts', 'condition.morse']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "ad.var.index = np.array(ad.var.symbol)\n",
    "ad.var_names_make_unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EGFL6           1\n",
       "RP11-815M8.1    1\n",
       "KCNC1           1\n",
       "SLC17A8         1\n",
       "ZNF549          1\n",
       "               ..\n",
       "MSANTD4         1\n",
       "LINC01558       1\n",
       "CTA-243E7.2     1\n",
       "XXYLT1          1\n",
       "GABARAP         1\n",
       "Length: 33694, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad.var.index.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MP.markers 8780\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/icb/ignacio.ibarra/miniconda3/envs/mypython3/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if not is_categorical(df_full[k]):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MP.others 8780\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/icb/ignacio.ibarra/miniconda3/envs/mypython3/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if not is_categorical(df_full[k]):\n"
     ]
    }
   ],
   "source": [
    "for k in ['MP.markers', 'MP.others']:\n",
    "    next_marker_genes = marker_genes[k]\n",
    "    print(k, len(ad))\n",
    "    sc.tl.score_genes(ad, next_marker_genes, use_raw=False, score_name=k + \".score\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\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>GSM</th>\n",
       "      <th>sample.id</th>\n",
       "      <th>dataset</th>\n",
       "      <th>last_author/PI</th>\n",
       "      <th>lung_vs_nasal</th>\n",
       "      <th>original_celltype_ann</th>\n",
       "      <th>sample</th>\n",
       "      <th>total_counts</th>\n",
       "      <th>log10_total_counts</th>\n",
       "      <th>n_genes_detected</th>\n",
       "      <th>...</th>\n",
       "      <th>n_counts</th>\n",
       "      <th>log_counts</th>\n",
       "      <th>n_genes</th>\n",
       "      <th>mt_frac</th>\n",
       "      <th>size_factors</th>\n",
       "      <th>louvain_r1</th>\n",
       "      <th>louvain_r0.5</th>\n",
       "      <th>condition.morse</th>\n",
       "      <th>MP.markers.score</th>\n",
       "      <th>MP.others.score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>barcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACCTGGTCATGCCG-1-SC45</th>\n",
       "      <td>GSM3660644</td>\n",
       "      <td>SC45NOR</td>\n",
       "      <td>Pittsburgh_Lafyatis_2019Morse_10Xv2</td>\n",
       "      <td>Lafyatis/Rojas</td>\n",
       "      <td>lung</td>\n",
       "      <td>1 SPP1hi Macrophage</td>\n",
       "      <td>SC45</td>\n",
       "      <td>4703.0</td>\n",
       "      <td>3.672375</td>\n",
       "      <td>1442</td>\n",
       "      <td>...</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>8.018954</td>\n",
       "      <td>1206</td>\n",
       "      <td>0.052337</td>\n",
       "      <td>0.326308</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>healthy</td>\n",
       "      <td>1.333640</td>\n",
       "      <td>0.169076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCTGGTGGTGTAG-1-SC45</th>\n",
       "      <td>GSM3660644</td>\n",
       "      <td>SC45NOR</td>\n",
       "      <td>Pittsburgh_Lafyatis_2019Morse_10Xv2</td>\n",
       "      <td>Lafyatis/Rojas</td>\n",
       "      <td>lung</td>\n",
       "      <td>1 SPP1hi Macrophage</td>\n",
       "      <td>SC45</td>\n",
       "      <td>5371.0</td>\n",
       "      <td>3.730055</td>\n",
       "      <td>1556</td>\n",
       "      <td>...</td>\n",
       "      <td>5557.0</td>\n",
       "      <td>8.622813</td>\n",
       "      <td>1574</td>\n",
       "      <td>0.045528</td>\n",
       "      <td>0.474200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>healthy</td>\n",
       "      <td>2.120104</td>\n",
       "      <td>0.475861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCTGGTTGTACAC-1-SC45</th>\n",
       "      <td>GSM3660644</td>\n",
       "      <td>SC45NOR</td>\n",
       "      <td>Pittsburgh_Lafyatis_2019Morse_10Xv2</td>\n",
       "      <td>Lafyatis/Rojas</td>\n",
       "      <td>lung</td>\n",
       "      <td>1 SPP1hi Macrophage</td>\n",
       "      <td>SC45</td>\n",
       "      <td>15027.0</td>\n",
       "      <td>4.176872</td>\n",
       "      <td>3025</td>\n",
       "      <td>...</td>\n",
       "      <td>4771.0</td>\n",
       "      <td>8.470311</td>\n",
       "      <td>1372</td>\n",
       "      <td>0.057850</td>\n",
       "      <td>0.374147</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>healthy</td>\n",
       "      <td>1.448630</td>\n",
       "      <td>0.335282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCTGTCACAACGT-1-SC45</th>\n",
       "      <td>GSM3660644</td>\n",
       "      <td>SC45NOR</td>\n",
       "      <td>Pittsburgh_Lafyatis_2019Morse_10Xv2</td>\n",
       "      <td>Lafyatis/Rojas</td>\n",
       "      <td>lung</td>\n",
       "      <td>1 SPP1hi Macrophage</td>\n",
       "      <td>SC45</td>\n",
       "      <td>7806.0</td>\n",
       "      <td>3.892429</td>\n",
       "      <td>2170</td>\n",
       "      <td>...</td>\n",
       "      <td>8826.0</td>\n",
       "      <td>9.085457</td>\n",
       "      <td>2104</td>\n",
       "      <td>0.046794</td>\n",
       "      <td>0.804564</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>healthy</td>\n",
       "      <td>1.992919</td>\n",
       "      <td>0.797149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACGGGCACAGCGTC-1-SC45</th>\n",
       "      <td>GSM3660644</td>\n",
       "      <td>SC45NOR</td>\n",
       "      <td>Pittsburgh_Lafyatis_2019Morse_10Xv2</td>\n",
       "      <td>Lafyatis/Rojas</td>\n",
       "      <td>lung</td>\n",
       "      <td>0 FABP4hi Macrophage</td>\n",
       "      <td>SC45</td>\n",
       "      <td>7709.0</td>\n",
       "      <td>3.886998</td>\n",
       "      <td>2263</td>\n",
       "      <td>...</td>\n",
       "      <td>5150.0</td>\n",
       "      <td>8.546752</td>\n",
       "      <td>1523</td>\n",
       "      <td>0.054757</td>\n",
       "      <td>0.432858</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>healthy</td>\n",
       "      <td>1.501712</td>\n",
       "      <td>0.297146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTCACAGGACCCT-1-SC156</th>\n",
       "      <td>GSM3660648</td>\n",
       "      <td>SC156NORUP</td>\n",
       "      <td>Pittsburgh_Lafyatis_2019Morse_10Xv2</td>\n",
       "      <td>Lafyatis/Rojas</td>\n",
       "      <td>lung</td>\n",
       "      <td>0 FABP4hi Macrophage</td>\n",
       "      <td>SC156</td>\n",
       "      <td>14965.0</td>\n",
       "      <td>4.175076</td>\n",
       "      <td>3164</td>\n",
       "      <td>...</td>\n",
       "      <td>6496.0</td>\n",
       "      <td>8.778942</td>\n",
       "      <td>1687</td>\n",
       "      <td>0.077278</td>\n",
       "      <td>0.580740</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>healthy</td>\n",
       "      <td>1.926309</td>\n",
       "      <td>0.225631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTCAGTACACCGC-1-SC156</th>\n",
       "      <td>GSM3660648</td>\n",
       "      <td>SC156NORUP</td>\n",
       "      <td>Pittsburgh_Lafyatis_2019Morse_10Xv2</td>\n",
       "      <td>Lafyatis/Rojas</td>\n",
       "      <td>lung</td>\n",
       "      <td>0 FABP4hi Macrophage</td>\n",
       "      <td>SC156</td>\n",
       "      <td>17738.0</td>\n",
       "      <td>4.248905</td>\n",
       "      <td>3267</td>\n",
       "      <td>...</td>\n",
       "      <td>6591.0</td>\n",
       "      <td>8.793461</td>\n",
       "      <td>1948</td>\n",
       "      <td>0.025338</td>\n",
       "      <td>0.674075</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>healthy</td>\n",
       "      <td>1.484474</td>\n",
       "      <td>0.252252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTCAGTGCCTGTG-1-SC156</th>\n",
