code/rnaseq.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import gzip\n",
    "import re\n",
    "import seaborn as sbn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sbn.set(style='white', font_scale=1.5)\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NM_000014.5</td>\n",
       "      <td>chr12</td>\n",
       "      <td>9220303</td>\n",
       "      <td>9268825</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>9220418</td>\n",
       "      <td>9268445</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>132,42,103,69,91,128,219,224,181,75,157,88,177...</td>\n",
       "      <td>0,475,1032,2037,2780,4651,4945,6852,9048,9638,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NM_000015.2</td>\n",
       "      <td>chr8</td>\n",
       "      <td>18248754</td>\n",
       "      <td>18258723</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "      <td>18257513</td>\n",
       "      <td>18258386</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>101,1216,</td>\n",
       "      <td>0,8753,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NM_000016.5</td>\n",
       "      <td>chr1</td>\n",
       "      <td>76190031</td>\n",
       "      <td>76229364</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "      <td>76190472</td>\n",
       "      <td>76228448</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>471,88,98,70,101,81,131,109,141,96,249,988,</td>\n",
       "      <td>0,4054,8297,8506,9181,10444,15633,21459,25072,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NM_000017.3</td>\n",
       "      <td>chr12</td>\n",
       "      <td>121163540</td>\n",
       "      <td>121177811</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "      <td>121163688</td>\n",
       "      <td>121177251</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>194,164,150,112,152,171,138,96,57,713,</td>\n",
       "      <td>0,1288,11248,11618,12099,12542,12795,13082,134...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NM_000018.3</td>\n",
       "      <td>chr17</td>\n",
       "      <td>7123149</td>\n",
       "      <td>7128586</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "      <td>7123303</td>\n",
       "      <td>7128416</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>216,76,66,73,65,135,145,130,126,199,105,87,63,...</td>\n",
       "      <td>0,291,633,773,935,1093,1707,2121,2346,2836,330...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            3      0          1          2   4  5          6          7   8   \\\n",
       "0  NM_000014.5  chr12    9220303    9268825   0  -    9220418    9268445   0   \n",
       "1  NM_000015.2   chr8   18248754   18258723   0  +   18257513   18258386   0   \n",
       "2  NM_000016.5   chr1   76190031   76229364   0  +   76190472   76228448   0   \n",
       "3  NM_000017.3  chr12  121163540  121177811   0  +  121163688  121177251   0   \n",
       "4  NM_000018.3  chr17    7123149    7128586   0  +    7123303    7128416   0   \n",
       "\n",
       "   9                                                  10  \\\n",
       "0  36  132,42,103,69,91,128,219,224,181,75,157,88,177...   \n",
       "1   2                                          101,1216,   \n",
       "2  12        471,88,98,70,101,81,131,109,141,96,249,988,   \n",
       "3  10             194,164,150,112,152,171,138,96,57,713,   \n",
       "4  20  216,76,66,73,65,135,145,130,126,199,105,87,63,...   \n",
       "\n",
       "                                                  11  \n",
       "0  0,475,1032,2037,2780,4651,4945,6852,9048,9638,...  \n",
       "1                                            0,8753,  \n",
       "2  0,4054,8297,8506,9181,10444,15633,21459,25072,...  \n",
       "3  0,1288,11248,11618,12099,12542,12795,13082,134...  \n",
       "4  0,291,633,773,935,1093,1707,2121,2346,2836,330...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# human transcriptome bed file\n",
    "## source: UCSC table browser\n",
    "## http://genome.ucsc.edu/cgi-bin/hgTables?hgsid=698225799_VBVYkJAZFNjxKaJnafKIkQY9ZcPB&boolshad.hgta_printCustomTrackHeaders=0&hgta_ctName=tb_ncbiRefSeq&hgta_ctDesc=table+browser+query+on+ncbiRefSeq&hgta_ctVis=pack&hgta_ctUrl=&fbQual=whole&fbUpBases=200&fbExonBases=0&fbIntronBases=0&fbDownBases=200&hgta_doGetBed=get+BED\n",
    "with gzip.open('../raw_data/transcriptome/hg19_allTranscriptome.bed.gz', 'tr') as handle:\n",
    "    dfHT = pd.read_csv(handle, sep='\\t', header=-1)\n",
    "\n",
    "# Outrank the haplotype chromosomes\n",
    "dfHT = dfHT[~dfHT[0].str.contains('_')]\n",
    "dfHT = dfHT.sort_values(by=0).reset_index(drop=True)\n",
    "# Eliminate the redundant refSeq name, keep the first record (They should be the most common ones after outrank the haplotype chromosomes)\n",
    "dfHT = dfHT.groupby(3, as_index=False).first().reset_index(drop=True)\n",
    "dfHT.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>TSS</th>\n",
       "      <th>TSS1</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr12</td>\n",
       "      <td>9268824</td>\n",
       "      <td>9268825</td>\n",
