{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcParams['pdf.fonttype']=42\n",
    "rcParams['font.size']=14\n",
    "rcParams['font.family']='arial'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc_file = './Data/paralogs-3screens.foldchange.annotated.ctrl_only.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc = pd.read_csv(fc_file, index_col=0, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GENE</th>\n",
       "      <th>A549.T2A.Ex</th>\n",
       "      <th>A549.T2B.Ex</th>\n",
       "      <th>A549.T2C.Ex</th>\n",
       "      <th>HT29.T2A.Ex</th>\n",
       "      <th>HT29.T2B.Ex</th>\n",
       "      <th>HT29.T2C.Ex</th>\n",
       "      <th>OVCAR8.T2A.Ex</th>\n",
       "      <th>OVCAR8.T2B.Ex</th>\n",
       "      <th>OVCAR8.T2C.Ex</th>\n",
       "      <th>GENE1</th>\n",
       "      <th>GENE2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENE_CLONE</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>AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGGGGTATCCTCA</th>\n",
       "      <td>AARS.1:CDX4.2</td>\n",
       "      <td>-1.612</td>\n",
       "      <td>-1.280</td>\n",
       "      <td>-1.534</td>\n",
       "      <td>-2.291</td>\n",
       "      <td>-2.919</td>\n",
       "      <td>-3.031</td>\n",
       "      <td>-3.982</td>\n",
       "      <td>-3.185</td>\n",
       "      <td>-3.728</td>\n",
       "      <td>AARS</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGAATCTCCTGTA</th>\n",
       "      <td>AARS.2:F13B.3</td>\n",
       "      <td>-0.208</td>\n",
       "      <td>-0.223</td>\n",
       "      <td>0.141</td>\n",
       "      <td>-0.627</td>\n",
       "      <td>-0.827</td>\n",
       "      <td>-0.488</td>\n",
       "      <td>-1.848</td>\n",
       "      <td>-1.919</td>\n",
       "      <td>-1.996</td>\n",
       "      <td>AARS</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCCTCTACGGTAGG</th>\n",
       "      <td>AARS.3:SPEM1.1</td>\n",
       "      <td>-0.550</td>\n",
       "      <td>-0.489</td>\n",
       "      <td>-0.914</td>\n",
       "      <td>-3.025</td>\n",
       "      <td>-2.418</td>\n",
       "      <td>-3.121</td>\n",
       "      <td>-2.699</td>\n",
       "      <td>-2.076</td>\n",
       "      <td>-2.292</td>\n",
       "      <td>AARS</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCAGGTATGTGTGTCC</th>\n",
       "      <td>ABHD16A.1:GPX6.3</td>\n",
       "      <td>2.125</td>\n",
       "      <td>2.182</td>\n",
       "      <td>2.104</td>\n",
       "      <td>2.159</td>\n",
       "      <td>2.205</td>\n",
       "      <td>2.135</td>\n",
       "      <td>1.960</td>\n",
       "      <td>1.912</td>\n",
       "      <td>1.843</td>\n",
       "      <td>ABHD16A</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTGAACCATGCGCATC</th>\n",
       "      <td>ABHD16A.2:GSX2.3</td>\n",
       "      <td>0.866</td>\n",
       "      <td>-1.456</td>\n",
       "      <td>1.272</td>\n",
       "      <td>0.701</td>\n",
       "      <td>0.519</td>\n",
       "      <td>1.460</td>\n",
       "      <td>0.976</td>\n",
       "      <td>-0.636</td>\n",
       "      <td>0.229</td>\n",
       "      <td>ABHD16A</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                GENE  \\\n",
       "GENE_CLONE                                                             \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG...     AARS.1:CDX4.2   \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA...     AARS.2:F13B.3   \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC...    AARS.3:SPEM1.1   \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...  ABHD16A.1:GPX6.3   \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...  ABHD16A.2:GSX2.3   \n",
       "\n",
       "                                                    A549.T2A.Ex  A549.T2B.Ex  \\\n",
       "GENE_CLONE                                                                     \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG...       -1.612       -1.280   \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA...       -0.208       -0.223   \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC...       -0.550       -0.489   \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...        2.125        2.182   \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...        0.866       -1.456   \n",
       "\n",
       "                                                    A549.T2C.Ex  HT29.T2A.Ex  \\\n",
       "GENE_CLONE                                                                     \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG...       -1.534       -2.291   \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA...        0.141       -0.627   \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC...       -0.914       -3.025   \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...        2.104        2.159   \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...        1.272        0.701   \n",
       "\n",
       "                                                    HT29.T2B.Ex  HT29.T2C.Ex  \\\n",
       "GENE_CLONE                                                                     \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG...       -2.919       -3.031   \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA...       -0.827       -0.488   \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC...       -2.418       -3.121   \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...        2.205        2.135   \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...        0.519        1.460   \n",
       "\n",
       "                                                    OVCAR8.T2A.Ex  \\\n",
       "GENE_CLONE                                                          \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG...         -3.982   \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA...         -1.848   \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC...         -2.699   \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...          1.960   \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...          0.976   \n",
       "\n",
       "                                                    OVCAR8.T2B.Ex  \\\n",
       "GENE_CLONE                                                          \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG...         -3.185   \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA...         -1.919   \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC...         -2.076   \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...          1.912   \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...         -0.636   \n",
       "\n",
       "                                                    OVCAR8.T2C.Ex    GENE1  \\\n",
       "GENE_CLONE                                                                   \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG...         -3.728     AARS   \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA...         -1.996     AARS   \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC...         -2.292     AARS   \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...          1.843  ABHD16A   \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...          0.229  ABHD16A   \n",
       "\n",
       "                                                   GENE2  \n",
       "GENE_CLONE                                                \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG...  CTRL  \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA...  CTRL  \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC...  CTRL  \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...  CTRL  \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...  CTRL  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 12328 entries, AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGGGGTATCCTCA to ZSWIM6_GCGGCAACAAGGCACTGCAATGA_ZSWIM4_GTCGTTTCCACGCAGTGAACGGG\n",
      "Data columns (total 12 columns):\n",
      "GENE             12328 non-null object\n",
      "A549.T2A.Ex      12328 non-null float64\n",
      "A549.T2B.Ex      12328 non-null float64\n",
      "A549.T2C.Ex      12328 non-null float64\n",
      "HT29.T2A.Ex      12328 non-null float64\n",
      "HT29.T2B.Ex      12328 non-null float64\n",
      "HT29.T2C.Ex      12328 non-null float64\n",
      "OVCAR8.T2A.Ex    12328 non-null float64\n",
      "OVCAR8.T2B.Ex    12328 non-null float64\n",
      "OVCAR8.T2C.Ex    12328 non-null float64\n",
      "GENE1            12328 non-null object\n",
      "GENE2            12328 non-null object\n",
      "dtypes: float64(9), object(3)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "fc.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Essential controls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Mouse</th>\n",
       "      <th>Rat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AARS</th>\n",
       "      <td>Aars</td>\n",
       "      <td>Aars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARCN1</th>\n",
       "      <td>Arcn1</td>\n",
       "      <td>Arcn1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATP6V1A</th>\n",
       "      <td>Atp6v1a</td>\n",
       "      <td>Atp6v1a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUD31</th>\n",
       "      <td>Bud31</td>\n",
       "      <td>Bud31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCT2</th>\n",
       "      <td>Cct2</td>\n",
       "      <td>Cct2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Mouse      Rat\n",
       "Human                    \n",
       "AARS        Aars     Aars\n",
       "ARCN1      Arcn1    Arcn1\n",
       "ATP6V1A  Atp6v1a  Atp6v1a\n",
       "BUD31      Bud31    Bud31\n",
       "CCT2        Cct2     Cct2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ess = pd.read_csv('pan-species-control-essentials-50genes.txt', sep='\\t', index_col=0)\n",
    "ess.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "298"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_essential = where( fc['GENE2'].isin(ess.index) | fc['GENE1'].isin(ess.index) )[0]\n",
    "len(is_essential)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12030"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_other = where( ~fc['GENE2'].isin(ess.index) & ~fc['GENE1'].isin(ess.index) )[0]\n",
    "len(is_other)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx = linspace(-7, 3, 200)\n",
    "for sample in fc.columns[1:-2]:\n",
    "    kall = stats.gaussian_kde( fc.iloc[is_other][sample]  )\n",
    "    kess = stats.gaussian_kde( fc.iloc[is_essential][sample] )\n",
    "    figure( figsize(4,3))\n",
    "    plot( xx, kess.evaluate(xx), c='r', linewidth=2, label='essential')\n",
    "    plot( xx, kall.evaluate(xx), c='b', linewidth=2, label='other')\n",
    "    title(sample)\n",
    "    #savefig( 'kde_' + sample + '.pdf')\n",
    "    show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GENE</th>\n",
       "      <th>meanFC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENE_CLONE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGGGGTATCCTCA</th>\n",
       "      <td>AARS.1:CDX4.2</td>\n",
       "      <td>-1.475333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGAATCTCCTGTA</th>\n",
       "      <td>AARS.2:F13B.3</td>\n",
       "      <td>-0.096667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCCTCTACGGTAGG</th>\n",
       "      <td>AARS.3:SPEM1.1</td>\n",
       "      <td>-0.651000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCAGGTATGTGTGTCC</th>\n",
       "      <td>ABHD16A.1:GPX6.3</td>\n",
       "      <td>2.137000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTGAACCATGCGCATC</th>\n",
       "      <td>ABHD16A.2:GSX2.3</td>\n",
       "      <td>0.227333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                GENE    meanFC\n",
       "GENE_CLONE                                                                    \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG...     AARS.1:CDX4.2 -1.475333\n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA...     AARS.2:F13B.3 -0.096667\n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC...    AARS.3:SPEM1.1 -0.651000\n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...  ABHD16A.1:GPX6.3  2.137000\n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...  ABHD16A.2:GSX2.3  0.227333"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc_a549 = pd.DataFrame( index=fc.index, columns = ['GENE','meanFC'] )\n",
    "fc_a549.GENE = fc.GENE\n",
    "fc_a549.meanFC = fc[['A549.T2A.Ex','A549.T2B.Ex','A549.T2C.Ex']].mean(1)\n",
    "fc_a549.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kall = stats.gaussian_kde( fc_a549.iloc[is_other]['meanFC']  )\n",
    "kess = stats.gaussian_kde( fc_a549.iloc[is_essential]['meanFC'] )\n",
    "figure( figsize(4,3))\n",
    "plot( xx, kess.evaluate(xx), c='r', linewidth=2, label='essential')\n",
    "plot( xx, kall.evaluate(xx), c='b', linewidth=2, label='other')\n",
    "title('A549')\n",