       "      <td>GSM3660648</td>\n",
       "      <td>SC156NORUP</td>\n",
       "      <td>Pittsburgh_Lafyatis_2019Morse_10Xv2</td>\n",
       "      <td>Lafyatis/Rojas</td>\n",
       "      <td>lung</td>\n",
       "      <td>1 SPP1hi Macrophage</td>\n",
       "      <td>SC156</td>\n",
       "      <td>6310.0</td>\n",
       "      <td>3.800029</td>\n",
       "      <td>1927</td>\n",
       "      <td>...</td>\n",
       "      <td>33625.0</td>\n",
       "      <td>10.423025</td>\n",
       "      <td>4490</td>\n",
       "      <td>0.038275</td>\n",
       "      <td>2.828225</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>healthy</td>\n",
       "      <td>1.955700</td>\n",
       "      <td>0.777485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTCATCACCGTAA-1-SC156</th>\n",
       "      <td>GSM3660648</td>\n",
       "      <td>SC156NORUP</td>\n",
       "      <td>Pittsburgh_Lafyatis_2019Morse_10Xv2</td>\n",
       "      <td>Lafyatis/Rojas</td>\n",
       "      <td>lung</td>\n",
       "      <td>0 FABP4hi Macrophage</td>\n",
       "      <td>SC156</td>\n",
       "      <td>3291.0</td>\n",
       "      <td>3.517328</td>\n",
       "      <td>1049</td>\n",
       "      <td>...</td>\n",
       "      <td>17437.0</td>\n",
       "      <td>9.766350</td>\n",
       "      <td>3036</td>\n",
       "      <td>0.050181</td>\n",
       "      <td>1.450312</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>healthy</td>\n",
       "      <td>1.582551</td>\n",
       "      <td>0.601433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTCATCGCCTGAG-1-SC156</th>\n",
       "      <td>GSM3660648</td>\n",
       "      <td>SC156NORUP</td>\n",
       "      <td>Pittsburgh_Lafyatis_2019Morse_10Xv2</td>\n",
       "      <td>Lafyatis/Rojas</td>\n",
       "      <td>lung</td>\n",
       "      <td>0 FABP4hi Macrophage</td>\n",
       "      <td>SC156</td>\n",
       "      <td>20018.0</td>\n",
       "      <td>4.301421</td>\n",
       "      <td>3272</td>\n",
       "      <td>...</td>\n",
       "      <td>13400.0</td>\n",
       "      <td>9.503010</td>\n",
       "      <td>3075</td>\n",
       "      <td>0.051045</td>\n",
       "      <td>1.344328</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>healthy</td>\n",
       "      <td>1.899945</td>\n",
       "      <td>0.463083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8780 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 GSM   sample.id  \\\n",
       "barcode                                            \n",
       "AAACCTGGTCATGCCG-1-SC45   GSM3660644     SC45NOR   \n",
       "AAACCTGGTGGTGTAG-1-SC45   GSM3660644     SC45NOR   \n",
       "AAACCTGGTTGTACAC-1-SC45   GSM3660644     SC45NOR   \n",
       "AAACCTGTCACAACGT-1-SC45   GSM3660644     SC45NOR   \n",
       "AAACGGGCACAGCGTC-1-SC45   GSM3660644     SC45NOR   \n",
       "...                              ...         ...   \n",
       "TTTGTCACAGGACCCT-1-SC156  GSM3660648  SC156NORUP   \n",
       "TTTGTCAGTACACCGC-1-SC156  GSM3660648  SC156NORUP   \n",
       "TTTGTCAGTGCCTGTG-1-SC156  GSM3660648  SC156NORUP   \n",
       "TTTGTCATCACCGTAA-1-SC156  GSM3660648  SC156NORUP   \n",
       "TTTGTCATCGCCTGAG-1-SC156  GSM3660648  SC156NORUP   \n",
       "\n",
       "                                                      dataset  last_author/PI  \\\n",
       "barcode                                                                         \n",
       "AAACCTGGTCATGCCG-1-SC45   Pittsburgh_Lafyatis_2019Morse_10Xv2  Lafyatis/Rojas   \n",
       "AAACCTGGTGGTGTAG-1-SC45   Pittsburgh_Lafyatis_2019Morse_10Xv2  Lafyatis/Rojas   \n",
       "AAACCTGGTTGTACAC-1-SC45   Pittsburgh_Lafyatis_2019Morse_10Xv2  Lafyatis/Rojas   \n",
       "AAACCTGTCACAACGT-1-SC45   Pittsburgh_Lafyatis_2019Morse_10Xv2  Lafyatis/Rojas   \n",
       "AAACGGGCACAGCGTC-1-SC45   Pittsburgh_Lafyatis_2019Morse_10Xv2  Lafyatis/Rojas   \n",
       "...                                                       ...             ...   \n",
       "TTTGTCACAGGACCCT-1-SC156  Pittsburgh_Lafyatis_2019Morse_10Xv2  Lafyatis/Rojas   \n",
       "TTTGTCAGTACACCGC-1-SC156  Pittsburgh_Lafyatis_2019Morse_10Xv2  Lafyatis/Rojas   \n",
       "TTTGTCAGTGCCTGTG-1-SC156  Pittsburgh_Lafyatis_2019Morse_10Xv2  Lafyatis/Rojas   \n",
       "TTTGTCATCACCGTAA-1-SC156  Pittsburgh_Lafyatis_2019Morse_10Xv2  Lafyatis/Rojas   \n",
       "TTTGTCATCGCCTGAG-1-SC156  Pittsburgh_Lafyatis_2019Morse_10Xv2  Lafyatis/Rojas   \n",
       "\n",
       "                         lung_vs_nasal original_celltype_ann sample  \\\n",
       "barcode                                                               \n",
       "AAACCTGGTCATGCCG-1-SC45           lung   1 SPP1hi Macrophage   SC45   \n",
       "AAACCTGGTGGTGTAG-1-SC45           lung   1 SPP1hi Macrophage   SC45   \n",
       "AAACCTGGTTGTACAC-1-SC45           lung   1 SPP1hi Macrophage   SC45   \n",
       "AAACCTGTCACAACGT-1-SC45           lung   1 SPP1hi Macrophage   SC45   \n",
       "AAACGGGCACAGCGTC-1-SC45           lung  0 FABP4hi Macrophage   SC45   \n",
       "...                                ...                   ...    ...   \n",
       "TTTGTCACAGGACCCT-1-SC156          lung  0 FABP4hi Macrophage  SC156   \n",
       "TTTGTCAGTACACCGC-1-SC156          lung  0 FABP4hi Macrophage  SC156   \n",
       "TTTGTCAGTGCCTGTG-1-SC156          lung   1 SPP1hi Macrophage  SC156   \n",
       "TTTGTCATCACCGTAA-1-SC156          lung  0 FABP4hi Macrophage  SC156   \n",
       "TTTGTCATCGCCTGAG-1-SC156          lung  0 FABP4hi Macrophage  SC156   \n",
       "\n",
       "                          total_counts  log10_total_counts  n_genes_detected  \\\n",
       "barcode                                                                        \n",
       "AAACCTGGTCATGCCG-1-SC45         4703.0            3.672375              1442   \n",
       "AAACCTGGTGGTGTAG-1-SC45         5371.0            3.730055              1556   \n",
       "AAACCTGGTTGTACAC-1-SC45        15027.0            4.176872              3025   \n",
       "AAACCTGTCACAACGT-1-SC45         7806.0            3.892429              2170   \n",
       "AAACGGGCACAGCGTC-1-SC45         7709.0            3.886998              2263   \n",
       "...                                ...                 ...               ...   \n",
       "TTTGTCACAGGACCCT-1-SC156       14965.0            4.175076              3164   \n",
       "TTTGTCAGTACACCGC-1-SC156       17738.0            4.248905              3267   \n",
       "TTTGTCAGTGCCTGTG-1-SC156        6310.0            3.800029              1927   \n",
       "TTTGTCATCACCGTAA-1-SC156        3291.0            3.517328              1049   \n",
       "TTTGTCATCGCCTGAG-1-SC156       20018.0            4.301421              3272   \n",
       "\n",
       "                          ...  n_counts  log_counts  n_genes   mt_frac  \\\n",
       "barcode                   ...                                            \n",
       "AAACCTGGTCATGCCG-1-SC45   ...    3038.0    8.018954     1206  0.052337   \n",
       "AAACCTGGTGGTGTAG-1-SC45   ...    5557.0    8.622813     1574  0.045528   \n",
       "AAACCTGGTTGTACAC-1-SC45   ...    4771.0    8.470311     1372  0.057850   \n",
       "AAACCTGTCACAACGT-1-SC45   ...    8826.0    9.085457     2104  0.046794   \n",
       "AAACGGGCACAGCGTC-1-SC45   ...    5150.0    8.546752     1523  0.054757   \n",
       "...                       ...       ...         ...      ...       ...   \n",
       "TTTGTCACAGGACCCT-1-SC156  ...    6496.0    8.778942     1687  0.077278   \n",
       "TTTGTCAGTACACCGC-1-SC156  ...    6591.0    8.793461     1948  0.025338   \n",
       "TTTGTCAGTGCCTGTG-1-SC156  ...   33625.0   10.423025     4490  0.038275   \n",
       "TTTGTCATCACCGTAA-1-SC156  ...   17437.0    9.766350     3036  0.050181   \n",
       "TTTGTCATCGCCTGAG-1-SC156  ...   13400.0    9.503010     3075  0.051045   \n",
       "\n",
       "                         size_factors louvain_r1 louvain_r0.5 condition.morse  \\\n",