       "      <td>NM_000014.5</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>9220418</td>\n",
       "      <td>9268445</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>132,42,103,69,91,128,219,224,181,75,157,88,177...</td>\n",
       "      <td>0,475,1032,2037,2780,4651,4945,6852,9048,9638,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr8</td>\n",
       "      <td>18248754</td>\n",
       "      <td>18248755</td>\n",
       "      <td>NM_000015.2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>76190031</td>\n",
       "      <td>76190032</td>\n",
       "      <td>NM_000016.5</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "      <td>76190472</td>\n",
       "      <td>76228448</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>471,88,98,70,101,81,131,109,141,96,249,988,</td>\n",
       "      <td>0,4054,8297,8506,9181,10444,15633,21459,25072,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr12</td>\n",
       "      <td>121163540</td>\n",
       "      <td>121163541</td>\n",
       "      <td>NM_000017.3</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "      <td>121163688</td>\n",
       "      <td>121177251</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>194,164,150,112,152,171,138,96,57,713,</td>\n",
       "      <td>0,1288,11248,11618,12099,12542,12795,13082,134...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr17</td>\n",
       "      <td>7123149</td>\n",
       "      <td>7123150</td>\n",
       "      <td>NM_000018.3</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "      <td>7123303</td>\n",
       "      <td>7128416</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>216,76,66,73,65,135,145,130,126,199,105,87,63,...</td>\n",
       "      <td>0,291,633,773,935,1093,1707,2121,2346,2836,330...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "       0        TSS       TSS1            3  4  5          6          7  8  \\\n",
       "0  chr12    9268824    9268825  NM_000014.5  0  -    9220418    9268445  0   \n",
       "1   chr8   18248754   18248755  NM_000015.2  0  +   18257513   18258386  0   \n",
       "2   chr1   76190031   76190032  NM_000016.5  0  +   76190472   76228448  0   \n",
       "3  chr12  121163540  121163541  NM_000017.3  0  +  121163688  121177251  0   \n",
       "4  chr17    7123149    7123150  NM_000018.3  0  +    7123303    7128416  0   \n",
       "\n",
       "    9                                                 10  \\\n",
       "0  36  132,42,103,69,91,128,219,224,181,75,157,88,177...   \n",
       "1   2                                          101,1216,   \n",
       "2  12        471,88,98,70,101,81,131,109,141,96,249,988,   \n",
       "3  10             194,164,150,112,152,171,138,96,57,713,   \n",
       "4  20  216,76,66,73,65,135,145,130,126,199,105,87,63,...   \n",
       "\n",
       "                                                  11  \n",
       "0  0,475,1032,2037,2780,4651,4945,6852,9048,9638,...  \n",
       "1                                            0,8753,  \n",
       "2  0,4054,8297,8506,9181,10444,15633,21459,25072,...  \n",
       "3  0,1288,11248,11618,12099,12542,12795,13082,134...  \n",
       "4  0,291,633,773,935,1093,1707,2121,2346,2836,330...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## \n",
    "def picktss(x):\n",
    "    if re.match('\\+', x[5]):\n",
    "        return x[1]\n",
    "    elif re.match('\\-', x[5]):\n",
    "        return x[2]-1\n",
    "    else:\n",
    "        print('Problematic TSS')\n",
    "        return None\n",
    "dfHT['TSS'] = dfHT.apply(lambda x: picktss(x), axis=1)\n",
    "dfHT['TSS1'] = dfHT['TSS']+1\n",
    "dfTSS = dfHT[[0, 'TSS', 'TSS1'] + list(range(3,12))]\n",
    "dfTSS.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/james/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "window = 2000\n",
    "dfTSS.loc[:, 'TSS'] = dfTSS['TSS']-2000\n",
    "dfTSS.loc[:, 'TSS1'] = dfTSS['TSS1']+500-1\n",
    "dfTSS = dfTSS.sort_values(by=[0, 'TSS'], ascending=True).reset_index(drop=True)\n",
    "\n",
    "with gzip.open('../raw_data/transcriptome/hg19_allTSS_2000up_500down.bed.gz', 'tw') as handle:\n",
    "    dfTSS.to_csv(handle, header=False, index=False, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr1</td>\n",
       "      <td>11673</td>\n",
       "      <td>12073</td>\n",
       "      <td>NR_046018.2</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "      <td>14409</td>\n",
       "      <td>14409</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>354,109,1189,</td>\n",
       "      <td>0,739,1347,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr1</td>\n",
       "      <td>17235</td>\n",
       "      <td>17635</td>\n",
       "      <td>NR_106918.1</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>17436</td>\n",
       "      <td>17436</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>68,</td>\n",
       "      <td>0,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>29169</td>\n",
       "      <td>29569</td>\n",
       "      <td>NR_024540.1</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>29370</td>\n",