    "#savefig( 'kde_A549_mean3repl.pdf')\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## merge replicates by mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 12328 entries, AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGGGGTATCCTCA to ZSWIM6_GCGGCAACAAGGCACTGCAATGA_ZSWIM4_GTCGTTTCCACGCAGTGAACGGG\n",
      "Data columns (total 4 columns):\n",
      "GENE      0 non-null float64\n",
      "A549      0 non-null float64\n",
      "HT29      0 non-null float64\n",
      "OVCAR8    0 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 481.6+ KB\n"
     ]
    }
   ],
   "source": [
    "## Merge replicates by mean\n",
    "cells = list(['A549','HT29','OVCAR8'])\n",
    "cols = list(['GENE']) + cells\n",
    "fc_merge = pd.DataFrame( columns=cols, index=fc.index, dtype=float)\n",
    "fc_merge.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc_merge.GENE = fc.GENE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GENE</th>\n",
       "      <th>A549</th>\n",
       "      <th>HT29</th>\n",
       "      <th>OVCAR8</th>\n",
       "      <th>GENE1</th>\n",
       "      <th>GENE2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENE_CLONE</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>AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGGGGTATCCTCA</th>\n",
       "      <td>AARS.1:CDX4.2</td>\n",
       "      <td>-1.475333</td>\n",
       "      <td>-2.747000</td>\n",
       "      <td>-3.631667</td>\n",
       "      <td>AARS</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGAATCTCCTGTA</th>\n",
       "      <td>AARS.2:F13B.3</td>\n",
       "      <td>-0.096667</td>\n",
       "      <td>-0.647333</td>\n",
       "      <td>-1.921000</td>\n",
       "      <td>AARS</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCCTCTACGGTAGG</th>\n",
       "      <td>AARS.3:SPEM1.1</td>\n",
       "      <td>-0.651000</td>\n",
       "      <td>-2.854667</td>\n",
       "      <td>-2.355667</td>\n",
       "      <td>AARS</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCAGGTATGTGTGTCC</th>\n",
       "      <td>ABHD16A.1:GPX6.3</td>\n",
       "      <td>2.137000</td>\n",
       "      <td>2.166333</td>\n",
       "      <td>1.905000</td>\n",
       "      <td>ABHD16A</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTGAACCATGCGCATC</th>\n",
       "      <td>ABHD16A.2:GSX2.3</td>\n",
       "      <td>0.227333</td>\n",
       "      <td>0.893333</td>\n",
       "      <td>0.189667</td>\n",
       "      <td>ABHD16A</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                GENE  \\\n",
       "GENE_CLONE                                                             \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG...     AARS.1:CDX4.2   \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA...     AARS.2:F13B.3   \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC...    AARS.3:SPEM1.1   \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...  ABHD16A.1:GPX6.3   \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...  ABHD16A.2:GSX2.3   \n",
       "\n",
       "                                                        A549      HT29  \\\n",
       "GENE_CLONE                                                               \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG... -1.475333 -2.747000   \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA... -0.096667 -0.647333   \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC... -0.651000 -2.854667   \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...  2.137000  2.166333   \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...  0.227333  0.893333   \n",
       "\n",
       "                                                      OVCAR8    GENE1 GENE2  \n",
       "GENE_CLONE                                                                   \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG... -3.631667     AARS  CTRL  \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA... -1.921000     AARS  CTRL  \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC... -2.355667     AARS  CTRL  \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...  1.905000  ABHD16A  CTRL  \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...  0.189667  ABHD16A  CTRL  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for cell in cells:\n",
    "    samples = [x for x in fc.columns if cell in x]\n",
    "    fc_merge[cell] = fc[ samples ].mean(1)\n",
    "fc_merge['GENE1'] = fc.GENE1\n",
    "fc_merge['GENE2'] = fc.GENE2\n",
    "fc_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GENE</th>\n",
       "      <th>A549</th>\n",
       "      <th>HT29</th>\n",
       "      <th>OVCAR8</th>\n",
       "      <th>GENE1</th>\n",
       "      <th>GENE2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENE_CLONE</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>CXorf66_GATATGATGGCCTAGTTAACTTT_MAP3K10_ATGACCTTCGGACCAAGGAGAAG</th>\n",
       "      <td>CXorf66.2:MAP3K10.2</td>\n",
       "      <td>2.686000</td>\n",
       "      <td>2.615000</td>\n",
       "      <td>2.582333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>MAP3K10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARID1A_CAGGGATGGCAGCCAGCCCAGAG_ARID1B_GGGAAGGATGAATCACTCACATC</th>\n",
       "      <td>ARID1A.1:ARID1B.3</td>\n",
       "      <td>1.109000</td>\n",
       "      <td>2.589000</td>\n",
       "      <td>0.807667</td>\n",
       "      <td>ARID1A</td>\n",
       "      <td>ARID1B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAX_CCTGGACCCGGACCTCTGGTAGG_PUM1_GTTGCAAACTCCAACACTGGCAG</th>\n",
       "      <td>RAX.3:PUM1.2</td>\n",
       "      <td>-0.174667</td>\n",
       "      <td>2.568667</td>\n",
       "      <td>1.698000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>PUM1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAX_CCTGGACCCGGACCTCTGGTAGG_AGAP1_AGATGCCTTCGTGAACAGCCAGG</th>\n",
       "      <td>RAX.3:AGAP1.1</td>\n",
       "      <td>2.005000</td>\n",
       "      <td>2.539333</td>\n",
       "      <td>1.211667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>AGAP1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KLC1_CCCAATGATGAAGACGACCCAGG_KLC4_GCTGTGCTCTATGGCAAAAGGGG</th>\n",
       "      <td>KLC1.1:KLC4.1</td>\n",
       "      <td>2.816667</td>\n",
       "      <td>2.451333</td>\n",
       "      <td>2.332333</td>\n",
       "      <td>KLC1</td>\n",
       "      <td>KLC4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   GENE  \\\n",
       "GENE_CLONE                                                                \n",
       "CXorf66_GATATGATGGCCTAGTTAACTTT_MAP3K10_ATGACCT...  CXorf66.2:MAP3K10.2   \n",
       "ARID1A_CAGGGATGGCAGCCAGCCCAGAG_ARID1B_GGGAAGGAT...    ARID1A.1:ARID1B.3   \n",
       "RAX_CCTGGACCCGGACCTCTGGTAGG_PUM1_GTTGCAAACTCCAA...         RAX.3:PUM1.2   \n",
       "RAX_CCTGGACCCGGACCTCTGGTAGG_AGAP1_AGATGCCTTCGTG...        RAX.3:AGAP1.1   \n",
       "KLC1_CCCAATGATGAAGACGACCCAGG_KLC4_GCTGTGCTCTATG...        KLC1.1:KLC4.1   \n",
       "\n",
       "                                                        A549      HT29  \\\n",
       "GENE_CLONE                                                               \n",
       "CXorf66_GATATGATGGCCTAGTTAACTTT_MAP3K10_ATGACCT...  2.686000  2.615000   \n",
       "ARID1A_CAGGGATGGCAGCCAGCCCAGAG_ARID1B_GGGAAGGAT...  1.109000  2.589000   \n",
       "RAX_CCTGGACCCGGACCTCTGGTAGG_PUM1_GTTGCAAACTCCAA... -0.174667  2.568667   \n",
       "RAX_CCTGGACCCGGACCTCTGGTAGG_AGAP1_AGATGCCTTCGTG...  2.005000  2.539333   \n",
       "KLC1_CCCAATGATGAAGACGACCCAGG_KLC4_GCTGTGCTCTATG...  2.816667  2.451333   \n",
       "\n",
       "                                                      OVCAR8   GENE1    GENE2  \n",
       "GENE_CLONE                                                                     \n",
       "CXorf66_GATATGATGGCCTAGTTAACTTT_MAP3K10_ATGACCT...  2.582333    CTRL  MAP3K10  \n",
       "ARID1A_CAGGGATGGCAGCCAGCCCAGAG_ARID1B_GGGAAGGAT...  0.807667  ARID1A   ARID1B  \n",
       "RAX_CCTGGACCCGGACCTCTGGTAGG_PUM1_GTTGCAAACTCCAA...  1.698000    CTRL     PUM1  \n",
       "RAX_CCTGGACCCGGACCTCTGGTAGG_AGAP1_AGATGCCTTCGTG...  1.211667    CTRL    AGAP1  \n",
       "KLC1_CCCAATGATGAAGACGACCCAGG_KLC4_GCTGTGCTCTATG...  2.332333    KLC1     KLC4  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc_merge.nlargest(5, 'HT29')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc_merge.to_csv('./Data/fc_replicateMeans_12kPairs_annotated.txt', sep='\\t', float_format='%5.4f', index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get SMF for each gene (gene-CTRL pairs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5124"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_ctrl = where( (fc_merge.GENE1=='CTRL') | (fc_merge.GENE2=='CTRL') )[0]\n",
    "len(is_ctrl)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GENE</th>\n",
       "      <th>A549</th>\n",
       "      <th>HT29</th>\n",
       "      <th>OVCAR8</th>\n",
       "      <th>GENE1</th>\n",
       "      <th>GENE2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENE_CLONE</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>AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGGGGTATCCTCA</th>\n",
       "      <td>AARS.1:CDX4.2</td>\n",
       "      <td>-1.475333</td>\n",
       "      <td>-2.747000</td>\n",
       "      <td>-3.631667</td>\n",
       "      <td>AARS</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGAATCTCCTGTA</th>\n",
       "      <td>AARS.2:F13B.3</td>\n",
       "      <td>-0.096667</td>\n",
       "      <td>-0.647333</td>\n",
       "      <td>-1.921000</td>\n",
       "      <td>AARS</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCCTCTACGGTAGG</th>\n",
       "      <td>AARS.3:SPEM1.1</td>\n",
       "      <td>-0.651000</td>\n",
       "      <td>-2.854667</td>\n",
       "      <td>-2.355667</td>\n",
       "      <td>AARS</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCAGGTATGTGTGTCC</th>\n",
       "      <td>ABHD16A.1:GPX6.3</td>\n",
       "      <td>2.137000</td>\n",
       "      <td>2.166333</td>\n",
       "      <td>1.905000</td>\n",
       "      <td>ABHD16A</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTGAACCATGCGCATC</th>\n",
       "      <td>ABHD16A.2:GSX2.3</td>\n",
       "      <td>0.227333</td>\n",
       "      <td>0.893333</td>\n",
       "      <td>0.189667</td>\n",
       "      <td>ABHD16A</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                GENE  \\\n",
       "GENE_CLONE                                                             \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG...     AARS.1:CDX4.2   \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA...     AARS.2:F13B.3   \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC...    AARS.3:SPEM1.1   \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...  ABHD16A.1:GPX6.3   \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...  ABHD16A.2:GSX2.3   \n",
       "\n",
       "                                                        A549      HT29  \\\n",
       "GENE_CLONE                                                               \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG... -1.475333 -2.747000   \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA... -0.096667 -0.647333   \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC... -0.651000 -2.854667   \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...  2.137000  2.166333   \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...  0.227333  0.893333   \n",
       "\n",
       "                                                      OVCAR8    GENE1 GENE2  \n",
       "GENE_CLONE                                                                   \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG... -3.631667     AARS  CTRL  \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA... -1.921000     AARS  CTRL  \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC... -2.355667     AARS  CTRL  \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...  1.905000  ABHD16A  CTRL  \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...  0.189667  ABHD16A  CTRL  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc_merge.iloc[is_ctrl].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2562\n",
      "2562\n"
     ]
    }
   ],
   "source": [
    "is_ctrl1 = where( fc_merge.GENE1=='CTRL' )[0]\n",
    "print( len(is_ctrl1) )\n",
    "is_ctrl2 = where( fc_merge.GENE2=='CTRL' )[0]\n",
    "print( len(is_ctrl2) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "smf_gene1 = fc_merge.iloc[is_ctrl2].groupby('GENE1').mean()\n",
    "smf_gene2 = fc_merge.iloc[is_ctrl1].groupby('GENE2').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GENE</th>\n",
       "      <th>A549</th>\n",
       "      <th>HT29</th>\n",
       "      <th>OVCAR8</th>\n",
       "      <th>GENE1</th>\n",
       "      <th>GENE2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENE_CLONE</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>AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGGGGTATCCTCA</th>\n",
       "      <td>AARS.1:CDX4.2</td>\n",
       "      <td>-1.475333</td>\n",
       "      <td>-2.747000</td>\n",
       "      <td>-3.631667</td>\n",
       "      <td>AARS</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGAATCTCCTGTA</th>\n",