       "barcode                                                                         \n",
       "AAACCTGGTCATGCCG-1-SC45      0.326308          0            1         healthy   \n",
       "AAACCTGGTGGTGTAG-1-SC45      0.474200          0            1         healthy   \n",
       "AAACCTGGTTGTACAC-1-SC45      0.374147          8            1         healthy   \n",
       "AAACCTGTCACAACGT-1-SC45      0.804564          8            1         healthy   \n",
       "AAACGGGCACAGCGTC-1-SC45      0.432858          8            1         healthy   \n",
       "...                               ...        ...          ...             ...   \n",
       "TTTGTCACAGGACCCT-1-SC156     0.580740          4            0         healthy   \n",
       "TTTGTCAGTACACCGC-1-SC156     0.674075          4            0         healthy   \n",
       "TTTGTCAGTGCCTGTG-1-SC156     2.828225          5            0         healthy   \n",
       "TTTGTCATCACCGTAA-1-SC156     1.450312          5            0         healthy   \n",
       "TTTGTCATCGCCTGAG-1-SC156     1.344328          9            5         healthy   \n",
       "\n",
       "                          MP.markers.score  MP.others.score  \n",
       "barcode                                                      \n",
       "AAACCTGGTCATGCCG-1-SC45           1.333640         0.169076  \n",
       "AAACCTGGTGGTGTAG-1-SC45           2.120104         0.475861  \n",
       "AAACCTGGTTGTACAC-1-SC45           1.448630         0.335282  \n",
       "AAACCTGTCACAACGT-1-SC45           1.992919         0.797149  \n",
       "AAACGGGCACAGCGTC-1-SC45           1.501712         0.297146  \n",
       "...                                    ...              ...  \n",
       "TTTGTCACAGGACCCT-1-SC156          1.926309         0.225631  \n",
       "TTTGTCAGTACACCGC-1-SC156          1.484474         0.252252  \n",
       "TTTGTCAGTGCCTGTG-1-SC156          1.955700         0.777485  \n",
       "TTTGTCATCACCGTAA-1-SC156          1.582551         0.601433  \n",
       "TTTGTCATCGCCTGAG-1-SC156          1.899945         0.463083  \n",
       "\n",
       "[8780 rows x 76 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "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>GSM</th>\n",
       "      <th>sample.id</th>\n",
       "      <th>n_counts</th>\n",
       "      <th>log_counts</th>\n",
       "      <th>n_genes</th>\n",
       "      <th>mt_frac</th>\n",
       "      <th>cell.type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>barcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACCTGAGAGCTGGT-1</th>\n",
       "      <td>GSM3660651</td>\n",
       "      <td>SC87IPFLOW</td>\n",
       "      <td>18523.076172</td>\n",
       "      <td>9.826773</td>\n",
       "      <td>4109</td>\n",
       "      <td>0.005121</td>\n",
       "      <td>1-SPP1hi Macrophage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCTGCATACGCCG-1</th>\n",
       "      <td>GSM3660651</td>\n",
       "      <td>SC87IPFLOW</td>\n",
       "      <td>11477.095703</td>\n",
       "      <td>9.348108</td>\n",
       "      <td>2051</td>\n",
       "      <td>0.006989</td>\n",
       "      <td>5-Fibroblast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCTGGTCCGAGTC-1</th>\n",
       "      <td>GSM3660651</td>\n",
       "      <td>SC87IPFLOW</td>\n",
       "      <td>12274.981445</td>\n",
       "      <td>9.415318</td>\n",
       "      <td>2516</td>\n",
       "      <td>0.007603</td>\n",
       "      <td>9-Goblet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCTGGTGTCGCTG-1</th>\n",
       "      <td>GSM3660651</td>\n",
       "      <td>SC87IPFLOW</td>\n",
       "      <td>13840.004883</td>\n",
       "      <td>9.535318</td>\n",
       "      <td>2786</td>\n",
       "      <td>0.006455</td>\n",
       "      <td>1-SPP1hi Macrophage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCTGGTTCTGAAC-1</th>\n",
       "      <td>GSM3660651</td>\n",
       "      <td>SC87IPFLOW</td>\n",
       "      <td>11685.170898</td>\n",
       "      <td>9.366076</td>\n",
       "      <td>2174</td>\n",
       "      <td>0.007381</td>\n",
       "      <td>1-SPP1hi Macrophage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTCACAGAGTGTG-1</th>\n",
       "      <td>GSM3660651</td>\n",
       "      <td>SC87IPFLOW</td>\n",
       "      <td>14617.307617</td>\n",
       "      <td>9.589962</td>\n",
       "      <td>2850</td>\n",
       "      <td>0.006189</td>\n",
       "      <td>9-Goblet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTCACAGGAATGC-1</th>\n",
       "      <td>GSM3660651</td>\n",
       "      <td>SC87IPFLOW</td>\n",
       "      <td>17131.289062</td>\n",
       "      <td>9.748662</td>\n",
       "      <td>3782</td>\n",
       "      <td>0.004736</td>\n",
       "      <td>5-Fibroblast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTCACATCGGAAG-1</th>\n",
       "      <td>GSM3660651</td>\n",
       "      <td>SC87IPFLOW</td>\n",
       "      <td>8669.977539</td>\n",
       "      <td>9.067621</td>\n",
       "      <td>1472</td>\n",
       "      <td>0.009714</td>\n",
       "      <td>1-SPP1hi Macrophage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTCAGTAAAGTCA-1</th>\n",
       "      <td>GSM3660651</td>\n",
       "      <td>SC87IPFLOW</td>\n",
       "      <td>11158.509766</td>\n",
       "      <td>9.319958</td>\n",
       "      <td>2124</td>\n",
       "      <td>0.007380</td>\n",
       "      <td>16-Dendritic Cell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTCAGTCCAGTTA-1</th>\n",
       "      <td>GSM3660651</td>\n",
       "      <td>SC87IPFLOW</td>\n",
       "      <td>6562.387695</td>\n",
       "      <td>8.789110</td>\n",
       "      <td>991</td>\n",
       "      <td>0.011364</td>\n",
       "      <td>17-Lymphatic Endothelial</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4299 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           GSM   sample.id      n_counts  log_counts  n_genes  \\\n",
       "barcode                                                                         \n",
       "AAACCTGAGAGCTGGT-1  GSM3660651  SC87IPFLOW  18523.076172    9.826773     4109   \n",
       "AAACCTGCATACGCCG-1  GSM3660651  SC87IPFLOW  11477.095703    9.348108     2051   \n",
       "AAACCTGGTCCGAGTC-1  GSM3660651  SC87IPFLOW  12274.981445    9.415318     2516   \n",
       "AAACCTGGTGTCGCTG-1  GSM3660651  SC87IPFLOW  13840.004883    9.535318     2786   \n",
       "AAACCTGGTTCTGAAC-1  GSM3660651  SC87IPFLOW  11685.170898    9.366076     2174   \n",
       "...                        ...         ...           ...         ...      ...   \n",
       "TTTGTCACAGAGTGTG-1  GSM3660651  SC87IPFLOW  14617.307617    9.589962     2850   \n",
       "TTTGTCACAGGAATGC-1  GSM3660651  SC87IPFLOW  17131.289062    9.748662     3782   \n",
       "TTTGTCACATCGGAAG-1  GSM3660651  SC87IPFLOW   8669.977539    9.067621     1472   \n",
       "TTTGTCAGTAAAGTCA-1  GSM3660651  SC87IPFLOW  11158.509766    9.319958     2124   \n",
       "TTTGTCAGTCCAGTTA-1  GSM3660651  SC87IPFLOW   6562.387695    8.789110      991   \n",
       "\n",
       "                     mt_frac                 cell.type  \n",
       "barcode                                                 \n",
       "AAACCTGAGAGCTGGT-1  0.005121       1-SPP1hi Macrophage  \n",
       "AAACCTGCATACGCCG-1  0.006989              5-Fibroblast  \n",
       "AAACCTGGTCCGAGTC-1  0.007603                  9-Goblet  \n",
       "AAACCTGGTGTCGCTG-1  0.006455       1-SPP1hi Macrophage  \n",
       "AAACCTGGTTCTGAAC-1  0.007381       1-SPP1hi Macrophage  \n",
       "...                      ...                       ...  \n",
       "TTTGTCACAGAGTGTG-1  0.006189                  9-Goblet  \n",
       "TTTGTCACAGGAATGC-1  0.004736              5-Fibroblast  \n",
       "TTTGTCACATCGGAAG-1  0.009714       1-SPP1hi Macrophage  \n",
       "TTTGTCAGTAAAGTCA-1  0.007380         16-Dendritic Cell  \n",
       "TTTGTCAGTCCAGTTA-1  0.011364  17-Lymphatic Endothelial  \n",
       "\n",