       "      <td>29370</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>468,69,152,159,198,136,137,147,99,154,50,</td>\n",
       "      <td>0,608,1434,2245,2496,2871,3244,3553,3906,10376...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr1</td>\n",
       "      <td>30165</td>\n",
       "      <td>30565</td>\n",
       "      <td>NR_036051.1</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "      <td>30503</td>\n",
       "      <td>30503</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>138,</td>\n",
       "      <td>0,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr1</td>\n",
       "      <td>35880</td>\n",
       "      <td>36280</td>\n",
       "      <td>NR_026818.1</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>36081</td>\n",
       "      <td>36081</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>564,205,361,</td>\n",
       "      <td>0,666,1110,</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0      1      2            3   4  5      6      7   8   9   \\\n",
       "0  chr1  11673  12073  NR_046018.2   0  +  14409  14409   0   3   \n",
       "1  chr1  17235  17635  NR_106918.1   0  -  17436  17436   0   1   \n",
       "2  chr1  29169  29569  NR_024540.1   0  -  29370  29370   0  11   \n",
       "3  chr1  30165  30565  NR_036051.1   0  +  30503  30503   0   1   \n",
       "4  chr1  35880  36280  NR_026818.1   0  -  36081  36081   0   3   \n",
       "\n",
       "                                          10  \\\n",
       "0                              354,109,1189,   \n",
       "1                                        68,   \n",
       "2  468,69,152,159,198,136,137,147,99,154,50,   \n",
       "3                                       138,   \n",
       "4                               564,205,361,   \n",
       "\n",
       "                                                  11  \n",
       "0                                        0,739,1347,  \n",
       "1                                                 0,  \n",
       "2  0,608,1434,2245,2496,2871,3244,3553,3906,10376...  \n",
       "3                                                 0,  \n",
       "4                                        0,666,1110,  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with gzip.open('../raw_data/transcriptome/hg19_allTSS.bed.gz', 'tr') as handle:\n",
    "    dfTSS = pd.read_csv(handle, header=-1)\n",
    "dfTSS.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>refseq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAAS</td>\n",
       "      <td>NM_015665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAGAB</td>\n",
       "      <td>NM_024666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAMP</td>\n",
       "      <td>NM_001087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAR2</td>\n",
       "      <td>NM_015511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AARS</td>\n",
       "      <td>NM_001605</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symbol     refseq\n",
       "0   AAAS   NM_015665\n",
       "1  AAGAB   NM_024666\n",
       "2   AAMP   NM_001087\n",
       "3   AAR2   NM_015511\n",
       "4   AARS   NM_001605"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housekeep = pd.read_csv('../raw_data/transcriptome/HK_genes.txt', sep='\\t', header=-1)\n",
    "housekeep.columns = ['symbol', 'refseq']\n",
    "housekeep.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfU2Dseq = pd.read_csv('../raw_data/transcriptome/U2OS_TSS_DNaseSeq.csv', index_col=0)\n",
    "dfHEKDseq = pd.read_csv('../raw_data/transcriptome/HEK_TSS_2000up_500down_DNaseSeq.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfExpHEK = pd.read_csv('../raw_data/transcriptome/HEK/paired/SRR3997505/abundance.tsv', sep='\\t')\n",
    "dfExpU2  = pd.read_csv('../raw_data/transcriptome/U2OS/ERR191523_trimmed/ERR191523/abundance.tsv', sep='\\t')\n",
    "dfExpU2['ref'] = dfExpU2['target_id'].str.strip('|').str.strip('ref|')\n",
    "dfExpHEK['ref'] = dfExpHEK['target_id'].str.strip('|').str.strip('ref|')\n",
    "\n",
    "# Take out zeros\n",
    "dfExpU2 = dfExpU2[dfExpU2['tpm']>5]\n",
    "dfExpHEK = dfExpHEK[dfExpHEK['tpm']>5]\n",
    "\n",
    "# Pick mRNA NM_\n",
    "dfExpU2 = dfExpU2[dfExpU2['ref'].str.contains('NM_')]\n",
    "dfExpHEK = dfExpHEK[dfExpHEK['ref'].str.contains('NM_')]\n",
    "\n",
    "# Pick housekeeping genes\n",
    "dfHEKhouse = dfExpHEK[dfExpHEK['ref'].apply(lambda x: x.split('.')[0]).isin(housekeep['refseq'])]\n",
    "dfU2house = dfExpU2[dfExpU2['ref'].apply(lambda x: x.split('.')[0]).isin(housekeep['refseq'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>eff_length_x</th>\n",
       "      <th>est_counts_x</th>\n",
       "      <th>tpm_x</th>\n",
       "      <th>ref</th>\n",
       "      <th>target_id_y</th>\n",
       "      <th>length_y</th>\n",
       "      <th>eff_length_y</th>\n",
       "      <th>est_counts_y</th>\n",
       "      <th>tpm_y</th>\n",
       "      <th>logTPMHEK</th>\n",
       "      <th>logTPMU2</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2817</td>\n",