       "      <td>AARS.2:F13B.3</td>\n",
       "      <td>-0.096667</td>\n",
       "      <td>-0.647333</td>\n",
       "      <td>-1.921000</td>\n",
       "      <td>AARS</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCCTCTACGGTAGG</th>\n",
       "      <td>AARS.3:SPEM1.1</td>\n",
       "      <td>-0.651000</td>\n",
       "      <td>-2.854667</td>\n",
       "      <td>-2.355667</td>\n",
       "      <td>AARS</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCAGGTATGTGTGTCC</th>\n",
       "      <td>ABHD16A.1:GPX6.3</td>\n",
       "      <td>2.137000</td>\n",
       "      <td>2.166333</td>\n",
       "      <td>1.905000</td>\n",
       "      <td>ABHD16A</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTGAACCATGCGCATC</th>\n",
       "      <td>ABHD16A.2:GSX2.3</td>\n",
       "      <td>0.227333</td>\n",
       "      <td>0.893333</td>\n",
       "      <td>0.189667</td>\n",
       "      <td>ABHD16A</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD16A_GCAGGATACGTACTATCAGCCCC_UBQLN3_GCGGCAATCCCTTTGCCACTGCC</th>\n",
       "      <td>ABHD16A.3:UBQLN3.3</td>\n",
       "      <td>0.193000</td>\n",
       "      <td>0.258333</td>\n",
       "      <td>0.128333</td>\n",
       "      <td>ABHD16A</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD4_AAGTCCGGCCGGAATCGCTGCAC_BMP15_CCGCCATCATCTCCAACTAACTC</th>\n",
       "      <td>ABHD4.1:BMP15.2</td>\n",
       "      <td>0.259000</td>\n",
       "      <td>0.306000</td>\n",
       "      <td>0.101333</td>\n",
       "      <td>ABHD4</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD4_CCCTCCGACCAACTAACCCCAGT_GABRA1_GGACCCGTTTCAGACCATGATAT</th>\n",
       "      <td>ABHD4.2:GABRA1.2</td>\n",
       "      <td>-0.646667</td>\n",
       "      <td>-0.377333</td>\n",
       "      <td>-0.393000</td>\n",
       "      <td>ABHD4</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD4_GGAGGGATACATATCTGGCCAGG_PLA2G2F_CTCACCGCTGCCAGCAAGGATGG</th>\n",
       "      <td>ABHD4.3:PLA2G2F.2</td>\n",
       "      <td>0.567667</td>\n",
       "      <td>0.403000</td>\n",
       "      <td>0.287333</td>\n",
       "      <td>ABHD4</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD5_GACGAAGTAGTAGACCCAGGTTT_FMR1NB_ACTGTTGGGCAGGATATGTCCAT</th>\n",
       "      <td>ABHD5.3:FMR1NB.2</td>\n",
       "      <td>-0.065333</td>\n",
       "      <td>-0.405667</td>\n",
       "      <td>-0.299333</td>\n",
       "      <td>ABHD5</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD5_CCTGAACGACCAGACCTTGCTGA_KLK12_AGGGCACCAGCCTGCGCTGCGGG</th>\n",
       "      <td>ABHD5.2:KLK12.2</td>\n",
       "      <td>0.566333</td>\n",
       "      <td>0.069667</td>\n",
       "      <td>0.196000</td>\n",
       "      <td>ABHD5</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD5_ATTAGGTGTGCCTTGCACATACA_PLA2G2F_GTCCTCCCTCAGGTCTAGCCTGG</th>\n",
       "      <td>ABHD5.1:PLA2G2F.3</td>\n",
       "      <td>-0.478000</td>\n",
       "      <td>-0.123667</td>\n",
       "      <td>-0.164333</td>\n",
       "      <td>ABHD5</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABL1_GGCTTCACACCATTCCCCATTGT_ADH7_GCCACAGGAATCTGTCGCACAGA</th>\n",
       "      <td>ABL1.3:ADH7.3</td>\n",
       "      <td>-0.014000</td>\n",
       "      <td>0.324333</td>\n",
       "      <td>0.217000</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABL1_CCGTCTGAGATACTGGATTCCTG_RXFP3_GTCCCCGCCGCGCGGCGGTCGGC</th>\n",
       "      <td>ABL1.1:RXFP3.2</td>\n",
       "      <td>0.088667</td>\n",
       "      <td>0.148667</td>\n",
       "      <td>0.110333</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABL1_GCCTGAGCAGGTTGATGACAGGG_SLC36A3_AAGGCCGTACATCGTGGCCTCTC</th>\n",
       "      <td>ABL1.2:SLC36A3.1</td>\n",
       "      <td>0.809333</td>\n",
       "      <td>0.724667</td>\n",
       "      <td>0.616667</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABL2_ACAGCAACTGTAAGGCTGTATTT_GSX2_CCCGCGGGTGAACCATGCGCATC</th>\n",
       "      <td>ABL2.1:GSX2.3</td>\n",
       "      <td>-0.454333</td>\n",
       "      <td>-0.621667</td>\n",
       "      <td>-0.247000</td>\n",
       "      <td>ABL2</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABL2_CTGGATTACCTCCGAGAATGCAA_RXFP3_GTCCCCGCCGCGCGGCGGTCGGC</th>\n",
       "      <td>ABL2.2:RXFP3.2</td>\n",
       "      <td>0.179667</td>\n",
       "      <td>0.167667</td>\n",
       "      <td>0.139667</td>\n",
       "      <td>ABL2</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABL2_GCTCCAGCATGAGCAGTATAAGT_UBQLN3_GCGGCAATCCCTTTGCCACTGCC</th>\n",
       "      <td>ABL2.3:UBQLN3.3</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.242667</td>\n",
       "      <td>0.207000</td>\n",
       "      <td>ABL2</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABR_GCAGGAACTCAAAGTGAAAGGTC_APOBEC1_CCTGAGCTTCGCCAGATCTTCCG</th>\n",
       "      <td>ABR.2:APOBEC1.2</td>\n",
       "      <td>0.442000</td>\n",
       "      <td>0.486333</td>\n",
       "      <td>0.218333</td>\n",
       "      <td>ABR</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABR_CAGGAAGCACCAGCAGTATGACT_BMP15_AATGATACTCATAAAAGCATTCG</th>\n",
       "      <td>ABR.1:BMP15.1</td>\n",
       "      <td>-0.807333</td>\n",
       "      <td>-0.112000</td>\n",
       "      <td>0.286333</td>\n",
       "      <td>ABR</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABR_TCCCAGCCCATGAAACCCCTGAA_GPR50_CGATAGGCAGAGGAGGATCTGGA</th>\n",
       "      <td>ABR.3:GPR50.2</td>\n",
       "      <td>0.196000</td>\n",
       "      <td>0.321667</td>\n",
       "      <td>-0.044333</td>\n",
       "      <td>ABR</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACAP2_CTCTGATATATGCCTCCTTCTCC_GABRA1_GGACCCGTTTCAGACCATGATAT</th>\n",
       "      <td>ACAP2.2:GABRA1.2</td>\n",
       "      <td>-0.410333</td>\n",
       "      <td>-0.320333</td>\n",
       "      <td>-0.170333</td>\n",
       "      <td>ACAP2</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACAP2_CGACACATAGCCCTCGATTATGT_GFRAL_TCTAGGAGGCACTATAGAACATT</th>\n",
       "      <td>ACAP2.1:GFRAL.3</td>\n",
       "      <td>-0.198667</td>\n",
       "      <td>-0.150000</td>\n",
       "      <td>0.156000</td>\n",
       "      <td>ACAP2</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACAP2_TGGTAAACCAACTGATTATTCTG_SERPINB12_GACAGAAAGGTGCATCCACCGTG</th>\n",
       "      <td>ACAP2.3:SERPINB12.2</td>\n",
       "      <td>0.049333</td>\n",
       "      <td>0.382000</td>\n",
       "      <td>0.349000</td>\n",
       "      <td>ACAP2</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACAP3_GTGAGCGGCGTCCGCGACCTGTC_GABRA1_GACGTGATCCATCTTCTGCTACA</th>\n",
       "      <td>ACAP3.2:GABRA1.1</td>\n",
       "      <td>-0.253333</td>\n",
       "      <td>-0.456667</td>\n",
       "      <td>-0.585000</td>\n",
       "      <td>ACAP3</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACAP3_TGGGGCTGACCTGGAGCACATAG_GABRA1_GGACCCGTTTCAGACCATGATAT</th>\n",
       "      <td>ACAP3.3:GABRA1.2</td>\n",
       "      <td>-0.330000</td>\n",
       "      <td>-0.048000</td>\n",
       "      <td>-0.149333</td>\n",
       "      <td>ACAP3</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACAP3_CACCGCAGAGTCGATCACCAGCT_KLK12_AATGGCACTGAGTGTGGGCGTAA</th>\n",
       "      <td>ACAP3.1:KLK12.1</td>\n",
       "      <td>-0.810333</td>\n",
       "      <td>-0.578667</td>\n",
       "      <td>-0.553667</td>\n",
       "      <td>ACAP3</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTG1_AGACCTTCAACACCCCGGCCATG_DSPP_GCCAGGTCCTTCTATGTTGACAA</th>\n",
       "      <td>ACTG1.1:DSPP.3</td>\n",
       "      <td>-3.775667</td>\n",
       "      <td>-4.219000</td>\n",
       "      <td>-3.743333</td>\n",
       "      <td>ACTG1</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTG1_CTTCCATCGTCGGGCGCCCCAGA_PLA2G2F_CTCACCGCTGCCAGCAAGGATGG</th>\n",
       "      <td>ACTG1.3:PLA2G2F.2</td>\n",
       "      <td>-1.317333</td>\n",
       "      <td>0.044333</td>\n",
       "      <td>0.221667</td>\n",
       "      <td>ACTG1</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTG1_CGGGCAGGTCGCAATGGAAGAAG_SERPINA7_GGGCCTGCTGCAGCACCCAAACT</th>\n",
       "      <td>ACTG1.2:SERPINA7.2</td>\n",
       "      <td>-0.643667</td>\n",
       "      <td>0.045667</td>\n",
       "      <td>0.096000</td>\n",
       "      <td>ACTG1</td>\n",
       "      <td>CTRL</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF764_CCTGGCGCTCCACACTGGTGGAG_CXorf66_GATATGATGGCCTAGTTAACTTT</th>\n",
       "      <td>ZNF764.1:CXorf66.2</td>\n",
       "      <td>-0.185000</td>\n",
       "      <td>-0.019333</td>\n",
       "      <td>-0.149667</td>\n",
       "      <td>ZNF764</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF764_CCTGGCGCTCCACACTGGTGGAG_HTR5A_AGGATTGCTGAGATACCCACCTC</th>\n",
       "      <td>ZNF764.1:HTR5A.1</td>\n",
       "      <td>-0.290333</td>\n",
       "      <td>-0.143333</td>\n",
       "      <td>0.197000</td>\n",
       "      <td>ZNF764</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF764_TGTCTGACATTTCGCCACCTCCG_NDST4_GATGTGTCAATAGGTTTAACTGT</th>\n",
       "      <td>ZNF764.3:NDST4.3</td>\n",
       "      <td>0.242333</td>\n",
       "      <td>0.318333</td>\n",
       "      <td>0.591333</td>\n",
       "      <td>ZNF764</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF76_CCGCAGCCTGGCACCGTGCAAAC_BPIFB6_TTGCCTGCCTCGGCTGCCATCTT</th>\n",
       "      <td>ZNF76.3:BPIFB6.3</td>\n",
       "      <td>0.138000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>-0.135333</td>\n",
       "      <td>ZNF76</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF76_AGTGATGAGCGGTGGTGTAGAGA_OPN5_CGTAGGAGAACACGATCACAGCC</th>\n",
       "      <td>ZNF76.1:OPN5.1</td>\n",
       "      <td>-1.379000</td>\n",
       "      <td>0.213667</td>\n",
       "      <td>0.277000</td>\n",
       "      <td>ZNF76</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF76_CATAGGTGAACGCCCGTTCCAGT_PDILT_GACGGATGGGATATCGACCTCTG</th>\n",
       "      <td>ZNF76.2:PDILT.2</td>\n",
       "      <td>-0.173667</td>\n",
       "      <td>-0.330667</td>\n",
       "      <td>-0.229000</td>\n",
       "      <td>ZNF76</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF773_AGGAGTTGCAGAGTCCACCTATC_APOBEC1_GTAGAGCAGACAGGCCTCTTTAC</th>\n",
       "      <td>ZNF773.2:APOBEC1.3</td>\n",
       "      <td>0.582000</td>\n",
       "      <td>0.602000</td>\n",
       "      <td>0.434667</td>\n",
       "      <td>ZNF773</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF773_AACTGGGCACTTCTACAGAAGCA_CLRN1_AAACTCTGCATGGTCCCCTAGGG</th>\n",
       "      <td>ZNF773.1:CLRN1.1</td>\n",
       "      <td>0.371667</td>\n",
       "      <td>0.109667</td>\n",
       "      <td>0.188000</td>\n",
       "      <td>ZNF773</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF773_GCCGTAATGCTGACCTCATTCAA_CST9_TGTCCATGCTATCCTCCCTCCAT</th>\n",
       "      <td>ZNF773.3:CST9.3</td>\n",
       "      <td>-0.022333</td>\n",
       "      <td>-0.088667</td>\n",
       "      <td>0.464000</td>\n",
       "      <td>ZNF773</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF77_ACAGAGTCTCCCCAGCTGACTTC_APOBEC1_GTAGAGCAGACAGGCCTCTTTAC</th>\n",
       "      <td>ZNF77.1:APOBEC1.3</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.057667</td>\n",
       "      <td>0.541000</td>\n",
       "      <td>ZNF77</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF77_TGTTAGAACCAGTGGATCAAGTT_CLRN1_ACCCATAAACTTGTCCAGCTCCT</th>\n",
       "      <td>ZNF77.3:CLRN1.2</td>\n",
       "      <td>0.187000</td>\n",
       "      <td>-0.075333</td>\n",
       "      <td>0.052333</td>\n",
       "      <td>ZNF77</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF77_GCTTCGGTAGGGTAACTCTTGTG_SERPINB12_CCACACGACGATTGAAAGTGTTG</th>\n",
       "      <td>ZNF77.2:SERPINB12.1</td>\n",
       "      <td>-0.306000</td>\n",
       "      <td>0.272333</td>\n",
       "      <td>-0.449000</td>\n",
       "      <td>ZNF77</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF785_AGCAGGGGCCAAGGACAGGAAGC_CDX4_TCGCACTATATGGGGTATCCTCA</th>\n",
       "      <td>ZNF785.1:CDX4.2</td>\n",
       "      <td>-0.133000</td>\n",
       "      <td>0.031000</td>\n",
       "      <td>-0.259667</td>\n",
       "      <td>ZNF785</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF785_CTTCTCCTCATTCCCATCCCTGG_HTR5A_AGGATTGCTGAGATACCCACCTC</th>\n",
       "      <td>ZNF785.3:HTR5A.1</td>\n",
       "      <td>0.147000</td>\n",
       "      <td>0.616667</td>\n",
       "      <td>0.447333</td>\n",
       "      <td>ZNF785</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF785_AGGATCCAGGGATGGGAATGAGG_PLA2G2F_GTCCTCCCTCAGGTCTAGCCTGG</th>\n",
       "      <td>ZNF785.2:PLA2G2F.3</td>\n",
       "      <td>0.203000</td>\n",
       "      <td>0.231667</td>\n",
       "      <td>0.242333</td>\n",
       "      <td>ZNF785</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZSCAN21_AGATTGCAGATTGAGTACCCAGC_KCNK10_GACAGAACCCGTAGCCAATCTCC</th>\n",
       "      <td>ZSCAN21.1:KCNK10.3</td>\n",
       "      <td>0.295667</td>\n",
       "      <td>0.398000</td>\n",
       "      <td>0.205333</td>\n",
       "      <td>ZSCAN21</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZSCAN21_GGTACCATGATACCCCTGGACCC_TPH2_AAATACTCTCGGCAAGCATGAGT</th>\n",