       "[4299 rows x 7 columns]"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 FABP4hi Macrophage\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/icb/ignacio.ibarra/miniconda3/envs/mypython3/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if not is_categorical(df_full[k]):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1 SPP1hi Macrophage\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/icb/ignacio.ibarra/miniconda3/envs/mypython3/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if not is_categorical(df_full[k]):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x1700 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = [13, 17]\n",
    "rcParams['figure.dpi'] = 100\n",
    "pi = 0\n",
    "for ct in sorted(set(ad.obs['original_celltype_ann']), key=lambda x: ad.obs[ad.obs['original_celltype_ann'] == x].shape[0])[::-1]:\n",
    "    print(pi, ct)\n",
    "    \n",
    "    grp = ad[ad.obs['original_celltype_ann'] == ct].obs\n",
    "    \n",
    "    ax = plt.subplot(10, 3, pi + 1)\n",
    "    sns.violinplot(x=\"original_celltype_ann\", y=\"MP.markers.score\", hue='sample',\n",
    "                   data=grp, palette=\"muted\")\n",
    "    plt.title(ct + '(%i)' % grp.shape[0])\n",
    "    \n",
    "    if pi != 1:\n",
    "        ax.get_legend().set_visible(False)\n",
    "    else:\n",
    "        plt.legend(bbox_to_anchor=(1.01, 1),borderaxespad=0)\n",
    "    pi += 1\n",
    "    # Hide the right and top spines\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "\n",
    "    # Only show ticks on the left and bottom spines\n",
    "    ax.yaxis.set_ticks_position('left')\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    \n",
    "    plt.xlabel('')\n",
    "    plt.xticks([])\n",
    "\n",
    "# plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(right=.6, bottom=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 FABP4hi Macrophage\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/icb/ignacio.ibarra/miniconda3/envs/mypython3/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if not is_categorical(df_full[k]):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1 SPP1hi Macrophage\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/icb/ignacio.ibarra/miniconda3/envs/mypython3/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if not is_categorical(df_full[k]):\n"
     ]
    },
    {
     "data": {
      "image/png": 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I+RrAIkJIR911SgADwJkYNjUwzjLo/DYIIe+AM29PBOczNY/Wjl7zB7CPEPIZuB+WZeC21y0mD26AQ+B2V3cRQraCWwW/oZu/AUrpFV305OuEEA24aK7e4Fah5TDdsVoMLq3BKULIRnCCyhWcE/gjAF6ilGZRSlWEkNPgBJ3xWFbfL5RSLSHkNXBmqAOEkI/B3a9vgzPTrDK6fiu4BJiJ4Mw0fuCiO58C8J7e1Kn7UToC7vObDc6kYjzHQp0/lzGDAVxs4EfI4RBCHgb3ej11h3oRQvSJaH9qwMx4hRDyGzil4ia4z2gQuM80H5zZ0ykQQtyg85nFnc9/GCHED0AVpfSw5SudA5MHBlq1PNAd7wVu9w3gdhTdjL4TVymlV3W7hCbX6a6dAkBjJmMyCSF7AfwfIUQJzj9VBG4TJQbA2xZ82ppFHrRL7Ik2sOcBLnrlQ3BfeCW46LQJVl67FHUkswVwAZzAdann+tPgtuhdYDm6Uw7ui/UBgACza4/DLHmlDa85AzYks4VZ9BrsiOZqoF2taC5wGdjTwQm9q+B2HmuNC84/YjY407USXJLDU+DMBnW+XqNzfcCFx5fprr8IYIpZm/t0r2OS7l4p0M3rBIBos7Y7YDkpo6W5TwWX3kAB7sf6TXA+NhRAqFE7EbjQ/XzdPaEXqGUA1pv16aebYxo4ZakYnGlgBQB3o3bTwK3cA+25X4zOPQZO+ZLr5nMAuqgxs9d5Aty9XgNuBXwctZPT6u/Buh7m94gHuJX0HHu+B4156N6TuuYZ2sC1/wfgB9xJNKsEt0D8GEAna997s3bHYYU8aOA9zrDQjskDJg+skgeoO9uByf1SR591vU+u4JTVJHALvWLd650Io8haXdtmkwft8aEPy2Uwmh3C1TY8Bi6M/vvmnQ0HIWQIOFP6REqpNaZF8+tdwTnxv08ptTYHX4uCcLmTPgSXMoGtnBlNApMHLRMmD5qWpvJJYzBaPISQkeDM3ufArZyjwK2yU2GjCVwPpVRBCFkCYCkh5CNKqblvR4tG5yg9H8AqJpAZ7QkmD2rD5EHTw5Q0BuMOMnBVK2aD84Eqgs5JmtYO7beFreDqQIaBMw+1JjoD+B84sw+D0Z5g8qA2TB40MczcyWAwGAwGg9ECsbviACHkXkLI/whXgDdYd+xZQshQx02PwWC0VpiMYDAYjMZhl5JGuHITR8DZ6QeAi4IBuC3hhY6ZGoPBaK0wGcFgMBiNx96dtEXgcr+8ANNSGKdgoUwNg8FodzAZwWAwGI3E3sCBcNzJyGyMDJxDZJOgy4jsCaCCMuc6BqMlwWQEg9FKOHfunCe4Ul12u0AxrEYLIDc6OrrCmsb2Kmm5ALqDS7ZnzFAYlZ5pAjwBlJeXmyeoZjDaPaSZx2cygsFouRAAOHfuHA/AQj6f/xwhRIjmlxvtAUoprTl37txXAN6Njo6ut/6yvUrapwA+JIToszQHEUL+A2AdgHfs7JPBYLQdmIxgMFo+C4VC4csBAQEqd3f3akII2212MpRSUlVV5ZaXl/dyTU0NwFWlqBO7U3AQQlaCq4XpqjukBLCOUvq2XR3aNwcJdKtkiUTSVMMyGK2BZl8RMxnBYLRYyLlz5yR8Pv+foKAgob+/f3FzT6i9UVBQ4JuTk1Oj0Wii6zN92ryTRgjhgzNZvA9gJbhCrzxwhV0r7Z0wg+FM1Go1VCoV3NzcmnsqbR4mI5qPiooKeHp6NtyQwQACCCFCd3f36uaeSHtEt3PpBs4XsE4lzWYnQUqpBlxovZRSWk0p/YdSmsiEL6MlM2vWLIwfPx6Vlew2dTZMRjQPR44cwSOPPIKDBw8291QYrQMeuNgaZuJsBnTvO0EDepi9kRz/gitpwWC0Cv79919UVFTg4sWLzT2V9gKTEU3MmjVcve61a9c280wYDIajsDdw4C0A6wghb4MrPmtSJJZSKmvsxBgMRquGyYgmRueEzGA0Go1GA61W22R+rTwej/L5/KYarlVhr5L2s+7vj+Ait/QQ3XP2bjNaJCxVVpPBZASD0QrRaDQY89jYKFl5qb36gc1IpN7qHw/sS7JFUcvOzhbMnTs3+NixY5Li4mKhRCLRREREVC9dujRnxIgRVQBw8uRJ8YoVKwITExM9Kysr+YGBgarBgwdXLFy4MK9fv35KAJg6dWrnxMREj9TUVHFYWJgiOTn5al1jXr58WTRo0KBePB6PVlRUXGzs67YGez+E4Q6dBYPBaGswGcFgtEK0Wi2RlZcKgmPfBhcD5Fwo1SD7xHKBVqslfD7f6lX0mDFjuqnVarJ169aM8PBwZXZ2tuDo0aOSoqIiAQDs3r1bOnny5G5Dhw6Vbdu2LS0iIkKZk5Mj3LNnj/eCBQuCDx06lMaNTzFp0qSixMRE92vXrtUZWaZUKsmECRPC7rrrrorz5897NP6VW4ddShql9A9HT4TBaAq4BPQMZ8NkBIPRuiGED8Jrgg3velO5WqaoqIh//vx5j4SEhOuPPvpoJQD07NlTNXz48GoAqKio4E2fPj102LBh5b/88stN/XURERGquLi4qqKiIsML27Fjx20AmDNnjrA+JW327NlB3bt3V8TFxcmaUkmzuwQEIcSLEPI6IeRzQshnhJDXCCFSR06OwWC0XpiMYDAYzkAqlWrc3Ny0e/fu9ZbL5bVW3vv27ZOUlZUJ5s+fn2fpej8/P40t4/3444+eBw8e9Pniiy9u2Ttne7FLSSOE3AXgJrhElT4A/ADMAXCTEMKKJzMY7RwmI+7wzz//YO/evcwfksFwEEKhEJs3b07//vvvfb29vQcMHDgwYsaMGcFnzpwRA0BKSooIAKKiohSNHSsvL4//4osvhn722WfpPj4+duz7NQ57d9I+AOcQHEopfZxSOhZAVwAJADY4aG4MRqujsrISv/zyC6qr231+SCYjdLz22mv44IMPWPoXBsOBTJkypSw3Nzdp9+7dN+Li4spPnjzpGRMT02vjxo2+lFKH+bU899xzoWPHji15+OGHmyXPo71K2l0A1lBK1foDuv/X6s61G5RKJU6cOIHS0tLmngqjBfDZZ5/hnXfewfbt25t7Ks0NkxFm5ObmNvcUGIw2hZubGx07dqxs3bp1uRcuXEgeN25c0erVq4PCw8MVAJCUlOTaUB8Ncfr0ac+tW7cGCASCaIFAEP3aa6+FVlZW8gUCQfSGDRt8G/8q6sdeJU0GIMTC8c6op7xBW+Tbb7/FW2+9hXfffbe5p+JQlEplc0+hVbJ3714AwJ49e5p5Js0OkxHgypExGIymITIyUiGXy3nx8fEyLy8v9Zo1awIstTMOHGiIP/74I/n06dNX9I833ngj293dXXv69OkrEydOLHPY5OvA3hQc3wDYRgh5A8ApcHmPhgJ4D8BuB82tVfDTTz8BAP7+++9mnonj+Prrr7F9+3Z8+OGH6Nu3b3NPh9E6YTICYGZvBsMJ5OXl8ePj47s999xzRdHR0XKpVKo5deqU+0cffRQwcuTIMolEot20adOtqVOnhsXFxXWfNWtWfmRkpDI/P1+wa9cun6ysLJeEhIQ0gMt9JpPJeHl5eQKFQsE7deqUGAAGDhyocHV1pQMHDjTxazt9+nQNIYTefffdjfZ3swZ7lbQ3wAndr4z6qAHwMYA3HTCvVoNGY1OQSKPYuHEj+Hw+XnnlFaeOs3XrVgDAp59+io8++sipYzHaLExGAKiqulNoge1OM1oTlGrsSo9h1zg2IpVKtdHR0VWbN2/umJmZKVKr1SQgIEA1ceLEwpUrV+YCwKRJk8pCQkKSV65cGTht2rSwqqoqfkBAgComJqZi9erV2fq+pk2bFnr27FlDSo2YmJheAJCcnPxveHi4ygEvsVHYmydNBeBVQsgCAN3AZRG/QSltd8tG41IsJSUl8PHxcco4paWl+O677wAAkydPhoeH89O0GP/AtBVYhF3TwGQEh/F3qC1+nxhtDx6PRyVSb3X2ieVNWnGAx+NZLZzFYjHdvHlzNoDs+trFxsZWx8bG3qyvTWJi4nVrxwWAWbNmFc+aNavYlmsag10fgi7XEZ9SWgKukLL+uA8AdXupy1dcXIyioiLD86SkJAwf7pxE68a+LU21e9cWFRqWzLZpYDKCw1gxq6xsluAwBsMm+Hw+fjywL4nV7mwZ2Bs4sAfABAvHn9Sdaxf89ddf9T53JFrtnX3npiqk3BQKTVFRkdOdq9uistkKYDICpkqaXC5vxpkwGNbD5/MhFAppUz2YglY39ippgwAcs3D8uO6cVRBCYgkhBwkhOYQQSgiJt3M+zcKhQ4dMnp84ccJpq2VjxayplDRnKzc3btzA448/jhUrVjh1HOP3iylsTYZDZERrx1gxY0EEDAbDVuxV0kSwbCoVAhDb0I87gCQAM+ycR7Nx7do1XLt2zfCcL/aDQqFAQkKCU8ZrDiXN2Ttp+izsv/32m1PHMd6pY+bOJsNRMqJVo1DcCQBjgQO2k5SUhE8//RQqVbP7bzMYzYK9StpZAP9n4fhLAM5Z2wml9DCldBGldK+d82g2du7cafLcsxO3OfDdd985RaAY99lUSpqzMTbhOpOmjMBlGHCIjGjtGCtpzNxpO2+++Sb+97//1bJaMBjtBXujN94C8CshJAqAfhvkfgB3A3jAEROzBCFEBG6FrsfTWWPVx40bN/DHH3+YHHPz74uKWydQUFCAQ4cOYezYsQ4d03g3yJk+XMbmwKZSopoSZu5sMppFRrQ0jH3SmLnTdvTuI9ev2xSAx2C0GezaSaOUngTwHwC3wTkCjwZwA0A/SumfjpteLRYAKDd6ZDlxrDr5/PPPAQCuHXoZjhG+EJ5dYgEAX331lcNNG8Y7ac40mxiv/NuKksZMnE1PM8qIFoVMdieItby8vBln4ljUajVb8DAYTYC95k5QSi9SSidSSntTSu+ilE6jlKY6cnIWWAVAavTo5OTxanHx4kWcPHkSIDxIQoaZnPMIugt8Vy8UFRXhm2++cei4FRUVFv93NG3RNMiUtOahmWREi6KwsNDwf0FBQZtQbORyOZ566im8+Wa7yUnMYDQbdilphJCBhJC+Rs8fI4TsJ4S8Swhxcdz0TKGUKimlMv0DTVwDUK1W44MPPgAAuAdGQ+juZ3Ke8ASQho0AwJVWys/Pd9jYBQUFFv93NMZ539qKo3NT/TC2hR9gR+EoGdHaI8AzMjIM/8vlchQXN1kOTKdx/vx5FBQU4NSpU809FQajzWPvTtqnAHoCACEkDFydvmoATwBY65iptTx27dqFtLQ08ARiSLveb7GNm38/uEi7QKFQ4L333nPYD3dqaqrF/x2N8Y9Kfn6+ifnT0Ri/N85UcJqqyDVzDDfBUTKi1UaAV1dXIz09w+TY1atXnTJWUy4Q2oobRHORnp6OmzfrTYLf7Gg0GtTU1JCmerRFC46jsDdwoCeAi7r/nwDwB6X0GUJIDLhElbOt6YQQ4gGgu9GhroSQ/gBKKKWZds7NKVy7dg1ffPEFAMCrxyPgu7hDq6kdxUkIgU/4GOT/8zHOnDmDvXv3Yty4cY0aW6vV4uzZs4bn//zzDyilTjHjnT9/3mTcixcvYvDgwQ4fh1JqogCWlZXB29vb4eMApn52zgzlz8nJMXleUFAAf39/p43XwnGIjKCUHgZwGGh9Zuvz58+DUlOFJjExEbGxsQ4fy5mLKWMopSY77EqlEiKRqJ4rGMYolUpMmTIFWq0WR48ehVjc8rLRaDQajH1sTFRpuazJykJ5SyXqfQd+TLIlqW12drZg7ty5wceOHZMUFxcLJRKJJiIionrp0qU5I0aMqAKAkydPilesWBGYmJjoWVlZyQ8MDFQNHjy4YuHChXn9+vUzMRXl5eXxo6KiehcUFAgLCwsv+vn5aQDg+vXrLhEREX3Nx//uu+9Sx48f7/TKKfZ+CAR3duFGANAnB7sNwM/iFZa5C6YJL9fr/n4JYIqdc3M4MpkMixcvhkajgbhDb4g79IFaXgqt9k4qDLWiDDyeEHyRBEJ3f0i7PYCy1J+wefNm9OrVC5GRkXaPf+HCBRPfltzcXFy/fh0RERGNel3mqFQqHD9+3OTY4cOHnaKkKRQK/P7774bnN2/exF133eXwcQBTHz5jR25Hc/nyZZPnly5dwogRI5w2XgvHUTLCtkFbSAQ4APzyyy+1jh0/fhyzZs2Ci4vTvEKcikKhwLJlywzPZTIZOnTo0Iwzal3IZDLDTmR5eXmLVNK0Wi0pLZcJtjzaFXye8xdGGi3F9EPpAq1WS/h8vtVbwmPGjOmmVqvJ1q1bM8LDw5XZ2dmCo0ePSoqKigQAsHv3bunkyZO7DR06VLZt27a0iIgIZU5OjnDPnj3eCxYsCD506FCacX+TJk0KjYiIqC4oKJBaGm///v0pAwYMMJhL/P39m2T7z14l7R8AiwghvwIYBuBl3fGuAKx2xKKUHgcnzFssGo0Gy5YtQ15eHgRiH/hExEOrqkDu3+tN2uUnbgIABA6eA4HYGx7Bg6EsTYe86BoWLVqEzz//3O6dol9//dXwf3SgO87lVuHIkSMOV9IOHTqE0tJSk2PHjx/HrVu30KVLF4eOZZ6O4OLFi05T0nJzcw3/O9Of78KFCybPz507156VNIfICDtYAGCJE/u3ipycnFoLHp6LJ8rLy/Hzzz9jzJgxzTMxB5Ofn98mlDRKKTIyMtC5c2cIBM7bQDJfMAYEBDhtrMbC5xEImkBJs4eioiL++fPnPRISEq4/+uijlQDQs2dP1fDhw6sBoKKigjd9+vTQYcOGlf/yyy8G23JERIQqLi6uqqioyGTLbs2aNR1kMplg0aJFOSdOnLCopHXo0EEdEhLSNL4zRtjrkzYbwEAAHwFYSSm9oTs+HkCb8ib96quvkJiYCMITwrfP0+AJXK26jhACn8jHIRD7oqCgAMuXL7fLl6OmpsYkJ9ugTu4AOMXNkb5W1dXV2LFjh8kxr55SaLVafPrppw4bR8+ZM2dMnv/5559O86sxVtLMTZKOglJaS0n7559/nDJWK2E2mkdGNHsEOAB8+umn0Gq1cPHqajjm2Ynbkd6+fbvDc6aZy4Km8o809mF1Js72uTtx4gSee+45bNiwwanjGAdmtYUgkuZCKpVq3NzctHv37vWWy+W1NMl9+/ZJysrKBPPnz8+zdL3elAkA586dc123bl3gzp0703m8ulWicePGdffx8YkaOHBgxPbt253jm2MBe/OkXaKU9qWUSimly4xOzQUw2TFTa36uXbtmUFy8w0fDxcO2VQ9P4ArfPk+D8IQ4e/Ys9u61vbDCP//8Y7L6ivBzg6cLD2VlZSb+Y41l586dKCkpgcjnjhmmU1wwwOMUKHMFpLH89NNPhv/5ANLS0pzmVH3lyhWT/50h8DMyMmrtQubl5SE7O9vhY7UGmktGNHcEOAAcO3ZMZ8on8DIKMHIPjIZA7IOioiJs3LjRoWMau0MAcGhkeX0Yf7ccjbH/qLODf7766isAwIEDB5w6jvEi0VkLxvaAUCjE5s2b07///ntfb2/vAQMHDoyYMWNG8JkzZ8QAkJKSIgKAqKioep015XI5eeaZZ8KWLVuW1aNHD4sOyxKJRLt06dLbu3bturl///7U2NhY2fPPPx+2ZcsWH8e/strYnSfNEpRSBaW0TdQsopRi48aN0Gq1cPPvC/eAAXb14+LREdLuDwLgkuDa6hN17JhpjWoBj2BgkAcA1DKn2EthYaEhr1vI/Xc2Htw6uKLjXZwp4+OPP3aYcnPlyhUkJSUZnvfTmYH/97//OaR/Y6qqqnD69GnD8/z8fKcog+YVKLxcucXdiRMnHD5Wa6YtyQhLpKenY/Xq1QAAz5B7IfQMNJwjfCG8w+MBEBw6dMihCkFWlmle78xM58RdmS9EHLlQNMe4yoCzd52aKrrQ+P0zfy8ZtjFlypSy3NzcpN27d9+Ii4srP3nypGdMTEyvjRs3+lJKrbLTzpw5M7hHjx6K6dOnl9TVJjAwUL1kyZKC4cOHV8fGxlZv2LAhZ9KkSQUbNmxoElu1Q5U0QsiXhJDfG27Z8rl27RouX74MwhPAq/vDjerLI+huCN07oqqqymQHqSFUKhX+/LN2cva7AjmT54kTJxyywty2bRuUSiU8QzzgFW5qju88PBg8Fx6uXbtm4uhvL5RSfPzxxybHYjt2BAHw119/4dKlS40ew5h9+/bVinwzr7vaWORyOfbt22dyLMCT82v54Ycf2ky+OUdgq4wghHgQQvrror4BXQQ4ISTEOTO0n7y8PLz++uuorq6GyCsU0q5xtdq4enc1HF+/fr3F77c9pKWZ+EA7LcWDiQmfx+0G3b592ylj/fvvv4b/r1271mTl8JyJcdWJtlSBorlwc3OjY8eOla1bty73woULyePGjStavXp1UHh4uAIAkpKS6vVP+uuvvySHDx/2FggE0QKBIPqxxx7rCQABAQH9X3vttaC6rhs8eHBVZmamdb5PjcShShqAbAC3HNxns5CYmAgAcPWLAF/UuAAxQnhwD7rLpF9r+OOPP1BZWQkvkenHFO4nhlTER3l5eaN3aq5du2ZQHLs82LlWmgOhhxBBQ7kFw5YtWxrtS3Pw4EEkJSVBYDSOv0iEe3x9AQBr1651WDqBsrIyiwrZn3/+6VBlcNu2bSgpKYGrkb9xgCcfIgFBfn6+wZTCAGC7jLgLwAXdA+AiwC8AeMfB82oU+fn5ePXVV1FYWAiBm5/OzcFyOgHPLsPgHjAQWq0WS5Ys4SqYNJLk5GST585yHTCWX56duR19451qZ41VXV3tNNMqpdRkJ82ZCltJSYnF/xmOITIyUiGXy3nx8fEyLy8v9Zo1ayzudukDB3744Yebf//995XTp09fOX369JX169dnAMDPP/+c/MYbb9QZZXbhwgU3Pz+/JrEIONrcuZBSOtWRfTYXep8OoXtHh/Sn78daXxG1Wm34cR/aRWJyjs8jiNUd++qrr+zeqlcqlVi1ahUopfCL8oVniIfFdkExARB5uaCgoABbtmyxayyAS0nxoc4x936zqKaHg4PhKRDg1q1bWLVqlUMSZm7btg2VlZWQiu9kfOiqq7f64YcfOmSMc+fO4dtvvwUA9PS748/H4xFEdBAC4My4jt4hbK3YKiMopccppcTCY4oTp2kTOTk5mDFjBnJyciAQ+6BD/6ng8UVQy0uhVpQZ2qkVZVDLSwGqhXf4GIg79EZNTQ0WLVrUqMWWWq3GxYsXTY4lJSU5fAdXoVCYBPx49+R23R3ldmFMdXW1iUsEwO20OwOFQoFbt+6sG5y5w2U8jvH/DNvIy8vjDx48uOeWLVt8zpw5I05OTnb54osvvD/66KOAkSNHlkkkEu2mTZtuHT9+XBoXF9d9//79ntevX3c5ceKE20svvdRpypQpXQCgd+/eyrvvvluhf3Tr1k0FAP3791cEBwerAWDTpk2+n3zyic/58+ddk5KSRIsXL+64fft2/xdeeKFJHD8doqQRQvg6E0STRTw4G0MYtrb+LfbRo0dj586dGD16NAgh0Cgt+ylTXT/Whnd/8803yMjIgLuQh2FmShoAjOgmhZuQh5s3b+K7776zqk9zNm3ahPT0dAg9BOjyYCcoSpVQlhklqixTQlGqBI/PQ7d4LkrtwIEDtfyvrCElJQXz58+HqqYGvaVSDDUL23cXCDCpa1fwCcHvv/+O9evXN2pFe+PGDfz4448AgKguMYbjvTvdAyHfBSkpKTaZni2Rl5eHZcuWgVKKYCkffh6mOycdPQUI9OQbdkzMnbvbE21RRgCcgjZz5kxDip4O/adBIJJAo5Qh9+/1htQ8AJemJ/fv9dAoZSA8Pnx7PQGxfx+o1WosXrzYbkXt9OnTqKqqMjz3EvEgl8sdrtT8/vvvJrvc3r28AcKZJR2tcJw5c6aWedNZEeDmi1xn+fPl5uaamKVv3brVogOLNFoKdRM8NFrbP1OpVKqNjo6u2rx5c8cRI0aEDxgwoPfy5cuDJk6cWPjFF19kAsCkSZPKfvvtt2ShUEinTZsW1q9fvz7PPvtsmEwm469evdqmN/69994LjImJiYyJiem1d+9enw8//DBjyZIlzsvnZIRdCWEIIRsA/Esp3UYI4QP4A8AQANWEkFG6/GetmuDgYABATVX9n8OECRMQEhKCCRMm4ODBg1AryiBCbXcZta4ffb/1ce3aNXz++ecAgCd6+8LdpbbZxMOFj/G9fPFVUiG2bt2KgQMHomfPng32ref48eOc4zIBuo8Lg1ZNcWG96W5P0ibOvDBgTj9Iu0kQNDQAOX/lYfXq1YiIiEDHjtbtMqalpWHOnDmorKxEqLs7ngkNtdguzNMTE7p0wa6MDBw4cABCoRCzZs2yOdO8RqPB+++/D61Wi84+3eEvufOeuwrd0LvTIFy89Sc++eQTxMTE2JW/rqioCK+//jpKS0vhKSKI6OACS6ImsqMLZEoFioqK8MYbb2DDhg1Oq6zQkmgPMqK4uBizZ89GQUEBBG5+8O8/FXxR7QVVXRAeH76R41FCeKjOv4QlS5Zg7dq1uPvuu22ahz7oR89/Oktw+EYZvvnmG8TFxTmkUoNGo8Hu3btNjokkLvAO90JpMudWsHDhwkaPo+e3334zec4HkJ2djZSUFISHhztsHOj6NSYlJQX9+vVz6BgALLo9fPnllw593xwBj8ej3lKJevqh9CatOMDj8azW1sRiMd28eXM2OPeJOomNja2OjY212kFz1KhRFZTSc8bHZs6cWTxz5sxmy5di74cwHoA+FG80uASVEQCeA7ASQEwd17UaevfuDQBQlmWAUi0IsbzpuGfPHkyYMAF79uwBIQQCVy+L7RRl6Sb91kVFRQUWL14MtVqNAQFuiPBzRXH1HdN3cXUNXAQ8eLsKENvFE5fyq3AxrxqLFy/G559/Dg8PyyZLY0pLS7Fu3ToAQNDQAHh1l0JR2rBppPOIYMgyKlCZVYnVq1dj/fr1Df4AZGVl4bXXXkN5eTk6u7lhcrduqFSrUWNkaixVqSDk8SB1cUF/Hx/UUIpvb93C999/D7FYjP/7v/9rcG7G7Ny5E5cvX4aAJ0T/0NoleHoGRCG98CrKy4uxdu1avPvuuzb9kGVlZeH1119HTk4OXAUE/QNdoNJwq0I98hotBDwCkYBgQLAIZzOVSEtLw4wZM7Bu3ToEBgbWM0KboE3LCLVajUWLFiE3NxcCsQ/8+0+zy3eV8PjwiRwHSrWQF1zG22+/jc8//xydOlmX3u38+fNISkoCnwAa3e0X28UTv6WX49q1azhz5oxDKoYcOXIEGRkZ4Il40CrvfHeDYwNRmlyGI0eO4Omnn0bXrl3r6cU6CgsLa/npRXp54XJZGfbv34/58+c3egxjjAMUAM5UPH78eIeOcebMGSQkJNQ6fvjwYdx3330YMmSIQ8drDHw+H/sO/Jik1WqbLJMtj8ejtpSEak/Ya+70A6BPEvcIgO8opSkAtgGoVeOqNRIZGQkPDw9o1XIoy+reyk9ISMDEiRORkJAASqlFQa1VK6Es5ZT5QYMG1Tvuhg0bkJeXBz83AR6L8Mabv97G28fuhNe/fSwL83/JRKlCDUIIpg7wh69YgOzsbGzatKmenu+wdetWlJeXw62jGJ3jGt7Z08Pj89B9fBh4AoJ//vmnVnoQc/Lz8/Haa6+hpKQEgWIxnu/eHSqNBquuXMG6a9cM7dZdu4ZVV66gXJcX6W5fX4zt3BkA8PXXX9sUjXnmzBls27YNADCw6zC4W/g8eDw+Bnd/ADzCx19//YUvv/zS6v6vXr2Kl156CTk5ORALCe7qLAIIwZ/pCpy+dUfRPX1LiT/TFVCqKdyEPNzVWQRXAUFmZiZeeuklpKSkWD1mK6VNy4jvvvuOi/4WuMKv33ONCi4ihAffyHFwkYSgqqoK7733ntXX6usJDw25M77UVYD7QiWG8401EVZXV+Ozzz4DAEMQkR7Pzh7w6eUNrVaLjz76yCHmyC+//BJqtRqh7u6GY//x4/xKf/7551rpRhrL33//bfL87NmzDq3xm52djXfe4eJcgiV3FJFOUu7/5cuXO83Eai98Ph9CoZA21YMpaHVjr5KWD6CXzozxEAB93SI3AG2inL1AIDAUQq7Ou1hnO71Qqk84VRdeAdWq0alTJ3Tr1q3Odv/++y+OHj0KAuD/ojtCLGz4xvVw4eOFaH8QcAliG4rqSk9PN/hihY3pAp7AtltA7OuKoHu5XaBPPvkENTWWA1z00W55eXnwE4nwQvfucLOh3MqQDh3wSFCQYZw9e/Y0eE1qaioWL14MrVaLrh16oYtfBCoVMlQp7+Smq1LKUKmQQermh+iu9wHgAgyOHDnSYP+XLl3Cq6++ivLyckhEPNzT2RVuQuveP3cXHu7pLIKHC0FJSQlmzZrltAi8FkKblRFqtdpg+vPq/hCEbr6N7pPwBPDtNR7gCXD+/HlcM1rE1MXly5e5SGkewQPdvEzOPdzDCy58gmvXrjU6EfWOHTtQVFQEkbcIAff41zof8kAnED5BYmJio1OKXLp0yeBLOsIouCjU3R3hEgnUajXee+89hwT9AFwEuHFaEVehO6qqqnDqlGOKYlRUVGD+/PmQyWSQuPLQw09oONfDVwipKw+VlZWYP3++SdJyBkOPvUradgDfArgMgALQVxIeBCC5rotaG48++igAoLrgUp0BAQ1BqRaVt08Z+qvPrKb3LRka4oluPtanYOnhK8Z/dOHw+kjDuvjss8+g1Wrh08sbniH2rf6DhgZA6CFEbm6uxS3827dv45VXXkF2djZ8XFzwfz16wFMotNBT/QwPCDAI6s2bN2P79u11KsPp6emGHFX+kk64K2w45KoqJFzYjsNJd5LkHk76HxIubEe1shLdOvZBeOBAAMCqVavqddy+desW5s+bB4VCAR83bmdMJLDNGuAq5OHuzq7wEvNQVVWFuXPntmjH4UbSZmVEZmYmSktLQfgiuHfs77B+BWJviH05v1JrksTq5cWgTh7wFpsugCQiAWI6e5q0s4dr164Zru/6aIjFRZ3Y1xVBMdz3dP369TYn7NZTUVGB5cuXg1KKu3x80NXMdSO+UycIeTycP3++ln+cvRw6dMgkcKCL7v13RKJhjUaDd955B7du3eLcHoJE4BnVwiQ8gv5B3A57VlYWlixZ4vTKCozWh71loZYC+C+ArQBiKKV6O48GwGrHTK356du3L3r37g2qVUN2y/aIRgCQF1xGTVU+3Nzc6i2qXFNTY9h2H97VYn3XeonTXXPq1Kk6v+jJycncSpcAne+33sxpDt+Fj073cbtpX375pUmof3p6OmbMmMEVXhaJ8HLPnvB2camrqwZ5IDAQD+j8t7744gts2bKllqJ2/fp1zJw5E6WlpfB274Ch4aPA51m3a9e/y1B08YuARqPB4sWLcfTo0VptVCoVFi9ejMqqKniJeRgQJLK78LCQTzAwWASJKw8ymczgf9jWaMsyQm8KIzwBUIevqjHWRoADAOGLAKDOHWo9WVlZhkXFA90sy4uR3aQg4GRCenp6g/M0p6KiAkuXLuUWdb29IfYXW4z+phqK4PuC4OrniuLiYrz77rs273RptVqsXLkSeXl58HFxwWM6dwdj/Fxd8ZjOV2/r1q210o7YilKprBUZH9axNwg4dw5rdjPr44svvsDff/8NHgEGBFle1Ol9VvmEM7PqzcoMhh6blTRCiJAQcgxc5NYHlFKDgwCl