{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import anndata\n",
    "from nero import Harmonia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "demux = Harmonia.MuxOut(\"/covid/demux/JYCVPBMC4/merged/freemux_merged.clust1.samples.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['path', 'label', 'raw', 'muxtype', 'type', 'usefulcols', 'useful_cols_run', 'store_meta_run', 'data', 'num_drops', 'num_sng', 'num_dbl', 'num_amb', 'sngrate', 'dblrate', 'ambrate', 'sngrate_nonamb', 'dblrate_nonamb', 'inds'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demux.__dict__.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>NUM.SNPS</th>\n",
       "      <th>NUM.READS</th>\n",
       "      <th>DROPLET.TYPE</th>\n",
       "      <th>BEST.GUESS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BARCODE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GCATACAGTGGTACAG-0</th>\n",
       "      <td>215</td>\n",
       "      <td>226</td>\n",
       "      <td>SNG</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCTCCTATCCACTCCA-0</th>\n",
       "      <td>772</td>\n",
       "      <td>1378</td>\n",
       "      <td>SNG</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GTCTCGTAGTTCCACA-0</th>\n",
       "      <td>672</td>\n",
       "      <td>1030</td>\n",
       "      <td>SNG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCTATTACAGTTCATG-0</th>\n",
       "      <td>399</td>\n",
       "      <td>717</td>\n",
       "      <td>SNG</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GTCACAACAAGACGTG-0</th>\n",
       "      <td>1013</td>\n",
       "      <td>1783</td>\n",
       "      <td>SNG</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    NUM.SNPS  NUM.READS DROPLET.TYPE BEST.GUESS\n",
       "BARCODE                                                        \n",
       "GCATACAGTGGTACAG-0       215        226          SNG          9\n",
       "GCTCCTATCCACTCCA-0       772       1378          SNG         14\n",
       "GTCTCGTAGTTCCACA-0       672       1030          SNG          1\n",
       "CCTATTACAGTTCATG-0       399        717          SNG         18\n",
       "GTCACAACAAGACGTG-0      1013       1783          SNG          9"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demux.sng().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "demux.llk_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "demux.snp_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "demux.ind_hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SNG rate: 0.7830026262036767\n",
      "DBL rate: 0.21308724832214765\n",
      "AMB rate: 0.0039101254741756635\n"
     ]
    }
   ],
   "source": [
    "print(\"SNG rate: {}\\nDBL rate: {}\\nAMB rate: {}\".format(demux.sngrate,demux.dblrate,demux.ambrate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}