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       "      <td>102.7530</td>\n",
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       "      <td>7988.99</td>\n",
       "      <td>54.98710</td>\n",
       "      <td>2.011795</td>\n",
       "      <td>1.740261</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ref|NM_005101.3|</td>\n",
       "      <td>685</td>\n",
       "      <td>516.45</td>\n",
       "      <td>358.00</td>\n",
       "      <td>16.3046</td>\n",
       "      <td>NM_005101.3</td>\n",
       "      <td>ref|NM_005101.3|</td>\n",
       "      <td>685</td>\n",
       "      <td>585.0</td>\n",
       "      <td>324.00</td>\n",
       "      <td>10.35730</td>\n",
       "      <td>1.212310</td>\n",
       "      <td>1.015247</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ref|NM_198576.3|</td>\n",
       "      <td>7343</td>\n",
       "      <td>7174.45</td>\n",
       "      <td>6310.60</td>\n",
       "      <td>20.6889</td>\n",
       "      <td>NM_198576.3</td>\n",
       "      <td>ref|NM_198576.3|</td>\n",
       "      <td>7343</td>\n",
       "      <td>7243.0</td>\n",
       "      <td>19153.00</td>\n",
       "      <td>49.45120</td>\n",
       "      <td>1.315737</td>\n",
       "      <td>1.694177</td>\n",
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       "      <td>4429.73</td>\n",
       "      <td>55.3490</td>\n",
       "      <td>NM_016176.3</td>\n",
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       "      <td>2051</td>\n",
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       "      <td>5424.55</td>\n",
       "      <td>51.99540</td>\n",
       "      <td>1.743110</td>\n",
       "      <td>1.715965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ref|NM_080605.3|</td>\n",
       "      <td>2793</td>\n",
       "      <td>2624.45</td>\n",
       "      <td>2520.00</td>\n",
       "      <td>22.5849</td>\n",
       "      <td>NM_080605.3</td>\n",
       "      <td>ref|NM_080605.3|</td>\n",
       "      <td>2793</td>\n",
       "      <td>2693.0</td>\n",
       "      <td>756.00</td>\n",
       "      <td>5.24981</td>\n",
       "      <td>1.353818</td>\n",
       "      <td>0.720144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        target_id_x  length_x  eff_length_x  est_counts_x     tpm_x  \\\n",
       "0  ref|NM_015658.3|      2817       2648.45      11569.90  102.7530   \n",
       "1  ref|NM_005101.3|       685        516.45        358.00   16.3046   \n",
       "2  ref|NM_198576.3|      7343       7174.45       6310.60   20.6889   \n",
       "3  ref|NM_016176.3|      2051       1882.45       4429.73   55.3490   \n",
       "4  ref|NM_080605.3|      2793       2624.45       2520.00   22.5849   \n",
       "\n",
       "           ref       target_id_y  length_y  eff_length_y  est_counts_y  \\\n",
       "0  NM_015658.3  ref|NM_015658.3|      2817        2717.0       7988.99   \n",
       "1  NM_005101.3  ref|NM_005101.3|       685         585.0        324.00   \n",
       "2  NM_198576.3  ref|NM_198576.3|      7343        7243.0      19153.00   \n",
       "3  NM_016176.3  ref|NM_016176.3|      2051        1951.0       5424.55   \n",
       "4  NM_080605.3  ref|NM_080605.3|      2793        2693.0        756.00   \n",
       "\n",
       "      tpm_y  logTPMHEK  logTPMU2  \n",
       "0  54.98710   2.011795  1.740261  \n",
       "1  10.35730   1.212310  1.015247  \n",
       "2  49.45120   1.315737  1.694177  \n",
       "3  51.99540   1.743110  1.715965  \n",
       "4   5.24981   1.353818  0.720144  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfExpMerge = pd.merge(left=dfExpHEK, right=dfExpU2, on='ref', how='inner')\n",
    "dfExpMerge['logTPMHEK'] = dfExpMerge['tpm_x'].map(np.log10)\n",
    "dfExpMerge['logTPMU2'] = dfExpMerge['tpm_y'].map(np.log10)\n",
    "dfExpMerge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>ref</th>\n",
       "      <th>target_id_y</th>\n",
       "      <th>length_y</th>\n",
       "      <th>eff_length_y</th>\n",
       "      <th>est_counts_y</th>\n",
       "      <th>tpm_y</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ref|NM_015658.3|</td>\n",
       "      <td>2817</td>\n",
       "      <td>2648.45</td>\n",
       "      <td>11569.90</td>\n",
       "      <td>102.7530</td>\n",
       "      <td>NM_015658.3</td>\n",
       "      <td>ref|NM_015658.3|</td>\n",
       "      <td>2817</td>\n",
       "      <td>2717.0</td>\n",
       "      <td>7988.99</td>\n",
       "      <td>54.98710</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ref|NM_016176.3|</td>\n",
       "      <td>2051</td>\n",
       "      <td>1882.45</td>\n",
       "      <td>4429.73</td>\n",
       "      <td>55.3490</td>\n",
       "      <td>NM_016176.3</td>\n",
       "      <td>ref|NM_016176.3|</td>\n",
       "      <td>2051</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>5424.55</td>\n",
       "      <td>51.99540</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ref|NM_080605.3|</td>\n",
       "      <td>2793</td>\n",