9EtKqXOLnzUhhBCD03pldiJUFbkNXGGKVq1E2U3OjPb0009DIqk78istLQ1KpRLuQh5CpLYrNV28RBALuND7uooe79q1CwDg188Xbv5im8cwxj+6A1ykLiguLjYoNiUlJXj99dcNPmjTe/aEVyMUNID7DEYGBhqE8549e0xMn2fOnMGMGTNRXl4Ob3d/3Bc5Fi4CkU39D+o+0qCoLV++3PA+6fn222+RlpYGIR+IChSBb6eCpkfA44INBDwuksy8WkFrp63LiC5dukAkEkFbUwVlaVqD7Y0jwCmlJrnTjNGqFVAUc6WQGopg/PLLL6HVatHHXwxXAa9WcFFRdQ383IQYoKtOoq9BbC36XaCcnByIvFzQaVggLqy/ZIj4Brjo7wvrL0EpU4Ev5KHnE2EgAoKTJ0/a5OcJcN/rkydPQkAIJnbtimq1GqVGfmGlKhVKlEpE+/hggDfnA7d06dJGlVbavXs3iouLIRbe2bFzF0nQpUMEAG733l6z6okTJwzRnL07usBDRCCv0UJec6c//XN3F4LeAZyc3LVrV4O+voz2hc1Kmq7uXh/AYsaBNsfAgQMxfPhwABSl1w+AUuu/tOXpv0GjlCEwMBBPP/10vW31deo6S0V2hczzCEFnnXJnXPNOj3HeJL1pojHwBDwEDOb8U379lXM32rRpEwoLC+EvEuHFHj3gYYeJsy6G+vtjjE5R+/TTTw07CfPnz4dCIUdHaQjiej0OkdB25ZNHeBjc/QGEB3L1WT/++GNDCpSKigpD4EJ4BxebTZx14SrkGZLffvnll42u5NCSaOsyQiwWY9SoUQCAkusHoKmp/7Pbs2cPMjMz640Ap5Si9PqP0NZUIyQkpN40HNevXzf4UN4XKsH8XzLrDC4aE+4NAi7H2eXLl61+jYZdIAFBz6e7g+/a8M60e5A7wkZ3MVxvbSWFlJQUbN26FQDwWOfO8BAI6gwuktXUYHxICPxduV27NWvW2BWskJKSYlCi+oaYRr/27TwYfJ4ASUlJ+OGHH2zu++bNm1ixYgUAIMRLgECJAEo1rTe4KMBTgC7e3Hv87rvvIjU11eZxGW0Te33SvgJnymgXzJo1Cx4eHlBVZKMy+6xV16gqclCZxZkv33jjDYhE9e/unDvHpWbp5mP9LpA53bxdTfoy5saNG6ipqYGLRAi3gPoVGXPzjKrCcqSTd08vAJzfSnFxsaG259Ndu8LdhiABaxnaoQN6SiTQaDTYvn07li5dCo1GgxDfnoiNGAOhDTto5hBCMCA0Fv27DAXAKU4//fQTfvzxR1RWVsLdhSDQ07ERSEFSPtyEBOXl5Y1OrNsCadMy4vnnn0dAQAA0ilIUXfoftOq6U9g0FAFOKUX5zaOoLvgXfD4f8+fPrzPptUajMSR6HtTJA52l9d/znaUixOh8T99//32rTOvnzp0zKDBh8V3hEeTewBV38B/YAQGDuMXbypUrUVRUVG97jUaD1atXQ6PRoK+XFwb5NhyE4cLnGxJfnzx5slZOtYYoLS3FW2+9hZqaGgR7h6GzTw+T8+4iCaJCODmwefNmmwIvSktLMX/+fMjlcniLeejZwfqFag8/IXzdeFAoFJg/f77Ti8ozWgf2KmkuAF4mhJwjhHxKCFlv/HDkBFsCfn5+BrOnLP03EL4LAgfPQcd7ZhradLxnJgIHzwFfJAGlFGU3DgOguP/++3HPPffU239FRYUhmqh/R+sFojlRAW4AuPIp5jsz+lInLhKXBnfqzM0zyjLLSpqLhNsJksvlOH/+PLRaLYLEYnRyc7NqvubKoKwBPxxCiKHG559//omamhoEeYVicI8Hwa+jTqKtRARFo08nLk3K5s2bcfjwYQBAF2+hVTuc5q9Jqa57lc8jBCG61fPPP//sgNm3KNq0jPDw8MDatWvh6ekJlew2Ci99VaeiVl8EOKUU5Wm/oOI2t8v9xhtv1JtI9eDBg7h69SrEAh6e6GVdVOn4Xr5wE/Jw48YN7N27t962KpXKkALE/64O6BBle+Rql4c6wz3IDRUVFQ2mBfrxxx+RmpoKMZ+PxzvXrh1cF4FisaG03ObNm63eia6ursa8efOQl5cHD5EU93QbaXHMHgH9EOLbExqNBgsXLMSNGzca7Fuj0WDJkiXIz8+Hm5ALCuDZYBXhEYJ+gSK4uxAUFhZiyZIldpf8Y7Qd7FXS+gA4D0AGoCeAAUaP/g6ZWQtj9OjRCA0NhVYtR2V2IgRibxOzhcDVCwKxNwiPD2VZOpRlGRAKXfDyyy832PehQ4egUCgQ7OmCrt727wZ193FFoIcQcrm81s6MfmVONQ2bBszNMyIvy35l1Ch5a14elxKro6v1UanmymCpFbmJAnT9y+VyAEB0WBx4Vjhv20KvTvfAzcUTMpnMUO7G38M6JdD8NRn7oFjC353rNzU1ta0FELR5GdG1a1esX78e7u4eUJVnojCpbkXNEgYFLZNLW/Hqq68azKiWqKioMJjh4yO8a0V01oWniKtOAgDbt29HWVlZnW1//vlnZGdnQ+ghROhDtZ33rYEn0JWRI5yZtS4FJz8/H59++ikA4MGgIJvdI+7r2BE+Li4oKioy9FMf+h2q5ORkuAhcMTRiNGo0Kospeigo7uk2En6egaisqsTrr7/eYC6zhIQEXLhwAXweMCBYBCHfdtcIIZ9T7vg8Lqnu/v37be7DEWg0GtTU1JCmejBltG7ssklRSoc7eiItHYFAgMmTJ2PZsmWozEmEpEvtTPZ6KrJOAwBGjXq0wdJJlFJDuPf9YdI6V5KjR482VDZISEhAmUINPzdToUYIQVyYFDsvFWH//v0YN26coT9PT87kUVPdsCKQkJCAgwcPghACSilcPC0raeoqbufLzWjnzJaVo3m1BmuiQI37F/BdLCartYT5+ydXVcHD1XIgB4/wIBF7o1p1JwpPq6WAFULX/DWJG8ijpteZeTzHKprNTXuREREREdiw4QO89tprqJRloujyLnTo95xV11Zk/mlQ0GbPno1x48bV237Xrl0oLy9HkKfQ5gjwe7t44lhGOW6XV+Krr77CrFmzLLbT5ygLGhoAvsj+3Wn3QDf49PJGyZVSHDx4EK+99prJeaVSiSVLlqCqqgohbm6GZLW2IOTxMC4kBJ/pdgijoqIQFxdnsa1KpcJbb72FixcvQsB3wX2R8RDwhEi4sN2knT5dz6gBU+HhKkFsxBj8fuUHlJQUYfbs2fjoo48QpMvfaAyl1BDQ1N1XCHcX+7/P7i5cPrXkghp88803ePzxxx1S2staNBoNHhs7Jqq8VNZkZaGk3hL1gX0/JtmS1DY7O1swd+7c4GPHjkmKi4uFEolEExERUb106dKcESNGVAHAyZMnxStWrAhMTEz0rKys5AcGBqoGDx5csXDhwrx+/fopAYAQEm3e95o1azLnzZtnKLasC1Tp+NVXX3XIyclx8fHxUU+ZMqVg9erVeebXOppGfQiEkO4AugE4QSmVE0IIdUYF3BbCfffdh02bNqGkpASKklSIvMNqtdGoqqAo5rLJjx07tsE+MzIykJWVBRc+waBOdZd0Mq8RWlytRnef2u3+08kT31wuxq1bt3D79m2EhHB1RP39OT8RVYUKWrW23iS21iToBQBlObfz1aFDB0MZm7TKSqi1WgisUDrMlUGJFSvpFKOEj2qNCpWKcni4NvyDZf7+VSll6ADLpZm0VIuyas6XJiwsDGlpacgoVSPCv2El0vw1NRRokFHCKbr9+/ev0w+pNdMeZERERATWr1+PV199FfLSNJTd/Bmeneov8yMvTkF5Gpc67pVXXmlQQSstLcUPP3wPAHg80tfmCGMeIRjfyxcfnM7Fgf378cwzz8DPTDEqKSnhgo4I7DJzmuM/wA8lV0prZfRXq9V45513cOXKFYj5fDzTtatNiztjekokGObvjz8KCrBixQp4e3tjwIABJm20Wi1WrFiBxMRE8HkCDIsYAx+PjqhUNJzPzUXgivt6jcXvV35AYWEhXn/9dWzZsqVW/d2ioiJkZWWBAAiWNv57HCwR4HphDXJzc5GXl9ekZeS0Wi0pL5UJ7nl7IIgdu4G2QjUUicvPC7RaLeHz+VbLhjFjxnRTq9Vk69atGeHh4crs7GzB0aNHJUVFRQIA2L17t3Ty5Mndhg4dKtu2bVtaRESEMicnR7hnzx7vBQsWBB86dMgQmv3hhx9mxMfHl+uf+/j4mGztTZs2rfMff/whWbly5e3o6Gh5SUkJv6CgoEkEtl3qPiHElxDyG4AUAD8Bhl+7zwkh7ztqci0NgUCA+++/HwBQXfCvxTbywisA1SI8PNyqOnb6dBmdJS5wrUdxMjdB+rpZvj/EQh4663zFjFNx+Pn5cSlAtEBlVlWD87IGWQanMHXr1g2DBw+Gj48PSlUqfHvrFjRW/A5bqwzqSa+sxE+65K965TOt4Ep9lxgwf//c6ymEnVuWAUVNNTw9PQ3m6swyNUqqG96St+U1FVZpkC3j+pwyZYoVr6L10N5kRGRkJJYsWQIAqMz6G2pleZ1+q0TgitLk/QCA+Ph4TJgwocH+f/jhB8jlCnSRitA/wDqfT3N6dxCju48rVDU1FpNe6yMKxX6uEHo0PjLbswu36MzJyTFk06eUYsOGDThx4gT4hGByWBh8GwiqaohHgoPRWypFTU0NFixYgLQ007QoW7ZswbFjx8AjPNwbPhodJLbliHQVumF4r7FwF0mQlZWliyhXmLTR+/wK+bA7h6IxfB6Bi05Bsjc5cGMhfAIen+f0hz2KYFFREf/8+fMe7777btbo0aMrevbsqRo+fHj1qlWr8iZMmFBeUVHBmz59euiwYcPKf//99xvx8fEVERERqri4uKqtW7dmffnllya1Hr29vTUhISFq/cPDw8MgwM+fP+/6v//9r8P+/ftvTJw4sTwiIkI1ZMgQeXx8fJOUiLB3T/YDADUAQgAYe2x+A64ETJuFS8cByIuug2pqO7pX65SGurbd66Kh7WzzCDEvK0LizfsfOpSLWMo+kdvoGns11Wrkn+V2g++9916IxWIsXLgQPB4PF0pL8VlqKioaCASwFkopzhQV4dPUVKi0Wtx9993473+5wMEbeZdQo2nYl838/RO71B2gkZzNZXsfNWoUBg8ejNGjRwMA/s1TocYKnz5rUGkoruRx/kvjxo1DVFSUQ/ptQbQ7GRETE2OoUlJx6486/Var8y5Co6pAYGAgZsyY0WC/Go0GBw8eBMCVe7LX9EUIwSM9uPkcOnSolg9kfn4+AMC1gQhza6O/Ba4CCHSLyYKCAgDA0aNHceDAARAAE0ND0c3T/pqneni63GphHh6oqqrCW2+9ZUiwvWfPHkPFhHu6jUSAV4hdY4hdPDAsMh4uAldcu3atVnUAV52vrMYx1ap0fVGTvhl3kEqlGjc3N+3evXu95XJ5rS/Evn37JGVlZYL58+dbNEf6+fmZrLjfeOONEG9v76g+ffpErl27toOxj9zevXulnTp1Uu3du9crODi4b3BwcN+nnnqqS35+fpMUHLVXSXsAwHzjJJU6UgF0adyUWja9e/eGv78/qEYJha5ouh6NqhLKMi6z93333WdVf126cG9XZrkSqnq+4VabINVa3JapTPrWM3HiRAiFQpSlliPnT/tN6Vq1Fqnf3YS6Wo0uXboYXuugQYOwatUqiMVi3KysxAfXruFGI+vRKTUa7Ll1C99nZkJDKWJjY/Huu+9i2LBh6NSpE1QapVW7ada+f8UVeSisyIZAIMATTzwBAJg5cyY6deoEpZoiuUDVaAWXUoqr+SqoNEBoaKhVwSWtkHYpI557jvNHU5TchEZlece6uuASAM4E31BqHoDLu1VSUgKxgGdITmsvfTu6wcOFq3ZhnlFfH4zDc6n/t8fa6G8ABr82hUIBtVptcPAfGRiIvmYmQ0tYGwEu5PHwXFgYJEIhsrKycPDgQXz//ffYvHkzAKBfyBCE6pLU2otE7I17w0eBz+Pj1KlTWLp0qUFRc9cVg9fQhmWMNRHglFKodT8H7u6N+8zbIkKhEJs3b07//vvvfb29vQcMHDgwYsaMGcFnzpwRA0BKSooIAKKiohT19wTMnTs3Z+fOnTcPHTqUMm7cuJIlS5Z0WrBggcG+nJaWJsrNzXXZv3+/97Zt29I//fTTjEuXLrk/9thjdRfidiD2KmnuMF0d6/EDYH14UyuEx+MZlJLqQlPloLqAK1MYGRlp0bnUEl27dkVAQABUGop/shtvhkzMroRaSxEcHGwwCeoJCQkxrNwzf8lC7t/5NvfPKWhpKL8hg6urKxYvXmziSzVkyBBs3boVXbt2RYVaja2pqfgtLw9aOxSbAoUCG5OTcb6kBDweD//3f/+H5cuXw9XVFXw+H0899RQA4HruBWi0jokOupbDFVseMWIEOnToAIBLXvrWW2+Bx+Mht0KD2+WNi8K8VaZGQaUGfD4fixYtsuqHuhXSLmVEUFCQbnFEoarIqXWeajWG4//5z3+s6lMfOR3oKazXlGb+41+mqH2f8ghBkC4QSL9zpkfvtG0ctW0Ja6O/AYDqFp58Ph8ZGRkoLCyEK5+P4Q0EVOmxJQLcXSDAfbp+f/zxR3z44YcAgMiguxAZdJdV4zVEB0kwYnqOAo/w8ccff2D58uU2VyWwNQKcYZkpU6aU5ebmJu3evftGXFxc+cmTJz1jYmJ6bdy40ZdSavV289q1a3NHjBhRNWTIEPmyZcvy582bl/Pxxx8bblBKKVGpVOTrr79Of+ihhypHjRpV8dlnn2WcOXPGMykpyenC214l7QQA4xAmSgjhAZgLoM3XtBgxYgQAQFGUYnJc76dmi6mTEILHHnsMAHAwpRTqBgRkfdRotDiUwpVJeeyxxyyaRcaOHYtJkyYBADIOZSLnpPU7alq1Fil7bqLkaimEQiFWrFiBnj171moXGhqKrVu34pFHHgEF8HNODnamp6NGq4XUxQULevfGG5GRhvZvREZiQe/ekBpFd16XybAxORkFSiX8/PywceNGPPvssyZRkA8//DB8fHxQrazAzXzLPoK2UFyRh6ySmyCE4JlnnjE516dPH0OuvOSCGuTK7FPUssvVSCnkdgNeeeWVBsv/tGLarYzorKs7qVHULlmkUVUAVAuhUIiAAOsqf+ijp6tU9f+Ym//4F9cRya1XCsRi06TW+kWJsqR+HdrcdaCu6G+NSgNVJXev+/n5GXad+IRYHShgrhA2FAHuopMPen/cyKBo9AsZ4tDoyCDvUAwNHwUe4eH3339HQkKCoTyVgNew64r5a7IUAU4IgX5DszGlr9o6bm5udOzYsbJ169blXrhwIXncuHFFq1evDgoPD1cAQFJSks224piYmMrKykr+7du3BQAQEBBQw+fzqT4aFAD69+8vB7hdNke9lrqwV0mbC+BFQshhcEkr1wK4DCAWwHwHza3FEhERgeDgYIDeEYJqRTlU5ZkghBiCC6xl7Nix8PHxQUFVDX5MLrF7XvuTS1FYrYavr69B8TNHX49Ub5a59fNt5P6dD5HEBQPm9EPUzN6GtlEze2PAnH4QSVxANRQp395E6fUyuLi4YNWqVRg0aFCdc3F1dcWCBQswd+5cCAQCXCorw+c3bkCt1cJHJDIRtt4uLvARicDXCbek0lJ8cfMmlFotoqKisG3bNos+WyKRCFOnTgUAXMo8hYo6aiJag1pTg8SbXHmrBx980GLQxzPPPGN4X//NUyGzlPsBEgkI7u3qiv90ufN9/U8XEe7t6mqI7qSUIqOkBlfyuZ2AJ554AuPHj7d7vq2AdisjpFIu2lhroVyU/phEIrFacejWjbOq5FfVoFRe9+LAmuCiCqUGWTp3CPMFVu/e3He/Kq8aipK6rUTWug6UXCsDtEBAQAB8fX3RtWtXeHh4oEqtxkUrFQ9zhbC+CHC1VouThYWGuQVIu6BfSIxdSajldZiq9QR5h6JfyBDDHEtKOLltTdk489dU1zX6wAGmpFlPZGSkQi6X8+Lj42VeXl7qNWvWWFwJFRUV1WnT/+eff9xEIhHV+63de++9lRqNhly5csUg4P/9919XAOjWrZvTrQJ2KWmU0qsA+gFIBPALONPGXgADKKU367u2LUAIqeVzpi+M3KdPH8OK1Frc3d0xe/ZsAMBPqWW4kGu72fOfnEr8fKMMADBnzhyT3GXmEELwwgsvGCIKM37KRNmNcrh6iyDyuqNoiLxEcPUWgfAJbh29jdJrZRAKhVizZk29CpoxY8aMwfr16+Hm5oa0ykp8nZ5eb+TndZkMuzIyoKUUI0eOxAcffAAfHwu5RnSMHj0aUVFRUGtr8FdyAlQ2JBPVQylF4s1fUS4vho+PD6ZPn26xHSEEc+bMweOPPw4ASC6sQUqhCgRcVK3xilj/nKdLxXG9sAYpRZxSN2HCBMycObNJcx81Ne1ZRnh4cFGNlhLbatUKkzbW4O3tjT59+gAATmfV7eNpTXDR31kVoOAKuJvLKT8/PwwePBigQMZPtxvle6lWaHD7V84d8dFHH+XMoiKRIZJ1b2YmMqsalnPWKoRaSvF9ZiZy5XK46BaAQkHD1VX0mO9CGie4rQshn5OVarXa4PJhTeCA9a+J+2tL7rD2Ql5eHn/w4ME9t2zZ4nPmzBlxcnKyyxdffOH90UcfBYwcObJMIpFoN23adOv48ePSuLi47vv37/e8fv26y4kTJ9xeeumlTlOmTOkCALt27ZK+//77fmfPnnW9cuWKaP369X6rVq0KfvrppwvFYjEFgMcee0zWq1ev6ilTpoSePHlS/Oeff7q99NJLXYYMGSIz3l1zFnZn3KOU5lFKl1BKR1FKH6GULqKU5jpyci2Ze++91+S5ojjV4nFrGT58OB5//HFQAFvP5eNGPStZc1KK5fj8HBc99cQTTyA2tu5Eu8ZMmzaN2xmiwI0f0uqM0iq9XobcU5z/yttvv4277rLNv2PAgAFYv349XF1dcV0mw9Gc2r46AFCqUmFnerpBQVu0aBGEDeRO4/P5WLJkCXx8fFAuL8afyT9CbSHqti4opTif8Qcyi1PA5/OxdOnSWjmQjOHxeJg9ezZeeOEFAEBGqRrXC2vqFLiUUlwrqEFmGbcD8sorr+CVV15p0wqanvYqI/T3j0ZV+4deo0uQXN89Zgl9JYJj6bI6XSIa+vHXUorf07k56aNQzXn55ZchEAhQer0MOSfs+6ioluLG3jQoy1QICAjAk08+aTg3ceJEREdHQ6nVYmtqKq47IL1EjVaLnenpOKfzXX3ppZcAALeLU3HDSjcIW1L0AEBpVSEu3uISEd9///3o1q0b+Hw+FGqKckXj/WNlCi2qayh4PB66d+/e6P7sgWootBqt0x/WVMExRyqVaqOjo6s2b97cccSIEeEDBgzovXz58qCJEycWfvHFF5kAMGnSpLLffvstWSgU0mnTpoX169evz7PPPhsmk8n4q1evzgYAFxcX+vnnn/vfd999kXfddVevTz75xH/evHk5W7duva0fi8/n49ChQzd8fHzUDzzwQMTYsWN79OjRQ7F37960uubnSOxOxkYI8QJwDwB/mCl7lNKvGjetlk9ERAQ8PT0N+X+U5VzaFWt3mCwxc+ZM5OTk4O+//8aHf+fijSGBWDMyBCq1Fm8f41aly4d3gouAB2/dKvl2uRIb/85DjZZi6NChde4CWYIQgldffRVXr15FamoqMg7fRrf4UJM2GpUGaQe51/bkk08aUpDYSu/evTF//nwsW7YMx/Lz0d2zdpHpb2/dglyjQWRkJN58802rs/B36NAB69atw6yZs1BYkYMTyT8iNmIM3EQeGDVgKjTaGkMm8YejJoHPE8JN5AFKKS7cOoHUvCQQQrBw4cJaiTAtQQjBc889B29vb7z33nvILFNzBdgltb9OmWVqZJWrwePxsGDBAjz0UJvMPmGR9iojgoO5PFw11bWLi6urCk3aWMuIESPw6aefoqS0FGezK/GfzranrjiXU4WCqhp4enriwQcftNgmLCwMs2bNwvr165H5azaEEhf4D7C+GgClFOmHbhl23ZcuXWqyqy8QCPDuu+9iwYIFOH/+PLbduIHRnTphaIcOdi1cylQqfJmWhqzqaggEArz99tuIi4tDeXk5vvzyS/yT9jtq1EpEBEXX2795Eur6UvTkl9/GX9cTUKNRoV+/fnjyySchFAoxcuRI/Pzzz7iar8Kgzq7g2ZkvTauL/gY4/2a9+byp4PF4VOotUScuP9+kFQd4PJ7V2ppYLKabN2/OBpBdX7vY2Njq2NjYOnfux48fLxs/fvzVhsYLDQ2tOXLkSLNYAOz6EAghowHsBGfCqABg/OZSAG1WAOvh8/no27evoTA6qAZSqdSqBLZ1IRAIsHz5crz22mu4fPkyPkrMx6LYYHgalX/ydRNCpEt6W6ZQY8PfuZCrOd+tpUuX2py1XigUYv78+XjhhRdQ/G8JOt5lagLJPZUPVTm3ItbvHtnLiBEjcObMGfz888/Yf/u2ybl/Skpwo6ICIpEIb7/9tsFkYS09evTAuvfXYc6cOSiQZeHP6wcRGzEGHq4Sk501d5EEAr4QlFJcvPUXUnIvAuAKWz/wwAM2jTl69GjIZDJ88sknSCmsgY+bqVIpr9EiVWfinDlzZntT0NqtjNAHg9RU5oNSiuDYtwEAhCeEUpZl0sZaRCIRnnjiCWzduhUJKaUY1MnDpiz9WkqRoAsqGjduXL3uEGPHjkVOTg727NmDtP3pEIgFcOsorrO9Mbd/z0F+YiEIIVi0aJHBz80YNzc3vPfee1i3bh0OHz6MH7OykCuX4/HOna2qVGIYq6oK22/eRIVaDalUiuXLlxsWWf/973+hVCqxZ88eJGWeRKWyHNFdh9dZ59daE2RGYTISb/4CLdWiX79+WLNmjWG3/8UXX8Tp06dRXl6OqwUq9O5ovbnVeB7JBTWQKbUmybSbEj6fjwP7fkzSarVNtt3P4/EoM+taxl5z5/sAvgDgSSn1opR6Gz3qdiBqY0REmObd6dWrV6PNWK6urlizZg06d+6MErkaX1wosCg4KKXYdr4AZQoNunTpglWrVtmdyiE8PBwPP/wwACDrjzumSLVCY4j+fPHFFx2SVHHmzJnw8vJCgfKOKV+l0RgqCUybNs0QHWcrvXv3xvvvvw+xWIz88ts4nfpznUI3Oec8rudySWvnzp2LMWPG2DXm008/jT59+kBDgfQSU6fum8U10FLO3NtQ2Z82SLuVEQEBAVzkJtVAVZYBHt8FPL4LqLbGsONuzY6tOY8//jg8PT2RV1mDv7Mqbbr2fG4VsmQquLm5GfL/1cfLL7+Mhx9+GFQLpH7L5USsL7AIAAouFCH7OCc/5syZU2+Uu4uLCxYsWIAZM2aAx+PhbHExvkxLszoCPEUmw8epqahQqxEWFoatW7eavKeEELzyyit49dVXQQjBzfzLSLzxS6P87NIKruLvG0egpVrExcVh/fr1Jr6Ffn5+ePvtt8Hj8ZAj0yCtxPYI8IxSbuedEIK3337bUM6vqeHz+RAKhbSpHkxBqxt7lbRgABsppZbyILUbzKOjwsJq1/K0B4lEgpUrV8JFKMTlAjku5tV+m8/mVOFqoRwikQgrV640FFC3l8mTJ4PH40GWdscxuehSMTQ6JdDWCgp1IZFIDNUC9JwtKUGlWo2goCCrfkDqo0+fPobVbVbJTSTnnKvVJr/8NpIy/wIAzJgxw24FDYCJD0yO7I4vSrVKa3j+0ksvtQsfNDParYwghGDIEC7yT150x5KiKE4FtGoEBgbWSjRtDe7u7nj66acBAD8ml1idrkejpdh/jYs+fPLJJ7nycA3A4/Ewb9483HPPPdDWcMmrhR7COgOL5EUKpP+YAYCTJfHx8Q2OQQjBU089hdWrV8PV1RXJMhn+l54OAtQbAX5Lt4NWo6s+8vHHH9eZl3L8+PFYtmwZl6etKBmXs/622K4h8stv46wu+vvxxx/HkiVLLC6KBw0aZAgCu1lcgyydL6o1EeDZ5WrDzvusWbOszqPHaNvYq6QdAeCY7ICtGPMdH32RcUfQtWtXTNAJ5CM3y0zOUUoN+dCeeeYZuwS+OUFBQbX86Uqu3Mm5Zq1/mDU8+uijJo7TiUWc784zzzzTYKCANQwYMACvvfYaAODf23+jQl5mOKfRqpF48zfDPPQJcRtDVFRUrWCKW7r0HEOGDEGvXr0aPUYrpF3LCH3wjrwoGVSXaFmuS349bNgwu5X28ePHw8fHB4XVapy4ZZ3T/enbFcitrIFEIrHpfhcIBFiyZAk6dOgARbESufXkVMw4nAmtmuKuu+7CtGnTrB4D4JL6rl27Fi4uLrhaXo5f8+oep0qtxldpaVBTisGDB2P16tX1mm4BLihr3rx5AIArWYnILrXN37taWYFTqYdBwQU0zZ49u155OHbsWEOKo6sFKhRWasDT5UOrKwK8qEpj8EObOHFiW0/Pw7ABq395CSFj9A8AhwC8RwhZSggZZ3xOd75dYB7CbmvqjYZ44oknIBQKkVluGnWZWa5ClkwFFxehQ7/M+tqeeip1FRDsjVitC6FQaJJLrkilgouLiyFJsCMYNWoU7r77bmipBleyEw3H0wquokpZDj8/P8yaNcth4+kTBOvJq9BaPN6WYTLiDlFRUZBKpdDWVENZlgGqVUNezCW/trZknCXEYjEmT54MADh4vRRiAQ9rRoZg+fA7C8TlwzthzcgQeLsKoNJosV+Xe3HSpEk2pf4AuJ1v/U5x7ul8aNW1c0xU51ejLKUcPB4Pc+bMsWtBN2DAAIMi9VtuLvJ0JarMOZiVBVlNDbp06YJly5ZZ7bv6yCOPGFLnnE49gtKqQkNg0cNRd76jD0dNwqgBU+Em4t6nGrUSf14/CGWNHN27d8f8+fOtUrCff/55QwTtpTxlvYmIq1VaXMpVggJ46KGH8OKLL1r1mhjtA1u+TfuNHp8B6AxgMYDvzM7tc9jsWjhisdjET8vWsPqG8PLyshgt+k8O54/yn/8Mscp0YS3mPnagXGLOjlaWcLEFvTlIz8CBAx1ao44QYvhxySq5YTh+PfcCAM4k09AK3BYGDhxokkGegqu8oM9v1U7YDyYjAHC7UDExMQAAefF1TlHTKOHr64tII18rexg9ejQ6deoEmVKDX9PK4ecmhK9ZcJGfmxB8HsGvN8tRqtCgY8eOBiXFVuLi4tChQweoq9UoSy2vdb7gQjEAbpFnrz8pwCWRHjp0KLQAErJrB+1lVVfjnC5p7IIFC2z+/s6YMQP9+/eHWqPCH9f2o1JRDg9XiUm6DXeRBB6uEvAID2pNDU5cP4jSqkJ4eXlh5cqVVvv9EkLwxhtvICoqChot8G+uymJpPEop/s1TQa3lXDXmzZvXHl0jGPVgtZJGKeVZ+WhXHoDGK1NbV6nWYL7qppTiXE6VxXONxZJfR1BQkFOEhnl0W79+/Rw+Rs+ePXHPPfeYHFPUVMHb29sQKOEoCCG1diIbY9ZqjTAZYcrgwYMBAIrSm1DoFgqDBw9utOuAUCg0lCj7+UZZnVUIKpQa/JRaBoDb2bE3sEggEBh2vkuumma/p5Si+DKnONkaHW2JV155BXw+H9dlMtysNA2O0AcXjRw50mLUaEMIhUK8++676NGjBxQ11Th+dS+qlZYDMLRUi1MpP6FQlg13d3esW7fO6nrMegQCARYvXgwPDw/IlFrcLqv9Od0uU6NcoYW7uzuWLl3qEHcPRtvCLmlBCHmOEFLrG08IcSGEPGfpmraK8U6aeS08RxATE2OSVuO2TIX8qhq4uLjU2o1qLB4eHrVWp86KLvL09DTJFWVeDN5RWNo9GDVqlFOKmptH7EVHRzt8jNYCkxFA//79AQDqqgKDqVN/rLHcd9996NOnD1Qain3XLJeS259cArlai549ezZagdIHDpWmmO6kVd6ugqpcBbFYbFBKG0OnTp0MpdeOGCW9Tq2oQGpFBQQCAZ5//nm7+/f09MT777+PkJAQVKsq8df1g9BoaytPl26dRE5ZBkQiEdauXWt3jV1/f39DGo0bxTVQqe/spqnUFDeKOd/VF1980SkWC0brx94l3XYAljLseerOtRuMd0ocaT7T4+HhYSL8EnWh90OHDnXKeOZKWWBgoMPHALj3zThJY2OjU+ti0KBBtXY4Hen7Zoy5ubgNF0+3hnYvI7y9vQ0mcHU1l8S2lkuBnRBCMGPGDADAqdsVyJKZVqfJrVAZAgv0aS4aQ0REBDp16gSqNjXZFSZxQT/33nuvwxY+kydPhtjVFdlGfmn6YIL4+Hibd7TM0SehlkgkKKkqwPWc8ybnC2XZSNal51m0aFGjd/lHjRqFyMhIaLTAjeI7/sU3izkzZ3h4eKMizBltG3u/uQSmySn1dAJQ22mhvo4ImU4ISSeEKAgh5wghjvVSdzLGeXdsTcBqLcZO9md1/miOMC1YwjxC1ZERq+YY7xDamoTXljH69u1reO7t7d2ohMP1YR444gwluhXhMBnRmgkNDTX8z+fzHfp96t27N4YPHw4K4FBKmcm5gyml0FJuMWdPTjZzCCG1qhRoarQousTt4jnSfcDHxwePm+UVzKquhouLi8MCcYKCgjBnzhwAQLLOT1VPUuZJAFz0tyNcSng8Hl599VUAdwKKACBX9/+sWbNaXH1OjUaDmpoa0lQPjabxpbTaKjb9MhJCLoATvBTAb4QQ431iPoCuAH62ob+nAGwAMB3ASQAvAjhMCOlFKc20ZW7NhfFOmrP8j+6++27D/xUqLTw8PBpVfqo+unbtir/++svw3FlmSAAm/hfO9MUIDQ3F6dOnAXCvx1mfU3vyP6sLR8uI1o7xTrS/v7/DFyNTp07FsWPHkJR/Jx1dqVyNs9ncYm7KlCkOG+vhhx/Gtm3bDM/LrpdBowtKGDhwoMPGAbiqCLt27TJZBI8cORK+vr4OGyMuLg579uxBcnKy4VhJVQGKKnIhFAobZVY1p3fv3hg2bBj++OMPk+MxMTFO8cdtDBqNBmPHjIkqlcmarCyUt0Si3vfjj0m2KKvZ2dmCuXPnBh87dkxSXFwslEgkmoiIiOqlS5fmjBgxogoATp48KV6xYkVgYmKiZ2VlJT8wMFA1ePDgioULF+bpi6MTQmr5paxZsyZz3rx5hfrnn3/+ufe6desCb926JfL29lb/97//LVi+fHm+A156g9j6IezX/e0PLg+SsdelCkAGgB9s6G8OgG2U0s91z2cTQh4E8DKABTbOrc0ikUgwdOhQg/J0zz33OG3nyXjlb+m5IzE2QzoystMcPz8/i/8znMJ+3d/+cIyMaNUY32+OVDD0dO3aFdHR0Th37k7S5nO5VdBSoG/fvg41ueuVsfPnOVOg8S6aI/MoAtyudL9+/ZCUlGQ45mg3BUIIxowZY6KkZRdzAR733nuvw2XFU089VUtJmzBhgkPHcARarZaUymSClf37g98EC08NpXjr4kWBVqslfD7f6pIQY8aM6aZWq8nWrVszwsPDldnZ2YKjR49KioqKBACwe/du6eTJk7sNHTpUtm3btrSIiAhlTk6OcM+ePd4LFiwIPnTokCFh3ocffpgRHx9v2OH38fExbO19++23kpdeeqnru+++e3vUqFHlly5dEs+cObOLWCzWLly4sBBOxqZfekrpMgAghGQA+IZSqrB3YEKIC4BoAKvNTh0FYNEjXueIbOz44BxHphYGIQT9+/c3KGnG5jtHY5wY19PT0+FpRYwxVsycOY6x75sjU5YwauNIGdEW8PLyMvzvrHs8Li7OREm7XFBtOO5o7r//foOSVp7G+bw5y/ViwIABBiVNXyvZ0ZgHXxXIciwedwR9+vSBv78/CgoKAHBKe1RUlMPHcRR8QppESbOHoqIi/vnz5z0SEhKuP/roo5UA0LNnT9Xw4cOrAaCiooI3ffr00GHDhpX/8ssvhsLoERERqri4uKqioiKTLTtvb29NSEiIxTDpr7/+2nfkyJFl+p21Xr16qVJSUvI2btwY+OabbxY6eoFijl29U0q/pJQqCCHRhJBJhJCJhBBbHR/8wJk/zLcM8wEE1G4OgNtdKzd6ZNk4ZqvFWHly5u6WccSlM1b+xhibB52RvkSPcQSuI+qPMhrGQTKi1WO8QDD+35GYRxGnlyosHncEJqWKKFcKrzG50erDWM4