       "      <td>ZSCAN21.2:TPH2.1</td>\n",
       "      <td>-0.374667</td>\n",
       "      <td>-0.176667</td>\n",
       "      <td>0.138333</td>\n",
       "      <td>ZSCAN21</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZSCAN21_GTAGGAACTGATTCTCTACCTTT_TSSK1B_CCGGGGAAGGAATTTCTCCAAGA</th>\n",
       "      <td>ZSCAN21.3:TSSK1B.2</td>\n",
       "      <td>0.215667</td>\n",
       "      <td>-0.005667</td>\n",
       "      <td>0.269000</td>\n",
       "      <td>ZSCAN21</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZSCAN29_GAATAACACTGGAGCACGGGTTA_DSPP_ACCTATGCTACTATCATGATCAT</th>\n",
       "      <td>ZSCAN29.3:DSPP.1</td>\n",
       "      <td>-0.044000</td>\n",
       "      <td>0.045333</td>\n",
       "      <td>0.313000</td>\n",
       "      <td>ZSCAN29</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZSCAN29_AAAGCCTGCAAACCAGCTATCGG_GFRAL_TCTAGGAGGCACTATAGAACATT</th>\n",
       "      <td>ZSCAN29.1:GFRAL.3</td>\n",
       "      <td>0.124000</td>\n",
       "      <td>0.142333</td>\n",
       "      <td>0.103333</td>\n",
       "      <td>ZSCAN29</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZSCAN29_CCTGCAAGCAGGATTGCCATTTC_SERPINB12_GAGTGAGATGGCAGCAGCACGAA</th>\n",
       "      <td>ZSCAN29.2:SERPINB12.3</td>\n",
       "      <td>0.419333</td>\n",
       "      <td>0.309000</td>\n",
       "      <td>0.013667</td>\n",
       "      <td>ZSCAN29</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZSCAN30_CAGCTCCTCCAGCATAGTCACAG_CST9_TGTCCATGCTATCCTCCCTCCAT</th>\n",
       "      <td>ZSCAN30.1:CST9.3</td>\n",
       "      <td>0.450333</td>\n",
       "      <td>0.672333</td>\n",
       "      <td>0.591000</td>\n",
       "      <td>ZSCAN30</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZSCAN30_CTGAAGGCCAAAGTCCTGGTCCA_HTR5A_GAGACCAACCACAGCCTCGGCAA</th>\n",
       "      <td>ZSCAN30.2:HTR5A.2</td>\n",
       "      <td>-0.289667</td>\n",
       "      <td>-0.065000</td>\n",
       "      <td>-0.043333</td>\n",
       "      <td>ZSCAN30</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZSCAN30_GTTACTCTGACTCCACTGGCCCT_HTR5A_GATCGCGTGCGACGTGCTTTGCT</th>\n",
       "      <td>ZSCAN30.3:HTR5A.3</td>\n",
       "      <td>-0.016333</td>\n",
       "      <td>0.309667</td>\n",
       "      <td>0.040333</td>\n",
       "      <td>ZSCAN30</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZSWIM4_GGGAGGTGCTGTTCCGGGAGAGC_C3orf30_GCCAAGCTACCAACGGAGTAGCT</th>\n",
       "      <td>ZSWIM4.2:C3orf30.3</td>\n",
       "      <td>0.575667</td>\n",
       "      <td>0.377667</td>\n",
       "      <td>0.018667</td>\n",
       "      <td>ZSWIM4</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZSWIM4_GGCCTCACCCAGCTCATCCCAGA_CLDN17_TACAACCCAGCCATCCACATAGG</th>\n",
       "      <td>ZSWIM4.1:CLDN17.3</td>\n",
       "      <td>0.141333</td>\n",
       "      <td>-0.050667</td>\n",
       "      <td>0.349667</td>\n",
       "      <td>ZSWIM4</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZSWIM4_GTCGTTTCCACGCAGTGAACGGG_GFRAL_CCTGAATCATTGCAGGCATCTTC</th>\n",
       "      <td>ZSWIM4.3:GFRAL.2</td>\n",
       "      <td>-0.142000</td>\n",
       "      <td>0.421667</td>\n",
       "      <td>0.091000</td>\n",
       "      <td>ZSWIM4</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZSWIM6_GCGGCAACAAGGCACTGCAATGA_GLRA2_CGATGGATGTCCAGACCTGTACA</th>\n",
       "      <td>ZSWIM6.3:GLRA2.2</td>\n",
       "      <td>0.577333</td>\n",
       "      <td>0.688333</td>\n",
       "      <td>0.490667</td>\n",
       "      <td>ZSWIM6</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZSWIM6_ACCTAAACCACTATATGGTCCGG_GPX6_GGGTCTGCTGAGCAAAGCCAACC</th>\n",
       "      <td>ZSWIM6.1:GPX6.2</td>\n",
       "      <td>-0.098667</td>\n",
       "      <td>-0.167667</td>\n",
       "      <td>-0.252667</td>\n",
       "      <td>ZSWIM6</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZSWIM6_CTTACACAGAGCTACCCCATAAA_KCNK10_GACAGAACCCGTAGCCAATCTCC</th>\n",
       "      <td>ZSWIM6.2:KCNK10.3</td>\n",
       "      <td>-0.158000</td>\n",
       "      <td>0.289667</td>\n",
       "      <td>-0.288000</td>\n",
       "      <td>ZSWIM6</td>\n",
       "      <td>CTRL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2545 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                 GENE  \\\n",
       "GENE_CLONE                                                              \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG...      AARS.1:CDX4.2   \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA...      AARS.2:F13B.3   \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC...     AARS.3:SPEM1.1   \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...   ABHD16A.1:GPX6.3   \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...   ABHD16A.2:GSX2.3   \n",
       "...                                                               ...   \n",
       "ZSWIM4_GGCCTCACCCAGCTCATCCCAGA_CLDN17_TACAACCCA...  ZSWIM4.1:CLDN17.3   \n",
       "ZSWIM4_GTCGTTTCCACGCAGTGAACGGG_GFRAL_CCTGAATCAT...   ZSWIM4.3:GFRAL.2   \n",
       "ZSWIM6_GCGGCAACAAGGCACTGCAATGA_GLRA2_CGATGGATGT...   ZSWIM6.3:GLRA2.2   \n",
       "ZSWIM6_ACCTAAACCACTATATGGTCCGG_GPX6_GGGTCTGCTGA...    ZSWIM6.1:GPX6.2   \n",
       "ZSWIM6_CTTACACAGAGCTACCCCATAAA_KCNK10_GACAGAACC...  ZSWIM6.2:KCNK10.3   \n",
       "\n",
       "                                                        A549      HT29  \\\n",
       "GENE_CLONE                                                               \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG... -1.475333 -2.747000   \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA... -0.096667 -0.647333   \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC... -0.651000 -2.854667   \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...  2.137000  2.166333   \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...  0.227333  0.893333   \n",
       "...                                                      ...       ...   \n",
       "ZSWIM4_GGCCTCACCCAGCTCATCCCAGA_CLDN17_TACAACCCA...  0.141333 -0.050667   \n",
       "ZSWIM4_GTCGTTTCCACGCAGTGAACGGG_GFRAL_CCTGAATCAT... -0.142000  0.421667   \n",
       "ZSWIM6_GCGGCAACAAGGCACTGCAATGA_GLRA2_CGATGGATGT...  0.577333  0.688333   \n",
       "ZSWIM6_ACCTAAACCACTATATGGTCCGG_GPX6_GGGTCTGCTGA... -0.098667 -0.167667   \n",
       "ZSWIM6_CTTACACAGAGCTACCCCATAAA_KCNK10_GACAGAACC... -0.158000  0.289667   \n",
       "\n",
       "                                                      OVCAR8    GENE1 GENE2  \n",
       "GENE_CLONE                                                                   \n",
       "AARS_AGGTCACCGTAGATGGTTCCAAT_CDX4_TCGCACTATATGG... -3.631667     AARS  CTRL  \n",
       "AARS_CCTTGAGCAGTATTTGAGAACAC_F13B_GTTGGTACCCAGA... -1.921000     AARS  CTRL  \n",
       "AARS_CTGTCCAGGATGCGACGCCCTCT_SPEM1_AGGGGCCTTTCC... -2.355667     AARS  CTRL  \n",
       "ABHD16A_CCATCCACCGCCTAGGCTTCCAG_GPX6_GTTCACTGCA...  1.905000  ABHD16A  CTRL  \n",
       "ABHD16A_CTCTCCACCTACCTCGGAGGCAG_GSX2_CCCGCGGGTG...  0.189667  ABHD16A  CTRL  \n",
       "...                                                      ...      ...   ...  \n",
       "ZSWIM4_GGCCTCACCCAGCTCATCCCAGA_CLDN17_TACAACCCA...  0.349667   ZSWIM4  CTRL  \n",
       "ZSWIM4_GTCGTTTCCACGCAGTGAACGGG_GFRAL_CCTGAATCAT...  0.091000   ZSWIM4  CTRL  \n",
       "ZSWIM6_GCGGCAACAAGGCACTGCAATGA_GLRA2_CGATGGATGT...  0.490667   ZSWIM6  CTRL  \n",
       "ZSWIM6_ACCTAAACCACTATATGGTCCGG_GPX6_GGGTCTGCTGA... -0.252667   ZSWIM6  CTRL  \n",
       "ZSWIM6_CTTACACAGAGCTACCCCATAAA_KCNK10_GACAGAACC... -0.288000   ZSWIM6  CTRL  \n",
       "\n",
       "[2545 rows x 6 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smf_guide1 = fc_merge.iloc[is_ctrl2].groupby('GENE1')\n",
    "smf_guide1.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>A549</th>\n",
       "      <th>HT29</th>\n",
       "      <th>OVCAR8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENE1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AARS</th>\n",
       "      <td>-0.741000</td>\n",
       "      <td>-2.083000</td>\n",
       "      <td>-2.636111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD16A</th>\n",
       "      <td>0.852444</td>\n",
       "      <td>1.106000</td>\n",
       "      <td>0.741000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD4</th>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.110556</td>\n",
       "      <td>-0.001444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD5</th>\n",
       "      <td>0.007667</td>\n",
       "      <td>-0.153222</td>\n",
       "      <td>-0.089222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABL1</th>\n",
       "      <td>0.294667</td>\n",
       "      <td>0.399222</td>\n",
       "      <td>0.314667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             A549      HT29    OVCAR8\n",
       "GENE1                                \n",
       "AARS    -0.741000 -2.083000 -2.636111\n",
       "ABHD16A  0.852444  1.106000  0.741000\n",
       "ABHD4    0.060000  0.110556 -0.001444\n",
       "ABHD5    0.007667 -0.153222 -0.089222\n",
       "ABL1     0.294667  0.399222  0.314667"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smf_gene1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GENE</th>\n",
       "      <th>A549</th>\n",
       "      <th>HT29</th>\n",
       "      <th>OVCAR8</th>\n",
       "      <th>GENE1</th>\n",
       "      <th>GENE2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENE_CLONE</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>ADH7_GCCACAGGAATCTGTCGCACAGA_ABL1_GGCTTCACACCATTCCCCATTGT</th>\n",
       "      <td>ADH7.3:ABL1.3</td>\n",
       "      <td>-0.347333</td>\n",
       "      <td>-0.103000</td>\n",
       "      <td>0.008333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>ABL1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_CCGAGTACACAGTGGTGGATGAA_ANXA11_CTCCTGGGGGTGGATACATCCCA</th>\n",
       "      <td>ADH7.2:ANXA11.1</td>\n",
       "      <td>-0.412000</td>\n",
       "      <td>0.090333</td>\n",
       "      <td>0.025333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>ANXA11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_GCCACAGGAATCTGTCGCACAGA_ARCN1_GTTGCTTCAGAAGTACGCTTGCC</th>\n",
       "      <td>ADH7.3:ARCN1.2</td>\n",
       "      <td>-0.918667</td>\n",
       "      <td>-0.653333</td>\n",
       "      <td>-1.571667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>ARCN1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_CCGAGTACACAGTGGTGGATGAA_ARID1A_GCCGAGACCGTGTCTCTGCACCC</th>\n",
       "      <td>ADH7.2:ARID1A.2</td>\n",
       "      <td>-0.271667</td>\n",
       "      <td>-0.362000</td>\n",
       "      <td>0.491333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>ARID1A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_ACAGCAGCGCCATATCCAGTGGA_BTN3A1_CAGTGAAGCTCCTGGAGGAACTC</th>\n",
       "      <td>ADH7.1:BTN3A1.1</td>\n",
       "      <td>0.500667</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>0.282000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>BTN3A1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_GCCACAGGAATCTGTCGCACAGA_CBX3_TTACTCTGTAAATCCCTTCCACT</th>\n",
       "      <td>ADH7.3:CBX3.3</td>\n",
       "      <td>-0.047667</td>\n",
       "      <td>0.383667</td>\n",
       "      <td>0.086000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>CBX3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_GCCACAGGAATCTGTCGCACAGA_CISD1_GACCTCCAACAACGGCAGTACAC</th>\n",
       "      <td>ADH7.3:CISD1.3</td>\n",
       "      <td>-0.054000</td>\n",
       "      <td>0.296333</td>\n",
       "      <td>0.549000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>CISD1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_ACAGCAGCGCCATATCCAGTGGA_CRY2_GTTGTCTCTCCTGCCGCCTCTTC</th>\n",
       "      <td>ADH7.1:CRY2.3</td>\n",
       "      <td>0.201333</td>\n",
       "      <td>0.433000</td>\n",
       "      <td>0.179000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>CRY2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_CCGAGTACACAGTGGTGGATGAA_DCUN1D2_GAAGAAGCTGTCCGTGGCCTCGT</th>\n",
       "      <td>ADH7.2:DCUN1D2.2</td>\n",
       "      <td>0.169000</td>\n",
       "      <td>-0.156667</td>\n",
       "      <td>0.257000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>DCUN1D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_CCGAGTACACAGTGGTGGATGAA_DPP8_GATCCATCCGTATGTTGGGACAA</th>\n",
       "      <td>ADH7.2:DPP8.3</td>\n",
       "      <td>-0.381333</td>\n",
       "      <td>-0.190000</td>\n",
       "      <td>0.056333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>DPP8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_CCGAGTACACAGTGGTGGATGAA_DYRK2_GCTCCAGTTGTTACGAGCATCAG</th>\n",
       "      <td>ADH7.2:DYRK2.2</td>\n",
       "      <td>0.262333</td>\n",
       "      <td>0.137000</td>\n",
       "      <td>0.240667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>DYRK2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_CCGAGTACACAGTGGTGGATGAA_FNDC3A_GGACCCCCTCTGGTTGATGGTGG</th>\n",
       "      <td>ADH7.2:FNDC3A.3</td>\n",
       "      <td>-0.097333</td>\n",
       "      <td>-0.063667</td>\n",