       "      <td>2624.45</td>\n",
       "      <td>2520.00</td>\n",
       "      <td>22.5849</td>\n",
       "      <td>NM_080605.3</td>\n",
       "      <td>ref|NM_080605.3|</td>\n",
       "      <td>2793</td>\n",
       "      <td>2693.0</td>\n",
       "      <td>756.00</td>\n",
       "      <td>5.24981</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ref|NM_058167.2|</td>\n",
       "      <td>2267</td>\n",
       "      <td>2098.45</td>\n",
       "      <td>1734.89</td>\n",
       "      <td>19.4459</td>\n",
       "      <td>NM_058167.2</td>\n",
       "      <td>ref|NM_058167.2|</td>\n",
       "      <td>2267</td>\n",
       "      <td>2167.0</td>\n",
       "      <td>1741.94</td>\n",
       "      <td>15.03250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ref|NM_017871.5|</td>\n",
       "      <td>2168</td>\n",
       "      <td>1999.45</td>\n",
       "      <td>2474.08</td>\n",
       "      <td>29.1043</td>\n",
       "      <td>NM_017871.5</td>\n",
       "      <td>ref|NM_017871.5|</td>\n",
       "      <td>2168</td>\n",
       "      <td>2068.0</td>\n",
       "      <td>2717.43</td>\n",
       "      <td>24.57350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        target_id_x  length_x  eff_length_x  est_counts_x     tpm_x  \\\n",
       "0  ref|NM_015658.3|      2817       2648.45      11569.90  102.7530   \n",
       "1  ref|NM_016176.3|      2051       1882.45       4429.73   55.3490   \n",
       "2  ref|NM_080605.3|      2793       2624.45       2520.00   22.5849   \n",
       "3  ref|NM_058167.2|      2267       2098.45       1734.89   19.4459   \n",
       "4  ref|NM_017871.5|      2168       1999.45       2474.08   29.1043   \n",
       "\n",
       "           ref       target_id_y  length_y  eff_length_y  est_counts_y  \\\n",
       "0  NM_015658.3  ref|NM_015658.3|      2817        2717.0       7988.99   \n",
       "1  NM_016176.3  ref|NM_016176.3|      2051        1951.0       5424.55   \n",
       "2  NM_080605.3  ref|NM_080605.3|      2793        2693.0        756.00   \n",
       "3  NM_058167.2  ref|NM_058167.2|      2267        2167.0       1741.94   \n",
       "4  NM_017871.5  ref|NM_017871.5|      2168        2068.0       2717.43   \n",
       "\n",
       "      tpm_y  \n",
       "0  54.98710  \n",
       "1  51.99540  \n",
       "2   5.24981  \n",
       "3  15.03250  \n",
       "4  24.57350  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfHouseMerge = pd.merge(left=dfHEKhouse, right=dfU2house, on='ref', how='inner')\n",
    "dfHouseMerge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f895dee1710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(7,7))\n",
    "#ax.plot(dfHouseMerge['tpm_x'].map(np.log10), dfHouseMerge['tpm_y'].map(np.log10), 'o')\n",
    "\n",
    "sbn.regplot(x='logTPMHEK', y='logTPMU2', data=dfExpMerge, scatter_kws={'alpha':0.3, 's': 13})\n",
    "sbn.despine()\n",
    "ax.set_xlabel('HEK293T')\n",
    "ax.set_ylabel('U2OS')\n",
    "ax.set_ylim(ax.get_xlim())\n",
    "ax.set_title('Gene expression level (tpm)')\n",
    "fig.tight_layout()\n",
    "fig.savefig('../paper_figures/20181216_GeneExp_HEK_U2_RegPlot.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>length_x</th>\n",
       "      <th>eff_length_x</th>\n",
       "      <th>est_counts_x</th>\n",
       "      <th>tpm_x</th>\n",
       "      <th>length_y</th>\n",
       "      <th>eff_length_y</th>\n",
       "      <th>est_counts_y</th>\n",
       "      <th>tpm_y</th>\n",
       "      <th>logTPMHEK</th>\n",
       "      <th>logTPMU2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>length_x</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.048839</td>\n",
       "      <td>-0.163188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.053910</td>\n",
       "      <td>-0.155035</td>\n",
       "      <td>-0.400752</td>\n",
       "      <td>-0.221823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_length_x</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.048835</td>\n",
       "      <td>-0.163173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.053911</td>\n",
       "      <td>-0.155029</td>\n",
       "      <td>-0.400743</td>\n",
       "      <td>-0.221818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>est_counts_x</th>\n",
       "      <td>-0.048839</td>\n",
       "      <td>-0.048835</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.726131</td>\n",
       "      <td>-0.048839</td>\n",
       "      <td>-0.048839</td>\n",
       "      <td>0.715772</td>\n",
       "      <td>0.729895</td>\n",
       "      <td>0.567881</td>\n",
       "      <td>0.450621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tpm_x</th>\n",
       "      <td>-0.163188</td>\n",
       "      <td>-0.163173</td>\n",
       "      <td>0.726131</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.163188</td>\n",
       "      <td>-0.163188</td>\n",
       "      <td>0.302255</td>\n",
       "      <td>0.642053</td>\n",
       "      <td>0.561672</td>\n",