FBwc7JSLb19fXJBChTBfg4QwTJCHEJG9inz59mIuEnUilUo2bm5t279693nK5vNabuG/fPklZWZlg/vz5FstX+Pn5mTjBvfHGGyHe3t5Rffr0iVy7dm0HYx85lUrFE4lEJjt8YrFYm5+fL0xNTXWOI7oR9qbg8CeE/A7gLICNAD4CcI4Q8hshxNa0++bbm3U5HAPAKnARY/qH87zaWxjONpvoMVaWmlKAOHMsY+HurOAOhikOlhGtFuOdW2cpaUFBQSbj1Gi5XWpHlIszx3wx5WhfNGOM/fkcXc3FmB49ehj+p6CQSCQmiamdNZbx/wzbEAqF2Lx5c/r333/v6+3tPWDgwIERM2bMCD5z5owYAFJSUkQAEBUV1eBO/ty5c3N27tx589ChQynjxo0rWbJkSacFCxYYbr6RI0eWHzlyxOvAgQOeGo0Gly5dEn388ccdAeD27dtOT2xn7z7dJgASAL0ppT6UUm8AfXTHNlrZRxEADWrvmvmj9u4aAIBSqqSUyvQPABWW2rVFjE2DzkpXYU5bWeUZR1k60/cN4BLYApyTczvHETICQOuOADdWnpz1vSWE1PrB79Gjh8P9xABuJ/qRRx4xGcdZODtRuB7zaO+wsDCnyT5jS4Xx/wzbmTJlSllubm7S7t27b8TFxZWfPHnSMyYmptfGjRt9KaVWf4Br167NHTFiRNWQIUPky5Yty583b16OXgkDgDlz5hRNmTKl4KmnnuohEomi77333sixY8eWAIAtPnT2Yu+3+CEAL1NKr+kPUEqvAngFgFWx2JRSFYBzAMx/zUYCOGXnvNosTZGuwpy2oqQ1hclJz6xZszBt2jS88sorTh2nFdBoGQGYRICvBDAAwJ/gIsCdF3bsQIyVC2cursyVpZ49ezplHEKIiXnTWXkUAdPk4M50U+jWrZvJc2fsQOphQUyOxc3NjY4dO1a2bt263AsXLiSPGzeuaPXq1UHh4eEKAEhKSrL5xomJiamsrKzk3759WwBwKVQ+/vjj7MrKyvOpqamX8vPzkwYNGlQFcH5wjn1FtbFXSeMBqLFwvMbGPtcDeJ4QMo0QEkkI+QBACIBP7JxXu8DZjoptDWPnbWeuyAEuzcLUqVOd7s/XCnCUjDBEgFNKr1FKZwO4DS4CvMVjfL85c3Flvhtkrng4EuMACOPvlqMxVtKc+d6Z+/g6U0kzfr+cvWBsj0RGRirkcjkvPj5e5uXlpV6zZo1Fu7V54IAx//zzj5tIJKLmfmsCgQBdu3atcXV1pbt37/bp379/VXBwsOWabA7E3jv/dwAfEkKeppTmAAAhJBjABwB+s7YTSuk3hBBfcEWYAwFcBvAIpfSWnfNqcqiFornOwHhXS6vV1tPSOWO2ZoxNnG3lNbUCGi0j2kIEeFMFqpgrFs4MLjJ2H3Dm7mBTJYPu1KkThEIhamq4NUVYWJjTxjKWRe082XWjyMvL48fHx3d77rnniqKjo+VSqVRz6tQp948++ihg5MiRZRKJRLtp06ZbU6dODYuLi+s+a9as/MjISGV+fr5g165dPllZWS4JCQlpu3btkubm5gpjY2Mr3dzc6JEjRzxXrVoV/PTTTxeKxWIKALm5uYKvv/7ae+TIkRVyuZx89tlnfocPH/Y5fPhwckPzdAT2KmkzABwAkEEIuQ3O0T8EwL8AbEoJTSndAmCLnfNodprqR9842kStdrryDsD5Cui9996LQ4cOmUQ8OQPjVTjbhWwyHCEj7I0AX2LzbJ2EcXJOZ8oK81JJTVUpxJmKRlN9VwUCATp27IisLC5ZgDN3IY2TdjeV24q9aJpoA8KecaRSqTY6Orpq8+bNHTMzM0VqtZoEBASoJk6cWLhy5cpcAJg0aVJZSEhI8sqVKwOnTZsWVlVVxQ8ICFDFxMRUrF69OhsAXFxc6Oeff+6/ePHizlqtFp07d1bOmzcv58033ywwHm/Xrl2+S5cu7UQpxYABA6p++umn6/p0H87GrruEUnobwEBCyEgAEeAiMq9SSn915OQYd9Cv8oCmU9KcrYAOGTIEa9asaZIop/79+yM9Pd1hBa4Z9eNgGWFrBPh6o+eeaOZUPd26dUNGRoZT7z3zHbumygfYVlLaGCtMzjThenp64sEHH4RWq4WPj4/TxmkMPB6Peksk6rcuXmzSigM8Hs9qbU0sFtPNmzdnA8iur11sbGx1bGzszbrOjx8/XjZ+/Pir9fURGBiovnjxYpPsmlmiUR8CpfQXAL84aC6MejBW0oz/b80QQpySNNISGzZsgFqtdkquJUbdNFJG2BUBDsBQbbwlmLc//vhjVFVVOdVRvLmUpbayM92Ur2PRokVNNpY98Pl87PvxxyStVttkXx4ej0dbWv3SloJDNWVCyF0A3CilJxzZb0umqb7cbdHc2ZTw+fwWV8S4PWKLjKCUqggh+gjwfUanRoIzpbYKxGKxiRO8M2hKZbQ5vkdscdW06ORl2/kBaMU4WsP4GsAxB/fZopk4cSIA4NFHH23mmTielrALwWhz2CojWAR4CyM6OhqdO3d2Stkpc8aNGwexWIzx48c7dRwm6xgtFZt20gghbgDeAxAPQAjgVwCzKKVFuib36463Gx588EGEhoY6NSIIMN2xa6qVbFvaSWM0DY6WEW0hArytIRKJsHPnziZRbGbPno3p06c7vVJIWzHbMtoett6ZywBMAXAIwG5wZoeP9ScppTntTXjyeDxERkY6fTueJbNltBIcLiMopVsopaGUUhGlNLo9uVO0VJpSNjRFKbdnn30WADB69Ginj8Vg2IKtv/aPA/gvpXQPABBCdgI4SQjhU0o19V/KaAzGodvG/zuDYcOG4Y8//kB8fLxTx2G0SZiMYLQ67rvvPmzbts2p+eUYDHuwVUnrDK4sCwCAUppICFEDCAKXBZzhJJpyJ23u3Ll4+OGHMWjQIKeOw2iTMBnBaHUQQpxWSovBaAy2/trzAZjXqlLb0Q+jEThbSZNKpYiJiXHqGIw2C5MRDAaD4SBsFZwEwA5CiNLomCuATwghVfoDlNLHHTE5xh2kUikkEgkIIU4P52cwGgGTEc3EnDlzsH79erzyyivNPRUGg+EgbFXSvkLtTN//c9BcGPXg4uJiiKhq6eVEGO0aJiOaifj4eNx9990IDg5u7qkwWjkajQYsmW3LwKZfe0rpFCfNg2EFzixXwmA4AiYjmg9CiFNrdjLaBxqNBo89Fh9VXl7WZLsBUqmX+sCB/Um2KGrZ2dmCuXPnBh87dkxSXFwslEgkmoiIiOqlS5fmjBgxogoATp48KV6xYkVgYmKiZ2VlJT8wMFA1ePDgioULF+b169dPCQBTp07tnJiY6JGamioOCwtTJCcnm5SJun79uktERERf8/G/++671PHjx8sAICEhwXP06NG1nBrPnz9/ZcCAAQob3w4TbM2T9oUVzSil9L92zscuZDJZUw7HYLR4pFKpBEAFbeJkd0xGMBgtH6lUKjl9+rRFq4xWqyXl5WWC8fdMB484P3+clmrxfeIWgVarJbZUORgzZkw3tVpNtm7dmhEeHq7Mzs4WHD16VFJUVCQAgN27d0snT57cbejQobJt27alRUREKHNycoR79uzxXrBgQfChQ4fSAC4f6KRJk4oSExPdr1275lbXePv3708ZMGCAXP/c39+/VrR6UlLSZS8vL8PxoKCgRpcHslVTngLgFoAL4HxPmhtPAOjcuXNzz4PBaGmUA5ACaGrtZAqYjGAwWjrlV69evSs6OrrOBjzCA4/XBCZIre2XFBUV8c+fP++RkJBw/dFHH60EgJ49e6qGDx9eDQAVFRW86dOnhw4bNqz8l19+MRRYj4iIUMXFxVUVFRUZXtiOHTtuA8CcOXOE9SlpHTp0UIeEhNSrdAUFBan9/PwcmmrIViXtEwATAIQB+ALA/yilJY6ckI3kAOgEoKIZ58BgtFSa43vBZASD0Qro1atXUHPPwV6kUqnGzc1Nu3fvXu+4uLgqsVhssgO3b98+SVlZmWD+/Pl5lq63R5EaN25cd6VSyQsNDVXOnDkzf+rUqaXmbaKionqpVCrSvXt3xcKFC3NHjx7daLljq0/adELIa+ASVk4DsIoQcgjANgBHm9q0ohsvuynHZDAYdcNkBIPROjh37lyrVdKEQiE2b96c/uqrr4bu3LmzQ69evaqHDBlS8eyzz5YMGjRInpKSIgKAqKioRvmDAYBEItEuXbr0dmxsbCWfz8fevXu9nn/++TC5XJ4+ffr0EgDo3Lmzat26dbcGDRpUrVAoyI4dO3wfe+yxnocOHbr+8MMPVzZmfJsdAymlSnDlXnYTQrqAM29sASAkhPSilDZqQgwGo3XDZASDwXA2U6ZMKXvyySeTjhw54nny5En33377TfrJJ58ErF+/PoNS6jBXi8DAQPWSJUsK9M9jY2OrS0tL+Rs2bAjQK2lRUVHKqKgoQ9qhESNGVOXk5Li89957AQ8//PCNxozfWK9AqnsQB/TFYDDaHkxGMBgMp+Dm5kbHjh0rW7duXe6FCxeSx40bV7R69eqg8PBwBQAkJSW5OmPcwYMHV2VmZtbb9913312ZkZHR6KLeNgtNQoiIEPI0IeQXANcB9AUwA0AIWyEzGAwmIxgMRnMQGRmpkMvlvPj4eJmXl5d6zZo1AZbaGQcO2MOFCxfc/Pz8auprk5SU5Obv719vG2uwNQXHFnBOwZkAtgOYQCktbuwkGAxG24DJCAaD4Wzy8vL48fHx3Z577rmi6OhouVQq1Zw6dcr9o48+Chg5cmSZRCLRbtq06dbUqVPD4uLius+aNSs/MjJSmZ+fL9i1a5dPVlaWS0JCQhoAXL58WSSTyXh5eXkChULBO3XqlBgABg4cqHB1daWbNm3yFQqF9J577qnm8/n0hx9+8Nq+fbv/okWLsvTzeeedd/y7du2q6t+/v1ypVJLt27f7HjlyxHvHjh0363oN1mKrT9pL4IRvOoBhAIYRUtv0y0q+MBjtFiYjGIw2gJZq7UqPYdc4NiKVSrXR0dFVmzdv7piZmSlSq9UkICBANXHixMKVK1fmAsCkSZPKQkJCkleuXBk4bdq0sKqqKn5AQIAqJiamYvXq1YZgomnTpoWePXvWQ/88JiamFwAkJyf/Gx4ergKA9957LzAnJ8eFz+cjNDRU8eGHH2bo/dEAQKVSkbfeeqtTQUGBi0gk0nbv3l2+Z8+eG0899VR5I94aAACxJdiKELIDtUu+1IJSOrURc2IwGK0UJiMYjNbBuXPnIgQCwc89evSodHNzM0RBtpaKA62d6upq19TUVA+1Wv1QdHR0cl3tWFkoBoPhMJiMYDBaN3w+HwcO7E9itTtbBqxSN4PBYDAYDAN8Ph+2lGhiOA8WEs9gMBgMBoPRAmFKGoPBYDAYDEYLhClpDAaDwWAwGC0QpqQxGAwGg8FgtECYksZgMBgMBoPRAmFKGoPBYDAYDEYLhClpDAaDwWAwGC0QlieNwWAwGAyGAY1GA5bMtmXAlDQGg8FgMBgAdGWhxoyOKpdVNF1ZKImn+sCPB20qC5WdnS2YO3du8LFjxyTFxcVCiUSiiYiIqF66dGnOiBEjqgDg5MmT4hUrVgQmJiZ6VlZW8gMDA1WDBw+uWLhwYV6/fv2UADB16tTOiYmJHqmpqeKwsDBFcnLyVeNxrl+/7hIREdHXfPzvvvsudfz48TL9c7lcTubNmxf0ww8/+BQVFQk7duyoev3113Nnz55dbPcbA6akMRgMBoPB0KHVakm5rEJwf3cxSBPspVEK/HajQqDVaoktVQ7GjBnTTa1Wk61bt2aEh4crs7OzBUePHpUUFRUJAGD37t3SyZMndxs6dKhs27ZtaREREcqcnBzhnj17vBcsWBB86NChNG58ikmTJhUlJia6X7t2za2u8fbv358yYMAAuf65v7+/xvj8qFGjwoqKioRbtmzJiIyMVObk5AjUanWj30GmpDEYDAaDwTCBEIDXBFqaFrZXnyoqKuKfP3/eIyEh4fqjjz5aCQA9e/ZUDR8+vBoAKioqeNOnTw8dNmxY+S+//HJTf11ERIQqLi6uqqioyLBlt2PHjtsAMGfOHGF9SlqHDh3UISEhakvnvv/+e0liYqLnjRs3/u3YsaMGAMLDw1U2vzALsMABBoPBYDAYrQapVKpxc3PT7t2711sul9fSJPft2ycpKysTzJ8/P8/S9X5+fhpLx+tj3Lhx3X18fKIGDhwYsX37dm/jc/v37/fq27dv9bJlywL8/f37hYaG9vm///u/TpWVlWwnjcFgMBgMRvtBKBRi8+bN6a+++mrozp07O/Tq1at6yJAhFc8++2zJoEGD5CkpKSIAiIqKUjR2LIlEol26dOnt2NjYSj6fj71793o9//zzYXK5PH369OklAHDr1i3RuXPnPEQikfabb765UVBQIJwzZ05IaWmp4LvvvstozPhMSWMwGAwGg9GqmDJlStmTTz6ZdOTIEc+TJ0+6//bbb9JPPvkkYP369RmUUofZaQMDA9VLliwp0D+PjY2tLi0t5W/YsCFAr6RptVoQQvD999+n+/r6agBAoVDcnjp1arfKyspbHh4ettt0dTBzJ4PBYDAYjFaHm5sbHTt2rGzdunW5Fy5cSB43blzR6tWrg8LDwxUAkJSU5OqMcQcPHlyVmZlp6Ltjx441/v7+Kr2CBgB9+/ZVUEqRnp7u0pixmJLGYDAYDAaj1RMZGamQy+W8+Ph4mZeXl3rNmjUBltoZBw7Yw4ULF9z8/Pxq9M+HDBlSWVhYKCwvLzfoVFevXnXl8Xjo2rVrowIImLmTwWAwGAyGCZTaF3lpzzi2kpeXx4+Pj+/23HPPFUVHR8ulUqnm1KlT7h999FHAyJEjyyQSiXbTpk23pk6dGhYXF9d91qxZ+ZGRkcr8/HzBrl27fLKyslwSEhLSAODy5csimUzGy8vLEygUCt6pU6fEADBw4ECFq6sr3bRpk69QKKT33HNPNZ/Ppz/88IPX9u3b/RctWpSln88LL7xQsm7duqAJEyaErlixIqegoECwaNGiTk888URRY0ydAFPSGAwGg8Fg6ODxeFQq8VT/dqNpk9nyeDyrlRmpVKqNjo6u2rx5c8fMzEyRWq0mAQEBqokTJxauXLkyFwAmTZpUFhISkrxy5crAadOmhVVVVfEDAgJUMTExFatXr87W9zVt2rTQs2fPeuifx8TE9AKA5OTkf/VpNN57773AnJwcFz6fj9DQUMWHH36YofdH08/n8OHDKa+88krnoUOHRkqlUs3o0aNLPvjgg2w0EkLtUWMZDAaDwWC0Ws6dOxchEAh+7tGjR6Wbm5tJFCQrC+V8qqurXVNTUz3UavVD0dHRyXW1YztpDAaDwWAwDPD5fNiS/Z/hPFjgAIPBYDAYDEYLhClpDAaDwWAwGC0QpqQxGAwGg8FgtECYksZgMBgMRvtDC4A6Mjs/w3p07zsF9znUCVPSGAwGg8Fof+RRSmuqqqrcmnsi7ZGqqio3SmkNgNz62rEUHAwGg8FgtEPOnTu3SCgUvhwQEKByd3evJoQwhcDJUEpJVVWVW15enktNTc3H0dHRK+prz1JwMBgMBoPRPnm3pqYGOTk5zxFC3AAw06fzoZTSGo1Gsw3Auw01ZjtpDAaDwWC0Y86dO+cJIBDMBaop0ALIjY6OrrCmMVPSGAwGg8FgMFogTGtmMBgMBoPBaIEwJY3BYDAYDAajBcKUNAaDwWAwGIwWCFPSGAwGg8FgMFogTEljMBgMBoPBaIEwJY3BYDAYDAajBcKUNAaDwWAwGIwWyP8D0OhoO6bd2fUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1300x1700 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:covid_macrophages_integration]",
   "language": "python",
   "name": "conda-env-covid_macrophages_integration-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}