       "      <td>0.392000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>FNDC3A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_GCCACAGGAATCTGTCGCACAGA_FNDC3B_GCTCTTATATCCTAACTCACCTC</th>\n",
       "      <td>ADH7.3:FNDC3B.1</td>\n",
       "      <td>-0.145000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.074667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>FNDC3B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_GCCACAGGAATCTGTCGCACAGA_FNIP2_GCTCAGTTGCCATGAGTTACAAA</th>\n",
       "      <td>ADH7.3:FNIP2.3</td>\n",
       "      <td>-0.289333</td>\n",
       "      <td>0.044667</td>\n",
       "      <td>0.350667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>FNIP2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_ACAGCAGCGCCATATCCAGTGGA_GANAB_GTGGCTGATCCACCAATAGCCCG</th>\n",
       "      <td>ADH7.1:GANAB.3</td>\n",
       "      <td>-0.058000</td>\n",
       "      <td>-0.276667</td>\n",
       "      <td>-0.230333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>GANAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_CCGAGTACACAGTGGTGGATGAA_GANAB_ACCTGGGACCCCAGTCGCTTCCC</th>\n",
       "      <td>ADH7.2:GANAB.1</td>\n",
       "      <td>-1.613000</td>\n",
       "      <td>-0.736333</td>\n",
       "      <td>-0.458667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>GANAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_CCGAGTACACAGTGGTGGATGAA_GIGYF2_CCCCTAGAAATGGTTGCTGATGT</th>\n",
       "      <td>ADH7.2:GIGYF2.2</td>\n",
       "      <td>0.432000</td>\n",
       "      <td>0.096000</td>\n",
       "      <td>-0.683000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>GIGYF2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_GCCACAGGAATCTGTCGCACAGA_GRK3_GAAAAATCGCAGGCAAGACCAAG</th>\n",
       "      <td>ADH7.3:GRK3.2</td>\n",
       "      <td>0.201667</td>\n",
       "      <td>-0.302333</td>\n",
       "      <td>0.306000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>GRK3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_CCGAGTACACAGTGGTGGATGAA_GTF2H2C_AAGGAAGCTGTGGATATGACCTG</th>\n",
       "      <td>ADH7.2:GTF2H2C.1</td>\n",
       "      <td>-1.126667</td>\n",
       "      <td>-0.344000</td>\n",
       "      <td>-0.740667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>GTF2H2C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_GCCACAGGAATCTGTCGCACAGA_HDAC4_CCCGGCACCCACCTCACTCCCTA</th>\n",
       "      <td>ADH7.3:HDAC4.2</td>\n",
       "      <td>-0.468667</td>\n",
       "      <td>-0.523000</td>\n",
       "      <td>-0.436000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>HDAC4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_ACAGCAGCGCCATATCCAGTGGA_HNRNPF_GATGCACAAAGGAAGAAATTGTT</th>\n",
       "      <td>ADH7.1:HNRNPF.3</td>\n",
       "      <td>-0.077667</td>\n",
       "      <td>0.342000</td>\n",
       "      <td>-0.830000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>HNRNPF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_ACAGCAGCGCCATATCCAGTGGA_KAT6A_GTGATAAAGGCACTCCAATATTA</th>\n",
       "      <td>ADH7.1:KAT6A.3</td>\n",
       "      <td>-0.829333</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>-0.424667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>KAT6A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_CCGAGTACACAGTGGTGGATGAA_KLHL20_CGTGGTCTCGTCCTTGGTCCTTG</th>\n",
       "      <td>ADH7.2:KLHL20.2</td>\n",
       "      <td>0.425333</td>\n",
       "      <td>0.174333</td>\n",
       "      <td>0.086333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>KLHL20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_GCCACAGGAATCTGTCGCACAGA_LAS1L_GGTCAGGCAACGAACTCCCTCTG</th>\n",
       "      <td>ADH7.3:LAS1L.3</td>\n",
       "      <td>-4.149000</td>\n",
       "      <td>-3.505333</td>\n",
       "      <td>-3.349667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>LAS1L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_GCCACAGGAATCTGTCGCACAGA_LATS1_GGTAGGGTGGTGGTGGTCCTTGA</th>\n",
       "      <td>ADH7.3:LATS1.3</td>\n",
       "      <td>0.454333</td>\n",
       "      <td>0.366333</td>\n",
       "      <td>0.025333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>LATS1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_CCGAGTACACAGTGGTGGATGAA_MFSD14A_CATACGTAGCAGATATAACCCAA</th>\n",
       "      <td>ADH7.2:MFSD14A.2</td>\n",
       "      <td>-0.389333</td>\n",
       "      <td>-0.517000</td>\n",
       "      <td>-0.581333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>MFSD14A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_CCGAGTACACAGTGGTGGATGAA_MPP2_ACTATGACCCGGCCCGAGACAGC</th>\n",
       "      <td>ADH7.2:MPP2.1</td>\n",
       "      <td>-0.459667</td>\n",
       "      <td>-0.299667</td>\n",
       "      <td>-0.688667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>MPP2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_ACAGCAGCGCCATATCCAGTGGA_P3H4_AGGGGGAGGGTGACTACGAGGAG</th>\n",
       "      <td>ADH7.1:P3H4.2</td>\n",
       "      <td>0.398000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>0.110333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>P3H4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_CCGAGTACACAGTGGTGGATGAA_PHLPP2_ATGACCTACCAAGTCAAATTGGC</th>\n",
       "      <td>ADH7.2:PHLPP2.1</td>\n",
       "      <td>0.109667</td>\n",
       "      <td>0.030667</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>PHLPP2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADH7_CCGAGTACACAGTGGTGGATGAA_PHTF2_AGGACGCCCCTAAATCGGGTACT</th>\n",
       "      <td>ADH7.2:PHTF2.2</td>\n",
       "      <td>-0.051333</td>\n",
       "      <td>-0.183000</td>\n",
       "      <td>0.063333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>PHTF2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CGTAACCCTGCCATGATGCAGGA_NUMBL_GCTCCAGGCAGGCGGCAAAAGCA</th>\n",
       "      <td>UBQLN3.1:NUMBL.3</td>\n",
       "      <td>-0.144000</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>0.029667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>NUMBL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CGTAACCCTGCCATGATGCAGGA_PBX4_CCACTCTGAGCTAGAGAAATATG</th>\n",
       "      <td>UBQLN3.1:PBX4.1</td>\n",
       "      <td>-0.297333</td>\n",
       "      <td>-0.322667</td>\n",
       "      <td>-0.268667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>PBX4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_GCGGCAATCCCTTTGCCACTGCC_PCMTD2_AGAGCTATCGATCGTGCAGACTA</th>\n",
       "      <td>UBQLN3.3:PCMTD2.1</td>\n",
       "      <td>-0.299000</td>\n",
       "      <td>-0.065667</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>PCMTD2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CTGAGATACCCCACAGAGAACAG_PHB2_GAGGTCTGGCCCGAATGTCATAG</th>\n",
       "      <td>UBQLN3.2:PHB2.1</td>\n",
       "      <td>-2.742000</td>\n",
       "      <td>-4.112667</td>\n",
       "      <td>-3.742667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>PHB2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CGTAACCCTGCCATGATGCAGGA_PITPNM1_CCCCTCCTACAGCCTGAGCCCTT</th>\n",
       "      <td>UBQLN3.1:PITPNM1.1</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>-0.053000</td>\n",
       "      <td>-0.209333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>PITPNM1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CTGAGATACCCCACAGAGAACAG_PITPNM1_GCTATACTGACCATGCTCCCACG</th>\n",
       "      <td>UBQLN3.2:PITPNM1.2</td>\n",
       "      <td>0.049667</td>\n",
       "      <td>0.214333</td>\n",
       "      <td>0.360333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>PITPNM1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CGTAACCCTGCCATGATGCAGGA_POLR2E_CCTGACGGTGCTTCGCAGTAAGC</th>\n",
       "      <td>UBQLN3.1:POLR2E.1</td>\n",
       "      <td>-1.080333</td>\n",
       "      <td>-1.453000</td>\n",
       "      <td>-2.672667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>POLR2E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CTGAGATACCCCACAGAGAACAG_PPP1CC_ACCCCAGCCTAAGACATCTTTAT</th>\n",
       "      <td>UBQLN3.2:PPP1CC.1</td>\n",
       "      <td>-0.083000</td>\n",
       "      <td>-0.414667</td>\n",
       "      <td>-0.666000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>PPP1CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_GCGGCAATCCCTTTGCCACTGCC_RBL1_GCTGAACGTAGTATTCTGGTAAG</th>\n",
       "      <td>UBQLN3.3:RBL1.3</td>\n",
       "      <td>0.684333</td>\n",
       "      <td>0.592000</td>\n",
       "      <td>0.413333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>RBL1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CGTAACCCTGCCATGATGCAGGA_RHOC_GCTACCTTGAGTGCTCAGCCAAG</th>\n",
       "      <td>UBQLN3.1:RHOC.2</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.422000</td>\n",
       "      <td>0.360667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>RHOC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CGTAACCCTGCCATGATGCAGGA_SAP30L_TTCTGCGAGATGCTCTTCTGGAC</th>\n",
       "      <td>UBQLN3.1:SAP30L.3</td>\n",
       "      <td>-0.206667</td>\n",
       "      <td>-0.032000</td>\n",
       "      <td>-0.243667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>SAP30L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CGTAACCCTGCCATGATGCAGGA_SIPA1L3_AGCCGACTCCACGGGAACCCACT</th>\n",
       "      <td>UBQLN3.1:SIPA1L3.1</td>\n",
       "      <td>0.072333</td>\n",
       "      <td>-0.504000</td>\n",
       "      <td>-0.160667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>SIPA1L3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_GCGGCAATCCCTTTGCCACTGCC_SLC4A2_GAGGCCCGAGGAGTCGCCTCCTG</th>\n",
       "      <td>UBQLN3.3:SLC4A2.2</td>\n",
       "      <td>0.206667</td>\n",
       "      <td>0.590333</td>\n",
       "      <td>0.069333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>SLC4A2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CGTAACCCTGCCATGATGCAGGA_SMARCA2_GGTGCTTCTGGTCCGAACAGAAC</th>\n",
       "      <td>UBQLN3.1:SMARCA2.2</td>\n",
       "      <td>-1.081667</td>\n",
       "      <td>0.492000</td>\n",
       "      <td>0.491000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>SMARCA2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_GCGGCAATCCCTTTGCCACTGCC_SMURF1_GATCTATGCAAACTAAACCCCTC</th>\n",
       "      <td>UBQLN3.3:SMURF1.2</td>\n",
       "      <td>-0.038000</td>\n",
       "      <td>0.205333</td>\n",
       "      <td>0.037667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>SMURF1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CGTAACCCTGCCATGATGCAGGA_SPAG9_GATCTGTACCAGCACTCATTCGG</th>\n",
       "      <td>UBQLN3.1:SPAG9.2</td>\n",
       "      <td>-0.239667</td>\n",
       "      <td>-0.413667</td>\n",
       "      <td>-0.259667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>SPAG9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_GCGGCAATCCCTTTGCCACTGCC_SPC24_CCACCAAGTTAGTAAAATTGAGT</th>\n",
       "      <td>UBQLN3.3:SPC24.2</td>\n",
       "      <td>-2.121667</td>\n",
       "      <td>-2.536000</td>\n",
       "      <td>-3.393333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>SPC24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CTGAGATACCCCACAGAGAACAG_STAM_GCAATGTGTCACCAGATGGGACC</th>\n",
       "      <td>UBQLN3.2:STAM.2</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>0.437333</td>\n",
       "      <td>0.454000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>STAM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CGTAACCCTGCCATGATGCAGGA_TRAK1_CCAGGATTCCTTGGCAGCAGAGA</th>\n",
       "      <td>UBQLN3.1:TRAK1.1</td>\n",
       "      <td>0.194333</td>\n",
       "      <td>-0.009667</td>\n",
       "      <td>0.202667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>TRAK1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CTGAGATACCCCACAGAGAACAG_UCK1_CCCTCCACGATGTTCTTCAGAGT</th>\n",
       "      <td>UBQLN3.2:UCK1.2</td>\n",
       "      <td>0.295333</td>\n",
       "      <td>0.233000</td>\n",
       "      <td>0.465333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>UCK1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_GCGGCAATCCCTTTGCCACTGCC_UGGT2_CGTACTAACCAGCAGTACCTCAA</th>\n",
       "      <td>UBQLN3.3:UGGT2.1</td>\n",
       "      <td>0.183000</td>\n",
       "      <td>-0.041333</td>\n",
       "      <td>0.356000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>UGGT2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_GCGGCAATCCCTTTGCCACTGCC_UNC119B_GCAATCTCAAAAAGTACTGTCCC</th>\n",
       "      <td>UBQLN3.3:UNC119B.2</td>\n",
       "      <td>0.126000</td>\n",
       "      <td>0.173333</td>\n",
       "      <td>0.351000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>UNC119B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CGTAACCCTGCCATGATGCAGGA_USP33_GGAGGGCTTGCCGATAAACGTGG</th>\n",
       "      <td>UBQLN3.1:USP33.1</td>\n",
       "      <td>-0.114667</td>\n",
       "      <td>-0.482667</td>\n",
       "      <td>-0.342333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>USP33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CTGAGATACCCCACAGAGAACAG_WDFY1_TGGGCCAGGATAATGGAGCTGTA</th>\n",
       "      <td>UBQLN3.2:WDFY1.3</td>\n",
       "      <td>0.479667</td>\n",
       "      <td>0.426333</td>\n",