       "      <td>0.410186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length_y</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.048839</td>\n",
       "      <td>-0.163188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.053910</td>\n",
       "      <td>-0.155035</td>\n",
       "      <td>-0.400752</td>\n",
       "      <td>-0.221823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_length_y</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.048839</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>-0.400752</td>\n",
       "      <td>-0.221823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>est_counts_y</th>\n",
       "      <td>0.053910</td>\n",
       "      <td>0.053911</td>\n",
       "      <td>0.715772</td>\n",
       "      <td>0.302255</td>\n",
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       "      <td>0.053910</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.779361</td>\n",
       "      <td>0.332011</td>\n",
       "      <td>0.508064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tpm_y</th>\n",
       "      <td>-0.155035</td>\n",
       "      <td>-0.155029</td>\n",
       "      <td>0.729895</td>\n",
       "      <td>0.642053</td>\n",
       "      <td>-0.155035</td>\n",
       "      <td>-0.155035</td>\n",
       "      <td>0.779361</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.537246</td>\n",
       "      <td>0.622624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logTPMHEK</th>\n",
       "      <td>-0.400752</td>\n",
       "      <td>-0.400743</td>\n",
       "      <td>0.567881</td>\n",
       "      <td>0.561672</td>\n",
       "      <td>-0.400752</td>\n",
       "      <td>-0.400752</td>\n",
       "      <td>0.332011</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.673095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logTPMU2</th>\n",
       "      <td>-0.221823</td>\n",
       "      <td>-0.221818</td>\n",
       "      <td>0.450621</td>\n",
       "      <td>0.410186</td>\n",
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       "      <td>-0.221823</td>\n",
       "      <td>0.508064</td>\n",
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       "      <td>0.673095</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "              length_x  eff_length_x  est_counts_x     tpm_x  length_y  \\\n",
       "length_x      1.000000      1.000000     -0.048839 -0.163188  1.000000   \n",
       "eff_length_x  1.000000      1.000000     -0.048835 -0.163173  1.000000   \n",
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       "tpm_x        -0.163188     -0.163173      0.726131  1.000000 -0.163188   \n",
       "length_y      1.000000      1.000000     -0.048839 -0.163188  1.000000   \n",
       "eff_length_y  1.000000      1.000000     -0.048839 -0.163188  1.000000   \n",
       "est_counts_y  0.053910      0.053911      0.715772  0.302255  0.053910   \n",
       "tpm_y        -0.155035     -0.155029      0.729895  0.642053 -0.155035   \n",
       "logTPMHEK    -0.400752     -0.400743      0.567881  0.561672 -0.400752   \n",
       "logTPMU2     -0.221823     -0.221818      0.450621  0.410186 -0.221823   \n",
       "\n",
       "              eff_length_y  est_counts_y     tpm_y  logTPMHEK  logTPMU2  \n",
       "length_x          1.000000      0.053910 -0.155035  -0.400752 -0.221823  \n",
       "eff_length_x      1.000000      0.053911 -0.155029  -0.400743 -0.221818  \n",
       "est_counts_x     -0.048839      0.715772  0.729895   0.567881  0.450621  \n",
       "tpm_x            -0.163188      0.302255  0.642053   0.561672  0.410186  \n",
       "length_y          1.000000      0.053910 -0.155035  -0.400752 -0.221823  \n",
       "eff_length_y      1.000000      0.053910 -0.155035  -0.400752 -0.221823  \n",
       "est_counts_y      0.053910      1.000000  0.779361   0.332011  0.508064  \n",
       "tpm_y            -0.155035      0.779361  1.000000   0.537246  0.622624  \n",
       "logTPMHEK        -0.400752      0.332011  0.537246   1.000000  0.673095  \n",
       "logTPMU2         -0.221823      0.508064  0.622624   0.673095  1.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfExpMerge.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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       "              length_x  eff_length_x  est_counts_x     tpm_x  length_y  \\\n",
       "length_x      1.000000      1.000000     -0.048839 -0.163188  1.000000   \n",
       "eff_length_x  1.000000      1.000000     -0.048835 -0.163173  1.000000   \n",
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       "eff_length_y  1.000000      1.000000     -0.048839 -0.163188  1.000000   \n",
       "est_counts_y  0.053910      0.053911      0.715772  0.302255  0.053910   \n",
       "tpm_y        -0.155035     -0.155029      0.729895  0.642053 -0.155035   \n",
       "\n",
       "              eff_length_y  est_counts_y     tpm_y  \n",
       "length_x          1.000000      0.053910 -0.155035  \n",
       "eff_length_x      1.000000      0.053911 -0.155029  \n",
       "est_counts_x     -0.048839      0.715772  0.729895  \n",
       "tpm_x            -0.163188      0.302255  0.642053  \n",
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       "eff_length_y      1.000000      0.053910 -0.155035  \n",
       "est_counts_y      0.053910      1.000000  0.779361  \n",
       "tpm_y            -0.155035      0.779361  1.000000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfExpMerge.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>bStart</th>\n",
       "      <th>bEnd</th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "      <th>strand</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
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       "      <th>normed_sumCov</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr1</td>\n",
       "      <td>9873</td>\n",
       "      <td>12373</td>\n",
       "      <td>NR_046018.2</td>\n",
       "      <td>0</td>\n",
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       "      <td>14409</td>\n",
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       "      <td>0.006805</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr1</td>\n",
       "      <td>15435</td>\n",
       "      <td>17935</td>\n",
       "      <td>NR_106918.1</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>17436</td>\n",
       "      <td>17436</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>68,</td>\n",
       "      <td>0,</td>\n",
       "      <td>700</td>\n",
       "      <td>0.001280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>27369</td>\n",
       "      <td>29869</td>\n",
       "      <td>NR_024540.1</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>29370</td>\n",
       "      <td>29370</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>468,69,152,159,198,136,137,147,99,154,50,</td>\n",
       "      <td>0,608,1434,2245,2496,2871,3244,3553,3906,10376...</td>\n",
       "      <td>1562</td>\n",
       "      <td>0.002857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr1</td>\n",
       "      <td>28365</td>\n",
       "      <td>30865</td>\n",
       "      <td>NR_036051.1</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "      <td>30503</td>\n",
       "      <td>30503</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>138,</td>\n",
       "      <td>0,</td>\n",
       "      <td>9507</td>\n",
       "      <td>0.017391</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr1</td>\n",
       "      <td>34080</td>\n",
       "      <td>36580</td>\n",
       "      <td>NR_026818.1</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>36081</td>\n",
       "      <td>36081</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>564,205,361,</td>\n",
       "      <td>0,666,1110,</td>\n",
       "      <td>2242</td>\n",
       "      <td>0.004101</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    chr  bStart   bEnd         name  score strand      6      7  8   9  \\\n",
       "0  chr1    9873  12373  NR_046018.2      0      +  14409  14409  0   3   \n",
       "1  chr1   15435  17935  NR_106918.1      0      -  17436  17436  0   1   \n",
       "2  chr1   27369  29869  NR_024540.1      0      -  29370  29370  0  11   \n",
       "3  chr1   28365  30865  NR_036051.1      0      +  30503  30503  0   1   \n",
       "4  chr1   34080  36580  NR_026818.1      0      -  36081  36081  0   3   \n",
       "\n",
       "                                          10  \\\n",
       "0                              354,109,1189,   \n",
       "1                                        68,   \n",
       "2  468,69,152,159,198,136,137,147,99,154,50,   \n",
       "3                                       138,   \n",
       "4                               564,205,361,   \n",
       "\n",
       "                                                  11  sumCov  normed_sumCov  \n",
       "0                                        0,739,1347,    3720       0.006805  \n",
       "1                                                 0,     700       0.001280  \n",
       "2  0,608,1434,2245,2496,2871,3244,3553,3906,10376...    1562       0.002857  \n",
       "3                                                 0,    9507       0.017391  \n",
       "4                                        0,666,1110,    2242       0.004101  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfHEKDseq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5331, 11)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfExpMerge.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['chr', 'bStart', 'bEnd', 'name', 'sumCov', 'normed_sumCov']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = list(dfHEKDseq.columns)\n",
    "\n",
    "cols = cols[:4] + cols[-2:]\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bStart</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.128826</td>\n",
       "      <td>-0.128826</td>\n",
       "      <td>0.055221</td>\n",
       "      <td>0.055222</td>\n",
       "      <td>-0.012867</td>\n",
       "      <td>-0.018082</td>\n",
       "      <td>0.055221</td>\n",
       "      <td>0.055221</td>\n",
       "      <td>0.008307</td>\n",