       "      <td>0.240667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>WDFY1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CTGAGATACCCCACAGAGAACAG_YIF1B_GATGACACAAGTTCAGCCCAGAG</th>\n",
       "      <td>UBQLN3.2:YIF1B.2</td>\n",
       "      <td>0.112000</td>\n",
       "      <td>0.441333</td>\n",
       "      <td>0.515000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>YIF1B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_GCGGCAATCCCTTTGCCACTGCC_ZFAND2A_GGGAAGCATTGTTCAGAAAAGAC</th>\n",
       "      <td>UBQLN3.3:ZFAND2A.3</td>\n",
       "      <td>0.707333</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.707000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>ZFAND2A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CGTAACCCTGCCATGATGCAGGA_ZKSCAN3_CCAGTAGCCCAGATCTGGGTTCT</th>\n",
       "      <td>UBQLN3.1:ZKSCAN3.2</td>\n",
       "      <td>-0.704333</td>\n",
       "      <td>-0.219333</td>\n",
       "      <td>-0.229000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>ZKSCAN3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CTGAGATACCCCACAGAGAACAG_ZKSCAN4_GTCAGGCCTGAACTTTGAGCAAA</th>\n",
       "      <td>UBQLN3.2:ZKSCAN4.2</td>\n",
       "      <td>0.165667</td>\n",
       "      <td>0.264667</td>\n",
       "      <td>0.431000</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>ZKSCAN4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_CGTAACCCTGCCATGATGCAGGA_ZMIZ2_GGGAACCCCATGGGCCCTGCAGG</th>\n",
       "      <td>UBQLN3.1:ZMIZ2.2</td>\n",
       "      <td>0.412333</td>\n",
       "      <td>-0.082000</td>\n",
       "      <td>-0.233333</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>ZMIZ2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQLN3_GCGGCAATCCCTTTGCCACTGCC_ZNF419_CCAGCATGTAGTCTCTGGTGCTG</th>\n",
       "      <td>UBQLN3.3:ZNF419.2</td>\n",
       "      <td>0.672333</td>\n",
       "      <td>0.611333</td>\n",
       "      <td>0.484667</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>ZNF419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2545 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  GENE  \\\n",
       "GENE_CLONE                                                               \n",
       "ADH7_GCCACAGGAATCTGTCGCACAGA_ABL1_GGCTTCACACCAT...       ADH7.3:ABL1.3   \n",
       "ADH7_CCGAGTACACAGTGGTGGATGAA_ANXA11_CTCCTGGGGGT...     ADH7.2:ANXA11.1   \n",
       "ADH7_GCCACAGGAATCTGTCGCACAGA_ARCN1_GTTGCTTCAGAA...      ADH7.3:ARCN1.2   \n",
       "ADH7_CCGAGTACACAGTGGTGGATGAA_ARID1A_GCCGAGACCGT...     ADH7.2:ARID1A.2   \n",
       "ADH7_ACAGCAGCGCCATATCCAGTGGA_BTN3A1_CAGTGAAGCTC...     ADH7.1:BTN3A1.1   \n",
       "...                                                                ...   \n",
       "UBQLN3_GCGGCAATCCCTTTGCCACTGCC_ZFAND2A_GGGAAGCA...  UBQLN3.3:ZFAND2A.3   \n",
       "UBQLN3_CGTAACCCTGCCATGATGCAGGA_ZKSCAN3_CCAGTAGC...  UBQLN3.1:ZKSCAN3.2   \n",
       "UBQLN3_CTGAGATACCCCACAGAGAACAG_ZKSCAN4_GTCAGGCC...  UBQLN3.2:ZKSCAN4.2   \n",
       "UBQLN3_CGTAACCCTGCCATGATGCAGGA_ZMIZ2_GGGAACCCCA...    UBQLN3.1:ZMIZ2.2   \n",
       "UBQLN3_GCGGCAATCCCTTTGCCACTGCC_ZNF419_CCAGCATGT...   UBQLN3.3:ZNF419.2   \n",
       "\n",
       "                                                        A549      HT29  \\\n",
       "GENE_CLONE                                                               \n",
       "ADH7_GCCACAGGAATCTGTCGCACAGA_ABL1_GGCTTCACACCAT... -0.347333 -0.103000   \n",
       "ADH7_CCGAGTACACAGTGGTGGATGAA_ANXA11_CTCCTGGGGGT... -0.412000  0.090333   \n",
       "ADH7_GCCACAGGAATCTGTCGCACAGA_ARCN1_GTTGCTTCAGAA... -0.918667 -0.653333   \n",
       "ADH7_CCGAGTACACAGTGGTGGATGAA_ARID1A_GCCGAGACCGT... -0.271667 -0.362000   \n",
       "ADH7_ACAGCAGCGCCATATCCAGTGGA_BTN3A1_CAGTGAAGCTC...  0.500667  0.449000   \n",
       "...                                                      ...       ...   \n",
       "UBQLN3_GCGGCAATCCCTTTGCCACTGCC_ZFAND2A_GGGAAGCA...  0.707333  0.440000   \n",
       "UBQLN3_CGTAACCCTGCCATGATGCAGGA_ZKSCAN3_CCAGTAGC... -0.704333 -0.219333   \n",
       "UBQLN3_CTGAGATACCCCACAGAGAACAG_ZKSCAN4_GTCAGGCC...  0.165667  0.264667   \n",
       "UBQLN3_CGTAACCCTGCCATGATGCAGGA_ZMIZ2_GGGAACCCCA...  0.412333 -0.082000   \n",
       "UBQLN3_GCGGCAATCCCTTTGCCACTGCC_ZNF419_CCAGCATGT...  0.672333  0.611333   \n",
       "\n",
       "                                                      OVCAR8 GENE1    GENE2  \n",
       "GENE_CLONE                                                                   \n",
       "ADH7_GCCACAGGAATCTGTCGCACAGA_ABL1_GGCTTCACACCAT...  0.008333  CTRL     ABL1  \n",
       "ADH7_CCGAGTACACAGTGGTGGATGAA_ANXA11_CTCCTGGGGGT...  0.025333  CTRL   ANXA11  \n",
       "ADH7_GCCACAGGAATCTGTCGCACAGA_ARCN1_GTTGCTTCAGAA... -1.571667  CTRL    ARCN1  \n",
       "ADH7_CCGAGTACACAGTGGTGGATGAA_ARID1A_GCCGAGACCGT...  0.491333  CTRL   ARID1A  \n",
       "ADH7_ACAGCAGCGCCATATCCAGTGGA_BTN3A1_CAGTGAAGCTC...  0.282000  CTRL   BTN3A1  \n",
       "...                                                      ...   ...      ...  \n",
       "UBQLN3_GCGGCAATCCCTTTGCCACTGCC_ZFAND2A_GGGAAGCA...  0.707000  CTRL  ZFAND2A  \n",
       "UBQLN3_CGTAACCCTGCCATGATGCAGGA_ZKSCAN3_CCAGTAGC... -0.229000  CTRL  ZKSCAN3  \n",
       "UBQLN3_CTGAGATACCCCACAGAGAACAG_ZKSCAN4_GTCAGGCC...  0.431000  CTRL  ZKSCAN4  \n",
       "UBQLN3_CGTAACCCTGCCATGATGCAGGA_ZMIZ2_GGGAACCCCA... -0.233333  CTRL    ZMIZ2  \n",
       "UBQLN3_GCGGCAATCCCTTTGCCACTGCC_ZNF419_CCAGCATGT...  0.484667  CTRL   ZNF419  \n",
       "\n",
       "[2545 rows x 6 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smf_guide2 = fc_merge.iloc[is_ctrl1].groupby('GENE2')\n",
    "smf_guide2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>A549</th>\n",
       "      <th>HT29</th>\n",
       "      <th>OVCAR8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENE2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AARS</th>\n",
       "      <td>-0.668444</td>\n",
       "      <td>-2.506333</td>\n",
       "      <td>-3.637444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD16A</th>\n",
       "      <td>0.593889</td>\n",
       "      <td>0.241778</td>\n",
       "      <td>-0.293000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD4</th>\n",
       "      <td>0.629333</td>\n",
       "      <td>0.937667</td>\n",
       "      <td>0.524000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD5</th>\n",
       "      <td>-0.024889</td>\n",
       "      <td>-0.058222</td>\n",
       "      <td>-0.113000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABL1</th>\n",
       "      <td>0.251333</td>\n",
       "      <td>0.071556</td>\n",
       "      <td>0.361556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             A549      HT29    OVCAR8\n",
       "GENE2                                \n",
       "AARS    -0.668444 -2.506333 -3.637444\n",
       "ABHD16A  0.593889  0.241778 -0.293000\n",
       "ABHD4    0.629333  0.937667  0.524000\n",
       "ABHD5   -0.024889 -0.058222 -0.113000\n",
       "ABL1     0.251333  0.071556  0.361556"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smf_gene2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "smf_gene = smf_gene1.join(smf_gene2, lsuffix='_Aposn', rsuffix='_Bposn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>A549_Aposn</th>\n",
       "      <th>HT29_Aposn</th>\n",
       "      <th>OVCAR8_Aposn</th>\n",
       "      <th>A549_Bposn</th>\n",
       "      <th>HT29_Bposn</th>\n",
       "      <th>OVCAR8_Bposn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENE1</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>AARS</th>\n",
       "      <td>-0.741000</td>\n",
       "      <td>-2.083000</td>\n",
       "      <td>-2.636111</td>\n",
       "      <td>-0.668444</td>\n",
       "      <td>-2.506333</td>\n",
       "      <td>-3.637444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD16A</th>\n",
       "      <td>0.852444</td>\n",
       "      <td>1.106000</td>\n",
       "      <td>0.741000</td>\n",
       "      <td>0.593889</td>\n",
       "      <td>0.241778</td>\n",
       "      <td>-0.293000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD4</th>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.110556</td>\n",
       "      <td>-0.001444</td>\n",
       "      <td>0.629333</td>\n",
       "      <td>0.937667</td>\n",
       "      <td>0.524000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD5</th>\n",
       "      <td>0.007667</td>\n",
       "      <td>-0.153222</td>\n",
       "      <td>-0.089222</td>\n",
       "      <td>-0.024889</td>\n",
       "      <td>-0.058222</td>\n",
       "      <td>-0.113000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABL1</th>\n",
       "      <td>0.294667</td>\n",
       "      <td>0.399222</td>\n",
       "      <td>0.314667</td>\n",
       "      <td>0.251333</td>\n",
       "      <td>0.071556</td>\n",
       "      <td>0.361556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         A549_Aposn  HT29_Aposn  OVCAR8_Aposn  A549_Bposn  HT29_Bposn  \\\n",
       "GENE1                                                                   \n",
       "AARS      -0.741000   -2.083000     -2.636111   -0.668444   -2.506333   \n",
       "ABHD16A    0.852444    1.106000      0.741000    0.593889    0.241778   \n",
       "ABHD4      0.060000    0.110556     -0.001444    0.629333    0.937667   \n",
       "ABHD5      0.007667   -0.153222     -0.089222   -0.024889   -0.058222   \n",
       "ABL1       0.294667    0.399222      0.314667    0.251333    0.071556   \n",
       "\n",
       "         OVCAR8_Bposn  \n",
       "GENE1                  \n",
       "AARS        -3.637444  \n",
       "ABHD16A     -0.293000  \n",
       "ABHD4        0.524000  \n",
       "ABHD5       -0.113000  \n",
       "ABL1         0.361556  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smf_gene.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A549: (0.874079734336047, 2.6480708028066905e-265)\n",
      "HT29: (0.938625580698329, 0.0)\n",
      "OVCAR8: (0.9387807098598936, 0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "for cell in cells:\n",
    "    f, ax = pyplot.subplots(figsize=(5,5))\n",
    "    sns.scatterplot(data=smf_gene, x=cell + '_Aposn',y=cell + '_Bposn', ax=ax)\n",
    "    plot([-4,1],[-4,1],'r--')\n",
    "    print(cell + ': ' + str( stats.pearsonr( smf_gene[ cell + '_Aposn' ], smf_gene[cell + '_Bposn' ])) ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A549: lst squares slope = 1.0126537545686707, int = -0.005365502867914778\n",
      "HT29: lst squares slope = 1.0414773130263297, int = 0.010563057191158352\n",
      "OVCAR8: lst squares slope = 1.0854969785668536, int = -0.0005980618003119719\n"
     ]
    },
    {
     "data": {
      "image/png": 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waE1KgpUEq/IaG+zyh+ZkJDPv8gEcqraTZLVQXlPL/rIaurZPoaLGM+o9Z0w/Xv9yF2OH9gDgQIXdLR0ohb0Ha0i0WiitsrM6v5DbzzmO8Us+87Dwjsqw8u7Mc83H46ct5t57Lqes2k5mSqJHvTsYFuGLk4by4JvfmT7OhePyOLZLps+xHK7PyV9mQW2tk90HqwLW3YcT8XEKQozxlWt426tGfuLkZfk89t8tPHDhCdTUOj1GTjQU175dMrHVOsygjbuFN3t0P1bnF3L3+blMXfGl172WjD+Z0ko7tU6nmQ50Vm5nbvxDH+547RuP61gteIjg4Yf28+7sa8zHeTcupzg1i3uBdimJFFfYfPog9x6s5ubhxzBzRC5Wi4XOGckBRTNQkKekyu4VCCvYUxaVktaGSMmlIEQBf8GfrNREBnTP4upTe3Hxoo2cMfc9Zr7xLdPP7ktORjK3vbqZKWf09ig5TEqw+ix9nLF6M7efc5z5uOG9rBZFp4wk0xIFGJ3X3RTZAd2zePiiE+nSLoUkq8Uswbzsq//w1nM3UZGcxo4rrqXnjLUUp2fV+RgtOLTxxeCvefHk5fnYHRpdl3jvj8YaE8ey/2ZDxOIUhCjgL7hRWmU3K258VctMXpZPVp0fzyUSXdun0rNTms/k96QEhUL5vJejbrSG++uO6ZLBkvEnA5oqu9MU0bNyO7PgouP46aopjPpkDYdSMylc9x7/2OaAum33nDH9sCjDb7o6v9DL1zp/7EDue+M7dpZUcaDCRud2yew9VI3WxjqsCiwWS9CJ7LHsv9kQsTgFIQr46rI+Z4zRFf2wdil+rVGXuLpeo5RhsSXXJb//ffQJ9M7JICczmd45GTicmkQrPHZJf697Pf3BLyRYFGfldmb62X2ZtbaAM+e+z4Sln1Npc/Lku1vNdajX/48TjzmcUZ+sofT6m/j+yy2kHd+XywcfyUuThjJr5AmkJlm5f813KGDK6Ufz/MdGY5BXp5zCsmsGs3zjDnPmUbXdwcFKO5cs2siw2Ru4ZNFGfiqq4O7XN7NlXxlOp/Y7wsMljL4+w2j132yIBIcEIUr4iqrba51U1zoZ6yOJfdbIE+iYkYQCfiu3kZ2eyPwNP9X5CxUJFsXeQzUeVuL9Fx6PU4NFQbXdSZ3OkmhR1Dg0mclWquy+7zdzRC6TX/iCl1fOYPDOAgB+nv0ElmsmcKjKRtf2KWwqPGhat670pGMPMxoZG13hLWjgoX8VmAGh2aP7kWS1cOvLX/m856y1BUEnsodaltqcfp0SHBKEOMFat8ertjtItFjQWvPQvwq8trkLx+XRIT2R+9fUR6Qfvbg/WalJlFTambriS2OaZbmNRy/uj93hpFNmEkVlNR4R86M6p1NYXEn71ETSkhOosDlItFrIyUj2aNRRWmWnR4Kdf7451xTNa29cwLgLL4LfKujeIY2iMiOS/XbBfgZ0z/KuKBqXxyPrfqCozMa04X2449zj2F1axfMfb+PO847zaVV3zkwOKZE9lHzNSHZNEuEUhCjgdGq2F1ew71C1R5rQC9cM5u2C/RSV2TwaAWenJVJSaee2s4/l+jOOZn9ZDc9++Av3XnA8xeU25ozpx2/lNo9mGQvGDgTwODZ/7ED+9+N+TuvbhevrLNNu2ak8dcUAqu1O/vLK1+wsqeJP5Tt4+qkbOMZi5fFhl/P6eeN5+NKBpCZZeWBNAY9e0p/Jy/NZcd0QCvaU+fTLXr883/TLundaGp3XHaf2TtTvlp1KRnJCxBLZI9k1SYRTEKJAcYWNHcWVpqiB8Ye8o9goQdxUWMrkZfkAZk13eXWtKWyuLa/d4WTvoWr6dslk3LOfegpXXSmk+7En393KPSOO54oGjTUOVBgdinYXlzPx8ze4/QOjbPLA2nVckDeEkQr2HqrmgTUFFJXXADBzRC5OrVk5cahXupLrur1z0hnQPYtNhaXsLKmiptbB5GX5vHnjMC+revboftgcThaNyzP9lOEchRHJKLwIpyAEQXP/oJ1OJ907pHr9Ic9bv5UFYwd6WIOzR/fjtzKbKZpQH2lfNXEoSVaL316baUn1EWZXmlOJjxzLtCQrKVu38MuzUwH4zzGncPu5N/PSSSdjt9WaflOXJVtRU8ustQVuFutAsyrIFaE3xE9x34W5puD+Vm6kEu0+WM3q/EIPq/r5j7dx+znH0SE9EYtFUVvrZMv+MrNHaHO31pGMwotwCkIjNNdX5uq0vv9QjdcfclF5DbVOJ6smDmXfoWqKK2zMXbeFO8491qcw2h1G8vrMEbk+RaHSVm9NubbTS8af7HVuu1XLeOfZWwH4tktvpvy/u+jWIY29B6t565s9LJ0wmESrwuHUHKyycdOqrzxE/IaVX/LCNYMBvNrazRnTjwdGHo9FKbLSElgy/mTe+mYPE4b18qpmsijISjW60e8+WMU/3/nRQ1wf++8Wc7BcqESysbRE1QWhEZrS8q3h6+9+fTNXndKTrlmp/FpcaVYGzbtsADmZSdgdmj88Wt9ubdGVeaaF527RZaUlcdsrXwN4BWcevbg/yYkW5m/4idF53enTOYOt+8s5skMqxRV2c6TF/Z+t4vL/vQLAA5feyZKew0zLMtGqSE9OpKbWQWmlHbvDSZd2KR5rc/HGDcPITk/ycAO4PptVE4eyfOM2Fv1vu9k9/ojsFGy1mupaJ1YFqUlWslLry0j3l1VTWmn32s4fnZNOl/apXvcPhjYRVVdKJQNPABcDNcA/tNaPxHZVQlunub4yp9PpZZUtGDuQTpnJ2B1OLn/6Uy8LcuF7PzNnTD+WfOQ9wsI1g2fuui3MGdOPbtlpOOoGnP1vSxE3nNmHG1bWb7VfuGYwz3/8I492PcSQOy4C4H+nXUiv5U9zWgWcUxeBt1gU1bVOrnvBs4xzd2mVT+s2MyWB4vIaP5+Nk4E9OzJgeylTzuhNld1Bpc3JYe1SfAqXrdZBktXisxDg5cmnBP8/qwGR6poUbwnwc4BTgD8Ck4F7lFKXxXZJQmsklKmIjSVmN4ZDew9Nu37Fl1TUOPh5fwU7S4z+l7NH9zPvU1ReQ05mMjNHHO9TTKac0du8/uVPf8IZc99j+itfc37/w03RdJ3/8Fvf89gTNzHk6lEAlOX2o9vqFey0W5m3fiuXLv6ECUs/Z/KyfEoq7F73Sk6wsmBcnkfi+cJxebz02Q72l9X4/Gz2HDRSjVyJ9mMWbuSSRRv5fu8h9h2s8vrMkxIM8fYlwvG4K44bi1MplQ5MBC7QWucD+UqpR4AbgRdjujihVRGqz7I5vjKnU2NzOH0KQlm1ne4dUnlp0lBKq+y8sWkXM0fk0qdzBkkJFt75bg/Dc7ua/TBds8pdVUXThvfxqDt3lTZ6+DKry1l89Qjz8fYVrzGusD07575vbu+dWmNRitIqO50ykrzWmZ2eyOL3f/HwPXZul8QVQ3tSbXd49ddcMHYga7/exZhBPbhp1SaP9U1elm8mvbt/5h3Tk6iy1cZNSWVjxJPF2R9IBj50O/YhcLJSKv4+OaHF0lgziYa4J2Z/NONMXp86LOjAUHGFjW1FFT6tstJKO4UHqsyZ5yMHHMHq/EKsFsWmHcUM7NmRy5/+xHx++tl9GdDdaK7ROTPZZ5TevdnG2Vs+ZvM/6zdsu7fvMUTT7X3/5ZWvqbY7zXtojGi8+zoLD1Txcv5OJi/LN8+rthuJ+xalmLf+R48Z7E+8u5Urhvb0+4WRlZro9ZlbLIrD2xs9NuOhpLIx4sbiBLoCB7TW1W7H9gFJQGdgT0xWJbQ6mjqmItDsH38BCFutg3nrt3rlMLqaBd+/psC8//Mfb+PeC47H7nByUo+OHkPNcjKSsdU6mXNxfywKFrz3M6MGHmGO2XVZpKvzC1k0diDJJw/k6L3bAPi/ISO45YwpvKqTfL5vVwrTzpIqprrNPe+Wnco/LzsJq8VoGpKTkcy04X3o2SmNBIvRWclqUYzO625awy7uu+B4stOS/DY28fWZJyRYOO6wdlEdgdFU4kk40zACQu64Hnv8i1VKTQImAfTo0SPyKxNaFeHM72ts25+UYKWovIa567aYHdkzUxJJS7Kwu7Sa28/pi0Up7A4nGSkJZif2V6ecYq7PV3njwnF5JCUorlnq2Rj5sENF9Oqeba7vmpsW8G5ad6DeGvUnZGCIWY8Oabw0aSiVNgdWi2LlJ78yZ0w/MpITuH7Fl+RkJPPAyOM98jpdAStXUw+X6DV0cbjO8/eZR3sERlOJp616NQ0E0u1xpftBrfVirfUgrfWgnJycqCxOaD2Es8tOY9t+172KymtY+N7PVNsdjF/yGac+vIFbXvoKgIff+oE7XvuGRIvihWsG8+5fTqdzuxTOyu0M4LO8ccryfHaVVHsc++LmmfQamAtAbWoq23cWm6IJeAWg3Ds0ueiWncpPReVmwOjGlZuYeNpRHNYuxUzSn3JGb69Gya6AlUvUs1MTvVwcK68bwvMfbzPFNV634cEQTxbnLiBbKZWktXY5mw7DsDoPxG5ZQmsjnFMRG9v2WyyKLu2SmTXyBHp2TONKt/ESO0vqu8AvfO9nDlXXmvPKXU0zANMn2PAe3TukMqB7Fl/vKObT+VeTU2Fslbfffh/jOpzGzFJPC3NTYSnPf7yNpRMGo4A9B41tuquksqFF6LqPRSnKa2rN6/hbT98umcwckcu89T+aSevuFqTTqXloVD/uuyC+t+HBEE8W51eADTjV7djvgHytdW1sliS0Vlx/0Edkp5l/4E2hsVQlp1NTZXOQlmTFqX3Xd2elJjLljN5eEfLrl+dz7wXHc0R2qs97FB6o4t5eTn6ZM9IUzd353/K3489nZ0kV6wv2sbBBGtHVp/bitle+RqNJSbTSpV0KL00ayrt/OZ1VE4eaFqH7fX49UIlTw1m5nRnQPYsO6Uk+17Nln1Eu+XbBfp/+4nB95vFA3Ain1roSeB6Yr5QarJS6EJgO/DO2KxME/7hv+wd0z2LJ+JNZfu0QNBq73cH3ew9x6WIjMv7rgUrf0fUqu18rbu/BaqwWVZfoXi+As0f3o/ba6xhw/mkA/NqpG9/8eoD7v63k2t8dxYDuWQzP7UKVzcGskSeYEe+564z5RT8XVTBm4UYqahzcuHITVz33GYeqbNz0hz5e95m3fis3rPySu87L5fZz+jJn3Q9eW/7Zo+u3/PGaQhRO4mmrDvBnYAHwLnAIeFBr/XJslyQI/nFt+9+48VT2lNZ45DOuuG6I2bACjIYec8b086rXfuQ/W5g2vI/PwE1xhY0O6UmkJVlZfu0Q9h2q5lBpGcP61Pv2989/hp/PvIAUmzHHvGBPmZlz+bd/f8/0s/t6dFlaMHYg977xnWlNTj+7L3PXbWHy8i+ZP3Ygy64ZzP6yGkqr7GbAB8Chtbl2Vxu8julJdGmXwqy135m+S/duR62VuBLOOqvz6rofQWgxVFQ7TNEE6sTFsxxxU2Epj/xni9nQo9pubGfvOPdYDs9K8eqS9NQVA/n35l2kJ1nJSEmg1ql5/pFlzF90q3nNEfes5i9n/o6kBAuP/GeLee+O6UkUV9g8IvpZqYlU2hyU19RSVF5j+jOLymvMPpqZKYls/63Co/0dGCJuUXj4S11t8F6feioThvXiznOPM2a3J7VuaxPiTDgFoSVSXGFjf5l3zXZxhY2zcjszOq+7WXGzOr+QHcUVZKYkcstLX5ki+eQVA+iYnsSK64ZQVDc3/KkNW7lp+DGs/Wonn20v5dm1s5n/nzcBKGzfhcvvXMnCcXlU2Rz87d/fA0ZzkI7pSXRtn0L71ASeumIgN6z8ksnL8k1r0Km1uW13WZOu+UagObZrplc10KJxeaQnJfi0imtqnTg1ZuCrsUqscPbcjBXSHUkQmsmukkq2/VZhziZ3Mfn3PRnRvxvXr3ArRxyXx7KPt3P1sJ5mYMg1v2fyGb252i3qDoYwvXhxX7r17mYe2/vqm1SffiYANbW1HKiw+2wGMmdMP17/chfDc7t45I9u2VvOvPVbTdHslm3MN+qcmUynjCSUAqtFUW134tCQkmihU7oRGffKWb1yEB0zkrhowcdBdY+K5DiLcBOoO5IIpyA0AY/Ba1YLRWXVHKqu9fBfrrhuiM+haCuvG4JSyqwMciW4J8k9Cs0AACAASURBVCdYGLNwo8d9zt7yMYv+72/m4+NufZWOXbJZfu0Q/vbvAiYM60X37FRQykyed7/XrJEnMG/9Vm4/p69P32pReQ0Lxg6kfVoiNXYnT3/wC6MGHmGee1ZuZ+453xgOl5RgJTs1kZIqu4e1uOdgFcNmb/D6jD6acSZHZKd5HGtui75o0mLayglCPNJwa5mVksCeQ9Xsr9tSt09NZPorX5OTkezhS/TXpd2hISNJsWhcHpOX55sJ7h6t5bTm3acnc1TJbgD2n30Bg0+aDBjllxaL0UDYJXDulUbu9+reIZV/XNqfK5/1zh9dds1gthdX4tCam1d9RVF5DUvGn8yEpZ+bgn71qb24ok78/VmHoVRiRXKcRTSJm3QkQYhHXFvLUfM/YtjsDdz9+mZ+2F/OFc98ypiFG5m1toAjslKYOSKXO849FjAqgSYs/ZwEi8VM2RnQPYtFV+bx6pRTcDgNQc1MTTC7Ibm3lhuoytj+yAWmaG7993tULFvJgO5ZpnVqq9Ue1UTuzT1cdMtO5eeiCvYf8t0z02JR2BxOHlhTYHZdslqUea6viiVfzVBCqcRqbou+eEEsTkEIQMOSytF53bneLXqek5FMSaXdq277+Y+3AZqF4/KYt/5HL//jwnF5FJfXMGttgWlpbios5bu7/sZrrzwGgE5M5IYFG/j3+6V02/wZs0f3o1NGIlaLlUSrMiuONhWWmqLbsOHxG5t2MfG0o3xahICZe+kKKiVYLea5/nJLG1qHoVRiRXKcRTQRH6cgBGBXSaWH/+6lSUO5dPEn5uPl1w72Cgq5/JsP/auAe0fkUl2rGb/EO+jz+KUnAbD4g58Zf3I3jh/Ul/aVZQDsvvtB7j/mHN4u2G++ZvLvezLipG6mcDdsrnFWbmdmnHscCRbFL0UVvPXNHkYOOILnP/YOHD1ZNx64c51f8eG3vuftgv2clduZG//Qh6krvjT7ZobbH9lSouri4xSEJtLQf1daZTcfD+ieRdcs756YO0uqcGrN2wX7mTCsF13apfg8Jyczmc2FB3iojyIn93DzuR8//orM3GO4trjSo2XbmEE9TP+j6xou3+istQVcfWov9h+qNiPs557Y1RRL94T1nMxk9h6sZnqD0cNFZTZTqF+efApWZViiDadONtc6bCkdkAIhPk5BCEBD/93q/EKz/nvKGb35tdh3GaXDafz3kf9sISnB4vOcX4oqOO2uG8g59WQAtmd1ZdLST6np0ZOLF270amDs7n90sbOkimMPy2TZtYOZu24LFqV4u2A/c9dt8Wh07EpYH7NwI06n9jl62DWO4+2C/Wit6dI+1eyPGWoD59aOWJyCEABf/rvs1ERenzqMSlstt7z4lZdvcVFdr0xXeWV5td3rnDnn9+GU47qY95l2wXTW5J7BokE9PHyoLlGbNfIEEt38jy66ZaditShKyo0qIZdFvKmwlJ/rOs83PN/hJ9qflZponuMK1rQG6zASiMUpCIQ2vM0lJmlJCR4ljS9NGsqskSeQk5nMX9cand2XXTOY9OREnv94G8uuGcxLk4byj8MOcsqJ9Q248z/7gTW5ZwCBW8j935c7vYamLRiXx+v5O3lgTQGzRp5Av27tTIvYNSnT/fxF4/JIS/Qd2XaJbksM1kQbsTiFNk+gahbwUS1T95x7hNhV0vj0VYPQdf5Nl79wQPcs7j7/OLYXV5J13p8YsG0zAB/0HMBdU+YyK7O+Y7u7D9WFK63om90HueCkI1h+7RCsFoVGU1ZVy6PvbKVbdiqHtU8hOcHKvPXf8fBFJ9I1K5UD5TYevuhEUhKtdM5M5vD2qVgsyiuyvejKPDqlJ/H61GFxG6yJJySqLrR5AlWzAAErXWprnewvr6HW4STBaqFzRjIlVXZGzf+InIxkppzRm8Pbp9Cuuowj+9RbmWMv/Ss7BpzCgrEDAczmHu5RbfduRu1SE3FqzfbfKpm3fqvRUX5cHt2yU6iocZCaZKW2rvfnD3vLzDSjKWf0NuvQu9aJJrScyHYskai6IASgsWoWf885nZqtReVe1mifnAxeuGYw++oi3H8+9C1H3nSN+fpfd+zjb+np2B2axe//TGmVjRcnDmXvoWqKK2ws37jDjIB3z06lqNxmlm66pyBNWZ7P61OH0bV9qpdV7DrHZQm/PnWYhzCG03fZFkVYfJxCmydQNUug5/zNGyqpspORnMBtr3zNnNnXcWydaG44Ko+eM9ZyxapvSUm0kpJoMTq/n30s7xTsISXRyqy1Bbycv9MY1as1lXbvdnWuCLhLwH2tw30GUDA+y1B8vA1f515ZNWr+R2zZVxb061sqYnEKbZ7Gqln8PbfnYJUpVgO6Z5nbYlutg4RdhXx45x/Ne5w//p9818VI99lZUkWlzcGVz9ZbkUsnnEx2WiIvTRqKQ0OCgsff2crUPxztNwLuEnB/FvOxh2Xy2tRT6ZQeeExFczoW+fvyiMemHeFEhFNoEwTaTjZWMujvOZc1mpOR7DG+d9r36/jzmicAqE3PYPhdq9lxqH4Eb7fsVLb/VuFRtllUVsP4JZ97BGsuHtSdWof2GSyqtDlMAfc39teiFElWFVHxay1NO0JFhFNo9QRjUQXy+fl7zmWp7j1YzYzVm9n7Wxmb511BO5sxzfrgvQ+yc/I05jk0T23Yyui87mblzlPv/mRex9egtsnL8nlx4lDmrd/K/LEDPYJFC8fl0bV9Mg5tTKpMTLCwdMLJHsI7e3Q/Zq39jpuGH0NGUiIJCf69cs0Rv3DOqG9JiHAKrZ5wbCddFqtCU1PrpNapSbRaOLpTOunJVtJ//J6fnrvRPP93k5/h8QmjGDPvQyb/vifThh/j0VF9zph+bN1fzqbCUr+5m040pVU2HE4nqyYOxak1Tq2x1TrYe7CGyW7XW1zXeWlnSRXFFTazfr1gTxkvTz6Fw7NSG74lk+aIX2tp2hEqIpxCq6epFpX79t7h1HxdeIB+3TuYoy1W5xcybfgxHPXQ3ax77ikA9mV0YMjU5+nWIc1svzawZ0evAM9tr25m2bWD+XFfORp8Cld6kpWbhh/j0dRj/tiBOJ2aG1dt8rjepGX5LLt2sFcj5J0lVdQ6nAHfZ3PEL5wz6lsSIpxCq6cpFlXD7f1ZuZ2ZNvwYj7SgOecdzQndsszX3H/Z3Sw98hSPdCDwXw20/5DRVm7phJN5+spBTFzmKVyVNqdX+eXUFV+y4rohPq/ncPr2hyZYAyfPNFf82mJZpgin0KIJJoewKRaVrz6c7lZjt68/5ZQ7hpvnl2zfyelViZybZKVDehJz1v1gzvTxVw1UWmVnZ0kV45d8zhs3nsrLk08xE+nTkiz8tL/Cp0AmWJTP6+09WO1VE79wXB6dMxoXtbYofs1BhFNosQSbRtMUi6rh9t7dalz37FT6/vYrAGWDT6Vs3Tsc1i6Fw+rWkpORzO3n9KVgTxk7S6pYnV/IgnF5PvtogiGGZVUOxrmlJy0alwf43sKnJlm9vghcM4QAM3m+a/sUumSmBAwMCU1DSi6FFou/Usk1Nw7D4aRZPreG1150ZR6Pv/wJbz04yjzn5uvm8OeHryc9yYpGeQwyc5VA2mud5pyiogobtQ4nPxdV+JwyOWHp5x7vY9XEIewurTZbwLnSlI47rB1A/bC4BAvl1bVcFeR4XiE4pORSaJX4CvrkZCSzp7TaI+LcFBFpuL3ftnApbz15p/n88Af/xbxrhrHh+7288fVepg3vQ69O6aQlW+na3ohgu8/msVotHJ6VitOpqbA5KCqvAeo7Ft3zf9963H9nSRX7DtVgUcq0IN2bdAAeW+tO6brNBWhiScjCqZRSgMf/Ea114LCdIEQAX0GfacP7mKIJTa9kMbf3159Ku/PPJvnDDwComnAtBx6dxzKluH/NtxSV2TyS311CnZxg8WsBNnQbWC2YQuqiW3YqxRU2juvajsOzUhoVQ/FRRpegnB9KqTyl1EallB2oBewNfgQh6viartirU3rYKlksZYfImTTeFE02bSL1uWc4IjvNbB3nbxLkjuJKr2MuC9Qlckdkp5GTmUxWahKLrvTsszl7dD9W5xeSmmg1zxMLMn4I1uJ8FigFLgIORW45ghA8vqw3je+UnJArWT79FIYOBasV/v53uP12sNTbGS5r11+qUVqS1euYP/G2WBTH5GSw4rohZo7o8x9vY9rwY8iu68ouxBfBhtv6Atdprd/UWr/f8CeSCxSEQDS03jqlJ/uc8Z2dmhhc9x+HA2bPht/9znj83//CHXeYounqImSrdbDyuiFm8ro7rlryhscCiXdpdS0P/auA4gobWamJjM7rzrz1P1JSFdyGrqndjYSmEazF+SVwHPBTYycKbZd46Mvob0aQr76ZXgGjb76Bfv2M38eMgaefhqz6BHdf6U9LJ5zscxJkckL9fKCGeaO+PidbrcOja7yL+y5o3MXQnO5GQtMIKh1JKXUDcC/wPPALYHN/Xmv9XNgWZASf1gEva62faex8SUeKD+L5jzdQh3czoLJmDYwcafx+8snGVl2poK7jK/0J8Pkl4u9z6tIumQufbGSNzXl/QsgESkcKdqs+HagELgZmADPdfu4JxyIBlFIWYB7wp3BdU4gO/hppuKfkxIqAteo2G9x5J/y//2c8sWoVfPaZKZruW+Aqe63P61TZHB7uAotFebkQXF8e/j6nWqf26WIIpl68rbZ2iyVBbdW11r0ivRCl1BHAcuAojECU0IKI5z9ef7XqKTt/hYEnQm0tTJwIjz8OaWnmOQ2twyXjT2524Mnf52SvdTa5XryttnaLJUHXYiml/qSU6lz3+9VKqX8ppR5QSoUr7DcQKATygINhuqYQJQKNmHARqwCGr7Sl1/f/lw6/H2oI5UMPweLFHqIJ3tbhvPVbvcbthtpCLdDnZLEoOqYnmV3diytsQX1Gvt5fW2jtFkuC9XHegbEtHw5YgfeApcDvgXVa65vDuiiltgN/FR9ny6ExH2esfaCugIy9qIjDe3evf+KXX6CX7w3VrpJKhs3e4HFsQPcsFo4bSK1T49CalESrz9EU/gJlTR1F3NhnFA+BudZGIB9nsMK5HbhBa/0vpdRC4Dit9elKqcHAm1rrLkFcIwXo5ufpfVrrsgb38yucSqlJwCSAHj165O3YsaPR9yBEnkB/vHERwPjuOzjhhPrHFRVeVqY7vtZ8Vm5nbv7jMV5RdHdxC+ZLxNfnFBefkWASjuBQZ2Bz3e8jgDfrfi8G/LeW9mQQsNXPz+ggrwGA1nqx1nqQ1npQTk5OKC8VIoi/gAiA0+lk5ohcXpo0lEVX5jGge1b0fKBaw113waBB0LkzPPywcSyAaILvLfA95+eaogm+g2CNBcr8fU7x7CcWPAk2j7MAGK+U2g8cDryhlEoCbgO+CuYCWusPaVDjLrQNnE7NbxU2Zq0t8Gir9vzH2yIfwCgthUmT4JVXoE8f+OADOOywoF7qKyc0GHFreI5rAmalrZaiMvxuoyXI03IIJR3pZmAB8ITWeivwGHAhcEuE1ia0EoorbF5W2ozVm7nn/NzIBjDmzYPsbHjtNaNssqDAr2j6C1w1tA6DCYK5nzOgexbTz+7LrLUFnD7nvYBzxyXI03IINh3pvbqIenutdUnd4QeBW7TW0uRDCIg/K81qaXx0bZNwOiEnBw4cMB5/8AGcemqA04MPXAXTTd79HH9NQHz5Ldvq/J6WSCht5Q4HblJKHYcRWd8CLAZ+iMTChNZDVLeg+/bBVVfVi+a2bdCzZ8CXhDIFMxhxcz+n0uY7ad5W6zBr3t2vIe3hWgbBtpU7HUMof099QOdU4Eul1O/CvSitdc9gUpGElkHUtqArVxpb8Q8+gKeeMizPRkQTQg/KBAqCNTwnLSnB59be4dSMmv8Rw2ZvCLh9F+KTYC3OucA/tdZ3uR9USv0deARDRAXBJxHfgtrtcO+9RlcjgM8/90w7aoRIWsS+tvaLrszjr/8qaHazZSF2BCucxwOX+zj+HEbQSBBM/OUpRkQUNmyAP/zB+H3SJHjssUbTjBrSnLnijeHrS8PpdHp1QZK0o5ZFsMK5DRiCd1u5ocC+sK5IiAnhqjyJRoWQa63JD95Pu7kPGwfHj4dFi5p0vUhbxA2/NIrKaiTtqIUTrHDOARYppY4HPqs7NgS4AbgjEgsTokc4xS6UQEtT1/rjtn1sueI6Rn72LwB2vLyG7qNHBN94wQfRDMpE0sIVokOw6UhLjTaZ3ISRt1mFEU0fr7V+LXLLE6JBOMUu0tUvpV98xbFDBnIssGDIGB79/TgO+yWd1ytsLcY/KGlHLZ+g05G01ksxGnsIrYxwil3EAi1aw4MPkv3ww5SmZPDEKZfy7OBRzVprLJG0o5ZN0MKplDoHo7HGcYATo3b9Sa31RxFamxAlwil2EdmGbt9udjCqHTiIq86dweba+hQf8Q8K0SbY7kiTgCeAVcAXGAnwgzA6wo/VWq+O5CIDIW3lmk+4AzphbXH2ySdwyinG7x074ty9hy3FVXE5okNoXYSjrdwO4L667br78YnAdK1133AstCmIcIaHuOvnWFsLd98Njz4Kqanw17/CzTfH51qFVkkg4Qx2q55FfTTdnQ+AfzR1YUL8EAufm18B3LsXxo2D9eth1Ch47jmPaZPiHxRiTbDC+QQwVyl1ldb6NwClVAbG5Mt5kVqc0Hrx5x449h+zUHPnQkoKzsceo/iaKdgcTpLKakKyLMUqFSJJsMI5nLqZQEqpX4BajKFq6cAupdSVrhO11j3Cvkqh1dEwBeq3/SUcd3j7+uff/4iDvY7hqgUfN2mMRHN8tiK6QmMEK5wLI7oKoc3hngLVrXQvz6yeZT530rSVZGwoZf7htSwYO5DdB6tZ+N7PQeeWNicvNdazkYSWQbAJ8M9HeiFC/BANi8uVAnX5/y1k4mevU52YzN9H/4WBD0yndFk+pSVVTF3xJTNH5DJrbQGzR/dj7rotQeVrNicvNdKVT0LrIKBw1vkx/1j38L9a6wql1G0YHeETgJeAP2utqyO7TCFaRMvi6qhqefPbZWR/8goAV9z8PLdOPIu567aY5+wsqSIrNdHsGD9r5AlB5Ws2Jy9V5v4IweC3vLduguU24AXgaWCrUmo6MANjbMajGEPWZkZhnUKUaGzQWFh46SUsmRlkL19K5Z+ns2tfKfPuHMXzH29jU2GpeVq37FRKq+zmOnp1Sg8qkb45/T+DGY0hCIEszscwRPN2rbVDKXUDRgT9Sq31SgCl1CaMOUR3R3ylQlSIqMWlNZx7LqxbZzz+xz9Iu/VW0jAs3Vv/1JeCPWUeA91cFmi37FTSkq1BWb3NqQWXBhxCMAQSzpMwmni4/mIWAo8Dm9zO+QpjpIbQSohYrXlpKVx3Xb1ofv019OtnPt1Q7BxOzV//VcCmwlJTvDqlB+9jbGqupzTgEILBb+WQUsoJHKa13u92rAzor7X+pe5xF2C31jpm+xipHAovEfFxbtxoDEtLSICHHoLp08Hi6SVqGJDKTk2kpMou4iXEjKZWDum6H1/HhVZKQ4srNclKrVOz52BV6AJWWwuzZhliCbB6NVx4oddpkgIktDQCCacCnlJKuUfMk4FHlVLldY9TIrYyIWa4trnNErSvvoIBA4zfL7nE6M7uVjbpjqQACS2NQE2znwcqAIfbzwrgoNvjCowAktAKaXKE/a236kVz8GB48UW/ogmSAiS0PPxanFrrCaFerG7q5Ryt9YFmrUqIC0IWtMpKmDkT/lHX9+Xll+Hiixu9T6CAlJQ/CvFIc8a0+OIGjE5KQisgpJzGX36Bvn0N0ZwyxRDRIEQT/OddJlrh+z2HZP64EHcE1Y8z6Is1iLpHA4mqR46gfZzXXw8rVhiR8oceghtuaNK9XJZlYoKF8upadhRXMvONb70sUfF9CtEgHP04hTZIozmNxcXQqVP9C7Ztg549m3wvlxgWldVw1XOf8ejF/cX3KcQlIpxCQPwmkn/7LZx4Yv3jykqjU3sYcPlWS6vsMn9ciEvC7eMUWjFOp6boUDXlk65Hn3wyuksXozu71qZoOp2aorIadpVUUlRW0yR/pMu3uvC9n5k9ul+Tas4FIZKIxSkEhdOp2fpjIbsvHseZ3/6PspQM9rz9IUef0Nv89g1XIrt7vfjcdVuYNfIEenVKJy3ZSqf0ZImqCzFHgkNCUJT//REy7pqB3WJl7mlXsnjwRRzRId0jUFNUVsOo+R+FJZgjaUhCrIlmcOgZjAR5obXgdMJhh5FRVATAVZc8yMYj+wPegZpwJrLLQDYhngno41RKDVRKrVBK/aKUOqSUqlZK7VdKfaaUmq2U8uiMpLW+VWtd3JSFKKU6192rqO4ezymlJCc0luzdC2efDXWiOfqOlaZognegRnpZCm2FQI2Mz8IY/3sIeASYj2FNPgYsAfoB3yqlBoRpLSuBbsCfgPOAE4Fnw3RtIVTeeAO6doWPPoKnn8ZZ6+Cv084PGKhpTgNhf0GlpgSbwhGgEoRABGorlw88obVe6nZsKLBUa31s3eMHgTO01qc1axFKdQMKgWO11lvqjp0K/A/I1FpX+nut+DjDTHW1UTY5d67x+Ntv4fjjgcB+R/fnlFJYFVgslqB8k/6CSn1yMthaVB5SsEk6LQnhIpCPM9BWvQ+wscGxL4DedX04AZ4DwmFxHgTOB7a6HdN162sXhusLwfDOO0Za0dy59WWTdaIJ9X7HI7LTyMlM9hDNLfvKzNLISxZt5EClPeiAjr9mIvvLa0JuMhKV0R9CmyeQcH4FzFBKuTuoJgPlWut9dY8vAHY0dxFa6zKt9b+11k63wzcD32mt9zb3+kIQPP44/OlPxu/jxsGCBUHnZjZXrPwFlWodzoDBJl/rkk5LQjQIFFWfBqwHzqzbtncDBgLjAZRSrwBnA2OCuZFSKqXuGr7Yp7Uuczv3VuDiuuv7utYkYBJAjx49grm94I+KCrjlFnjmGePxe+/B6aebTwez9W2uWPnrjpRgtQTsmuRrXR0zkqTaSIg4fi1OrfVXQF/gCWAv8C+MHM2Vdac8CRyjtX47yHsNwtiK+/oZ7TpJKfUXjAma07TW7/hZ22Kt9SCt9aCcnJwgby948c03kJFhiOadd4LN5iGaEJw12dxour+gUueMZL/BJn/rSrCoJgeoBCFYAuZxaq1/A/7R8HhdMOd/DbbWAdFaf4jRVd4vdcGmmRii+VSw1xY8adhpKMGiqLK5BXQUhpW5eDEcdhjcd5/h0/RBMNZkcydDBmom4u+4v3VV2Rwer3G9/yaN/hAEPzQ1Ab4A6I8xdz0sKKVuBu4BJmutF4fruq2FYCtpfG1h54zpxyP/2UJReQ1Lz+jE0UPrcjGPOsoYpNa5s9/7BjP1MhyTIf0lvPs7HmhdYRn9IQgBCJTHua0u8d3rB0gH3nd73CyUUj2A2Rgz2tcopQ5z+2nzzqmGUetADX19bWFve3UzU87oTedvv6wXzS5d4McfA4omBJ+b6S/iHimCWZdE2IVIEcjifAbDAvwMz7lCCsPvOR8oCtM6LsQYBDe17sedPsBPYbpPiySUYWa+trC7i8s56aE7ePnFZezKzCFt1v1k39zwY/ZNY9ZkrGrKg7FyJcIuRIpAM4ceUkq9hiGgl2JsobcBKKUeA14OVzMPrfWTGMEmwQehCEDDLWxO+QEWrHucLj99yXu98vj71fey/BqfyQp+8bddjvVWuLF69mDcDILQFALWqmutvwd+B/wb+EQp9WellDiHokwoUWv3LezM9U/z+VNXMWDn98y/7DbumTSbxyedEbYIc7xvhZtTAioIgQi6rZxS6ihgMdAeOAE4Pprt4/zRFkouQ7XsnBWVWDLSzceln2+iovcxYd9K7yqpZNjsDV7HP5pxJkdkp4XlHs1F2tMJTSUsbeXqRPKPSqmJwOVAVSMvEcJESFHrX37Bcuml9Y+Li8nq0CEio0dbwlZY2tMJkSBQVN2rRlwplYkhmOuAwUqpNj96I1qdeIKKWt9/P/TuDT/9BKtWGSMtOnSI2PqjtRWWbkdCvBHI4ixRSnXVWu8HUEodA2yoe812jGj3LqXUuVrrnRFfaRwSzBY6KlvFigq4+WZ4tq4L31dfwZFHhmX9gQhH/mak1ygIkSCQxdjwX+VjwCdAD631EKAH8AswL0Jri3saC46Ekn/ZZFasMMomn30W7rrLKJsMQjSDWX8whDN/05dlGe8BKKFtEkrl0ADgQq11DYDWulwpdTferefaDI2lCYWSf+kiaAtVa7j4Yli92nj80EOGcIZx/dHEn2XZIS0xbtYoCC4CWZwacHdWbcWoGHInGyijjdJYmlCowhS0hVpSAqNH14vmN9+ELJrBrD+a+PuScWjiZo2C4CKQcNYA2+vKKt/CsE7nK6VSAZRSl2A0Mn4l8suMTxoLjoQqTEFtSz/+2Aj4vP46zJkDDgeccEJE1h9N/H3JaK3jZo2C4CLQVj0dOArIdfs5DnCZS/8A/oNRWdQmaSw4EmrXoIAWqt0Ot98OTzwBHTsa/7388oiuP5oESm3q2yU1LtYoCC6aNFddKZUNjAWuBgZqrWO2b4r3BPhQour+5pK/eWoa2aedYhy46CJ47jlo3z4ay48aEj0X4o2wJMDXdSk6D0Msz8fwf24ErgrHIlsroSRg+7JQV3YrIfu04cYJw4bBq69CFKteo1V5E0/WryA0RqPCqZTqjzEu4wqgE7AHSARGaK3fiujq4phICIq7eNgPHiL7vrtIveMZSE+HF14wrM0oEm0rUKp8hJZCoMqhW5VSXwFfAucCS4FTge4YEfdmD2lrqUQyP9NiUeQU7eLw84aT+twzMHUqFBVFXTQh/pt4CEKsCBRVfxRIBcYBuVrrGVrrT3VTnKKtjIgKyiWXGGWThYWwbBk89ZQ5bTLaxFOepyDEE4G26mOBy4AlwIK6lKTXgTa7PXcREUEpKvLsxh5k2WQkaQlNPAQhFgSacNhA1QAAEMRJREFUcrlKaz0S6ArcBnQBVmJ0fbcAw5VSbTKZLuyJ45s3wzHH1D+uqvIQzVg1uYinPE9BiCdCSkdSSnXFaCl3Gca43xJghdZ6WmSW1zixSEcKW9BEaxg/3uhk1LEjzJ8Po0ZF5l5NRPpZCm2VQOlITcrjrLvo0RiR9su01rnNWF+ziFUeZ7MF5cABuO46owIIYN8+n4PT/OV2Bqp3FwSh+QQSzib309Ra/6S1fjCWohlLmtUV6JFHDAtzzRqYO9com/QzbVICNIIQfzR1rrrQFBwOw3e5a5fxePVqGDky4EskQCMI8Ueb7+AeNXbvhrPOqhfNwsJGRROiG6CRTuuCEBxicUaDt96C886DtDSjznz8+KDLJqNVihjrIJQgtCTE4owk5eVwyy2GaAJ8+CFMmBByrXk4u6z7Q6qEBCF4xOKMFG+/DWefbfw+daoRBIpwBVBzIv0ShBKE4BHhjATPPQfXXmv8fuWVRtlkhGnuVluCUIIQPLJVDycHDhiC6RLNDz4wuhpFgeZutaVKSBCCRyzOcPH113DSScbvd99tzDhPiN7H29yttvTDFITgEeFsLlobHY3efBMOOwwefRSuuCLqywjHVlv6YQpCcMhWvTn8/DNYLPVd2b/+OiaiCbLVFoRoEjcWp1LqcOBJYDhQBTwP3K21ro3pwvzx4Yfw+98bvx9+OPz6K1hjF0iRrbYgRI94sjhfBpKBocAlGA1EZsR0Rb5wOOCyy+D00+Goowxrc9euqIlmoOqeaOR7CoIQJxanUioTKARmaK1/Bb5XSr0CnA48FNPFubN7N4wbBxs2wJAhRq5mu3ZRu71U9whCfBAXFqfWukxrfXmdaKKUOh64EFgf25W5ceONcMQR8OmnRp7mxo1RFU2Q6h5BiBfiwuJ0Ryn1EcZQuHwMn2dssdkg2S3S/PnnkBubTnpS3SMI8UHULE6lVIpS6mg/P5lup07FCBClAqv8XGuSUuoLpdQXRUVFkVv0Tz8Zs8xdlJTETDQhAiM7BEFoEtHcqg8Ctvr5Ge06SWv9tdb6XeBa4AKlVM+GF9JaL9ZaD9JaD8rJyYnMaufNgz59jJSj114z8jWzsiJzryCRlCNBiA+itlXXWn8I+IxgKKU6KKUu1Vq/5Ha4oO6/nYDtEV5ePWVlcPPNsGSJ8firr6BHj6jdPhCSciQI8UFcBIeADsCLSqmBbsfyAAfwY9RWsWyZEfBZsgTuuQfs9rgRTRfNTTmSZsWC0HziIjiktf5JKfUfYJFSaiLQHlgMPKG1PhSFBcDkyfD008bj+++H++6L+G2jjaQzCUJ4iBeLE2As8D1GCtKrwJtEIwG+uBguuqheNL/7rlWKJkg6kyCEi7iwOAG01geAq6J6U/eyyUcfNbq1W+LpuyS8SDqTIISH1qsSgbDbjQqg00+HXr3gxRfhz39u1aIJks4kCOEibizOqJJUl75z9tnw8stRrwCKFa50poY+TklnEoTQaHvCqbVhbe7fb0yfDHFwWktG0pkEITy0PeFUykg7aqNIs2JBaD5tTjibMwlSEAQB2phwSh6jIAjhoHWHkRsgeYyCIISDNmVxNpbHKNt4QRCCoU0JZ6BJkLKNFwQhWNrUVj1QW7bmbuOb2jxDmm4IQsujTVmcgfIYm1OO2FRrVaxcQWiZtCmLE/y3ZWtOOWJTrdVwBavEahWE6NLmhNMfzemu3lRrNRxNN1xW66j5HzFs9gZGzf+ILfvKRDwFIYK0qa16IJpTjhgo6BSJ17njz2p9feowqRAShAghFqcbTe2uHshaDbSNDscMIWkVJwjRRyzOMODPWgUCBn/C0XQjHFarIAihIRZnmPBlrQYT/GnuDCGZfCkI0UcszggSjW20tIoThOgjwhlBorWNllZxghBdZKseQWQbLQitE7E4fRCuZh+yjRaE1okIZwPCXQYp22hBaH3IVr0B0rNTEITGEOFsgCSUC4LQGCKcDZDZ44IgNIYIZwMkEi4IQmNIcKgBEgkXBKExRDh9IJFwQRACIcLZQpHBcoIQO0Q4WyAyckMQYktcBoeUUncrpbbHeh3xiuSaCkJsiTvhVEodB8yM9TriGck1FYTYElfCqZSyAM8Cn8d6LfGM5JoKQmyJK+EEpgGVwPOxXkg8I7mmghBb4iY4pJQ6CrgbGAqcGePlxDWSayoIsSVqwqmUSgG6+Xl6H/A08IjW+mellAhnI0iuqSDEjmhanIOA//l5bhKQBfwjmAsppSbVvYYePXqEZXGRRvIuBaH1oLTWjZ8V6UUotQE4FbDXHUoAkjD8nbla61/9vXbQoEH6iy++iPwim4HkXQpCy0Mpla+1HuTruXgJDo0DjgdOqvt5ANhd9/vuGK4rLEjepSC0LuIiOKS13uX+WClVBNRqrX+K0ZLCiuRdCkLrIl4szlaN5F0KQusiLoVTa/2M1rpnrNcRLiTvUhBaF3GxVW/tSN6lILQuRDijhORdCkLrIS636oIgCPGMCKcgCEKIiHAKgiCEiAinIAhCiIhwCoIghIgIpyAIQoiIcAqCIISICKcgCEKIiHAKgiCEiAinIAhCiIhwCoIghIgIpyAIQoiIcAqCIIRIm+yOJIPTBEFoDm1OOGVwmiAIzaXNbdVlcJogCM2lzQmnDE4TBKG5tDnhlMFpgiA0lzYnnDI4TRCE5tLmgkMyOE0QhObS5oQTZHCaIAjNo81t1QVBEJqLCKcgCEKIiHAKgiCEiAinIAhCiIhwCoIghIgIpyAIQoiIcAqCIISICKcgCEKIKK11rNfQLJRSRcCOCF2+E/BbhK4db8h7bX20lfcJkXmvR2qtc3w90eKFM5Iopb7QWg+K9TqigbzX1kdbeZ8Q/fcqW3VBEIQQEeEUBEEIERHOwCyO9QKiiLzX1kdbeZ8Q5fcqPk5BEIQQEYtTEAQhREQ4G0EpdbhS6jWl1EGl1F6l1GylVKvsY6qU6qyUWqGUKlJK7VdKPaeUyor1uiKFMnhbKXVdrNcSTpRSyUqpxUqpkrp/s7fHek2Rpu49f6uU+mM07ifC2TgvA8nAUOAS4ApgRkxXFDlWAt2APwHnAScCz8Z0RRFCKWUB5mG819bGHOAU4I/AZOAepdRlsV1S5FBKpQCrgOOjdc9WaTmFC6VUJlAIzNBa/wp8r5R6BTgdeCimiwszSqluwHDgWK31lrpjNwP/U0qlaa0rY7rAMKKUOgJYDhwFlMZ4OWFFKZUOTAQu0FrnA/lKqUeAG4EXY7q4CKCUysX4wo/q7BuxOAOgtS7TWl9eJ5oopY4HLgTWx3ZlEeEgcD6w1e2Yxvg30i4mK4ocAzG+EPMw3ndroj/GDun/t3f+sVqWZRz/fGE452oUmlEZEssZ5JIRFmOxrLnppHD90Dgnt479clni7HezbJJs4WbLUMD+KVpzUYgmRVLNUkk0IaWApJGdfgihRYYmxA+//XHd73p4gcP7tPc9b7zn+mz3znNf9/M85/vs3a5dz33fz3WtrdjWAudI6sVSrm8GfkZE2MNGRpwtIukXwExgA3Bzl+W0HdvPAKubzFcBm23/tQuSOobtVcAqAKnnivS9DNhle2/FthM4ATgV2NEVVR3C9pLG8XD+liPecZb5kdOOMryzOBSAK4CTgUXEfMqcYZDXVmo8K5KuBi4Gzh8Obe2kznP2ICcB/26yNfpZobBNjHjHCUwH7j/K2GXANwFsbwSQ9AFgnaSJtgeHQ2AbaelZJX2CWGC40vZPh0daW2npOXuUvRzuIBv9npmn7jYj3nHaXstRJpYljZP0HtvLK+Yt5e8pwGCH5bWVoZ61gaT5wBeAebZvGRZhbaaV5+xhngBeLOkE2/uKbTwRde7qnqzeIheHhmYc8B1J0yq21wMHgd91R1LnKKvonwcut72o23qS/4lHgX3EfHyDNwEbbB/ojqTeY8RHnENhe5uku4FbJX0IGEt8E7vI9u7uqmsvkiYAC4ElwF2SxleGn7J9sDvKkjrYfk7SMmCxpAEi2vwksUUpaRMZcR6b9wK/JbYgrSBWY3txA/wcYi7sCmLltdpe1UVdSX0+DjwM3AMsBebb/m53JfUWmeQjSZKkJhlxJkmS1CQdZ5IkSU3ScSZJktQkHWeSJElN0nEmSZLUJB1nkiRJTdJxJh1FUp8kl+/fq/ZRkp4rY9V2WMZ5SddIGmyyTZV0v6RnJD0iqXYyEkknSnpa0m9qP1gyoknHmXSaPmAb8L4m+yTgRGAikQqt0Q7JjylpMvHtfNX2EmJz9+PAG4hM7islTa+p7ULgH8Dkps9qk2RIcgN80jEkjSO+PHo/kXF9mu1Hytgc4Gu2Jw5x/SgiCe9B4JWNc0v0Og94te39xbYUGGu7r4a+FcCTRPLf9bavqvuMycgkI86kk7yLyMqznEiKMlAZmwJsPcb184hUaMua7JOIpBX7K7aN1MgCXsqizAZ+DvwA6Jc0pjI+IOlBSQsk7Zb0Z0mXN91jQNIWSXskbZB0bmXs3GLbI+lPkj5XGRuU9DFJD0jaK2mjpHNa1Z50n3ScSSfpB35UsvJ8n0Od0xTghZLuk7RD0mpJZzYulDQJuIYoNtbMTg5PVHw6keqvVd4JjAbWFG2nEI60yjQiG9ZM4FpgkaQLi74B4Bbgy0TE+mNgtaQJpUTF7UReg8nAR4Frm+ZhvwjcALyOqHvUc1UFehrb2bK1vQEvJ16x+0p/BlHD6KLSf5hInvJWYp5yJbCdeN2GSKryqXL8QWCwcu8pwAGiANmYcu8ngX019N0NrKn0HwPuqPQHiGj5pRXbMuDOcvwrYGHTPdcRCaDHlWf9SGVsJjC+HA8CN1bG5gAHu/2bZWu9ZcSZdIq5hONs1DF6iHCMjUWiWcB02/fY/iURnY4BLiop/F4EfOVIN7a9hXBs1xMZz78F3AS0lOpP0qlERc87K+aVwGxJ1aj1cds7K/31RARJ+ftQ063XAZNt7yIiyMWSnpB0KzDKh9Zu+n3leDcwqkeLqfUk6TiTTtFHOMK/SzoA7CdWzWdLOtn2Xtv/apzsKC72B+AVhBM9C/inpGcJJzRB0rMlbyi2v01EdqcBZxKr44MtaruYyEV7s6QDRd9nit7+ynnNiX9HA8+X4z1HuO/o0rB9JfAawqFPAe6VdFnl3H2HXz5is9Yfd6TjTNqOpDOIuj9XA1Mr7W1EtcX+EonNrVzzAuAM4pX5UuC1leuuI6LVqcD2svCywvbztnc43nfnEGViW6EfuJeYm2z8j7OJ7OnVbVOTJFVLI08Hfl2OHwPe2HTfGcBWSeMlLQb+aPsG27OAbwCXtKgv+T8nM8AnnaCPWPBY6kPL1G6S9ADhnNYACyRtJ2rhLCC2Lq1yU4kHSU8BB2xvK/2twAWl1MddRAG2GcRc6JBIOp1Yfe+3valpbDHwdUlnFdNJRPb/64iphUuA88rYjcAySZuBB4uGs4mtV7uAdwCjJTXmPGcB3zuWvuT4ICPOpBP0Abc1Oc0GS4iV6puI+c/l/Heu8IJmp3kkbO8A3g18GNhELDCdZ/svLWibS7zW33GEsduI+caB0t9OvP6vBz4NXGr7vqLhduCzwHwiCn0LcL7tzY4iaW8nouZHgR8CPwG+1IK+5DggN8AnyREo242ut320+uzJCCYjziRJkpqk40x6CklfLavvR2sLu60xOf7JV/WkpygJQMYOccrTtv82XHqS3iQdZ5IkSU3yVT1JkqQm6TiTJElqko4zSZKkJuk4kyRJapKOM0mSpCb/AcvwtTvZA+JtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "#\n",
    "# linear regression to fit slope\n",
    "#\n",
    "slope_by_cell = {}\n",
    "for cell in cells:\n",
    "    f, ax = pyplot.subplots(figsize=(5,5))\n",
    "    x = smf_gene[cell + '_Aposn'].values\n",
    "    A = np.vstack([x, np.ones(len(x))]).T\n",
    "    m, c = np.linalg.lstsq( A, smf_gene[cell + '_Bposn'].values, rcond=None)[0]\n",
    "    sns.scatterplot(data=smf_gene, x=cell + '_Aposn',y=cell + '_Bposn', ax=ax)\n",
    "    plot( smf_gene[cell + '_Aposn'].values, m*smf_gene[cell + '_Aposn'].values + c, 'r--')\n",
    "    print(cell + ': lst squares slope = ' + str( m ) + ', int = ' + str( c ) ) \n",
    "    slope_by_cell[cell]=m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using uncorrected mean to calculate SMF:\n",
    "for cell in cells:\n",
    "    smf_gene[cell] = smf_gene[ [cell + '_Aposn', cell + '_Bposn'] ].mean(1)\n",
    "    smf_gene.drop( [cell + '_Aposn', cell + '_Bposn'], axis=1, inplace=True )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>A549</th>\n",
       "      <th>HT29</th>\n",
       "      <th>OVCAR8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENE1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AARS</th>\n",
       "      <td>-0.704722</td>\n",
       "      <td>-2.294667</td>\n",
       "      <td>-3.136778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD16A</th>\n",
       "      <td>0.723167</td>\n",
       "      <td>0.673889</td>\n",
       "      <td>0.224000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD4</th>\n",
       "      <td>0.344667</td>\n",
       "      <td>0.524111</td>\n",
       "      <td>0.261278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD5</th>\n",
       "      <td>-0.008611</td>\n",
       "      <td>-0.105722</td>\n",
       "      <td>-0.101111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABL1</th>\n",
       "      <td>0.273000</td>\n",
       "      <td>0.235389</td>\n",
       "      <td>0.338111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             A549      HT29    OVCAR8\n",
       "GENE1                                \n",
       "AARS    -0.704722 -2.294667 -3.136778\n",
       "ABHD16A  0.723167  0.673889  0.224000\n",
       "ABHD4    0.344667  0.524111  0.261278\n",
       "ABHD5   -0.008611 -0.105722 -0.101111\n",
       "ABL1     0.273000  0.235389  0.338111"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smf_gene.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 841 entries, AARS to ZSWIM6\n",
      "Data columns (total 3 columns):\n",
      "A549      841 non-null float64\n",
      "HT29      841 non-null float64\n",
      "OVCAR8    841 non-null float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 66.3+ KB\n"
     ]
    }
   ],
   "source": [
    "smf_gene.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "smf_gene.to_csv('./Data/smf-meanFC-meanRepl-841genes-3cells.txt', sep='\\t', float_format='%5.4f', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>A549</th>\n",
       "      <th>HT29</th>\n",
       "      <th>OVCAR8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENE1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCF3</th>\n",
       "      <td>0.857556</td>\n",
       "      <td>0.400056</td>\n",
       "      <td>0.677444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOAP1</th>\n",
       "      <td>0.787833</td>\n",
       "      <td>0.539111</td>\n",
       "      <td>0.407056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRY1</th>\n",
       "      <td>0.787389</td>\n",
       "      <td>0.320444</td>\n",
       "      <td>0.225333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABHD16A</th>\n",
       "      <td>0.723167</td>\n",
       "      <td>0.673889</td>\n",
       "      <td>0.224000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHMT1</th>\n",
       "      <td>0.719278</td>\n",
       "      <td>0.669333</td>\n",
       "      <td>0.453333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             A549      HT29    OVCAR8\n",
       "GENE1                                \n",
       "TCF3     0.857556  0.400056  0.677444\n",
       "MOAP1    0.787833  0.539111  0.407056\n",
       "CRY1     0.787389  0.320444  0.225333\n",
       "ABHD16A  0.723167  0.673889  0.224000\n",
       "SHMT1    0.719278  0.669333  0.453333"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smf_gene.nlargest(5, 'A549')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "        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>A549</th>\n",
       "      <th>HT29</th>\n",
       "      <th>OVCAR8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENE1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WDR3</th>\n",
       "      <td>-3.853722</td>\n",
       "      <td>-2.919111</td>\n",
       "      <td>-3.535722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIF11</th>\n",
       "      <td>-3.360889</td>\n",
       "      <td>-3.094167</td>\n",
       "      <td>-3.224722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPL8</th>\n",
       "      <td>-3.350167</td>\n",
       "      <td>-4.071944</td>\n",
       "      <td>-3.739389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCT2</th>\n",
       "      <td>-3.242444</td>\n",
       "      <td>-3.427389</td>\n",
       "      <td>-3.864889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DHX15</th>\n",
       "      <td>-3.223444</td>\n",
       "      <td>-2.724611</td>\n",
       "      <td>-2.797611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           A549      HT29    OVCAR8\n",
       "GENE1                              \n",
       "WDR3  -3.853722 -2.919111 -3.535722\n",
       "KIF11 -3.360889 -3.094167 -3.224722\n",
       "RPL8  -3.350167 -4.071944 -3.739389\n",
       "CCT2  -3.242444 -3.427389 -3.864889\n",
       "DHX15 -3.223444 -2.724611 -2.797611"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smf_gene.nsmallest(5, 'A549')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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