       "      <td>-0.013575</td>\n",
       "      <td>-0.101864</td>\n",
       "      <td>-0.034935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bEnd</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.128826</td>\n",
       "      <td>-0.128826</td>\n",
       "      <td>0.055221</td>\n",
       "      <td>0.055222</td>\n",
       "      <td>-0.012867</td>\n",
       "      <td>-0.018082</td>\n",
       "      <td>0.055221</td>\n",
       "      <td>0.055221</td>\n",
       "      <td>0.008307</td>\n",
       "      <td>-0.013575</td>\n",
       "      <td>-0.101864</td>\n",
       "      <td>-0.034935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sumCov</th>\n",
       "      <td>-0.128826</td>\n",
       "      <td>-0.128826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020236</td>\n",
       "      <td>0.020235</td>\n",
       "      <td>-0.011136</td>\n",
       "      <td>-0.028992</td>\n",
       "      <td>0.020236</td>\n",
       "      <td>0.020236</td>\n",
       "      <td>-0.015113</td>\n",
       "      <td>-0.035348</td>\n",
       "      <td>0.887678</td>\n",
       "      <td>0.005357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normed_sumCov</th>\n",
       "      <td>-0.128826</td>\n",
       "      <td>-0.128826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020236</td>\n",
       "      <td>0.020235</td>\n",
       "      <td>-0.011136</td>\n",
       "      <td>-0.028992</td>\n",
       "      <td>0.020236</td>\n",
       "      <td>0.020236</td>\n",
       "      <td>-0.015113</td>\n",
       "      <td>-0.035348</td>\n",
       "      <td>0.887678</td>\n",
       "      <td>0.005357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length_x</th>\n",
       "      <td>0.055221</td>\n",
       "      <td>0.055221</td>\n",
       "      <td>0.020236</td>\n",
       "      <td>0.020236</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042385</td>\n",
       "      <td>-0.163833</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062468</td>\n",
       "      <td>-0.159041</td>\n",
       "      <td>0.009917</td>\n",
       "      <td>-0.400155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_length_x</th>\n",
       "      <td>0.055222</td>\n",
       "      <td>0.055222</td>\n",
       "      <td>0.020235</td>\n",
       "      <td>0.020235</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042382</td>\n",
       "      <td>-0.163820</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062468</td>\n",
       "      <td>-0.159037</td>\n",
       "      <td>0.009915</td>\n",
       "      <td>-0.400147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>est_counts_x</th>\n",
       "      <td>-0.012867</td>\n",
       "      <td>-0.012867</td>\n",
       "      <td>-0.011136</td>\n",
       "      <td>-0.011136</td>\n",
       "      <td>-0.042385</td>\n",
       "      <td>-0.042382</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.720931</td>\n",
       "      <td>-0.042385</td>\n",
       "      <td>-0.042385</td>\n",
       "      <td>0.751611</td>\n",
       "      <td>0.750752</td>\n",
       "      <td>-0.000963</td>\n",
       "      <td>0.554972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tpm_x</th>\n",
       "      <td>-0.018082</td>\n",
       "      <td>-0.018082</td>\n",
       "      <td>-0.028992</td>\n",
       "      <td>-0.028992</td>\n",
       "      <td>-0.163833</td>\n",
       "      <td>-0.163820</td>\n",
       "      <td>0.720931</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.163833</td>\n",
       "      <td>-0.163833</td>\n",
       "      <td>0.322287</td>\n",
       "      <td>0.678015</td>\n",
       "      <td>-0.019282</td>\n",
       "      <td>0.559861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length_y</th>\n",
       "      <td>0.055221</td>\n",
       "      <td>0.055221</td>\n",
       "      <td>0.020236</td>\n",
       "      <td>0.020236</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042385</td>\n",
       "      <td>-0.163833</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062468</td>\n",
       "      <td>-0.159041</td>\n",
       "      <td>0.009917</td>\n",
       "      <td>-0.400155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_length_y</th>\n",
       "      <td>0.055221</td>\n",
       "      <td>0.055221</td>\n",
       "      <td>0.020236</td>\n",
       "      <td>0.020236</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042385</td>\n",
       "      <td>-0.163833</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062468</td>\n",
       "      <td>-0.159041</td>\n",
       "      <td>0.009917</td>\n",
       "      <td>-0.400155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>est_counts_y</th>\n",
       "      <td>0.008307</td>\n",
       "      <td>0.008307</td>\n",
       "      <td>-0.015113</td>\n",
       "      <td>-0.015113</td>\n",
       "      <td>0.062468</td>\n",
       "      <td>0.062468</td>\n",
       "      <td>0.751611</td>\n",
       "      <td>0.322287</td>\n",
       "      <td>0.062468</td>\n",
       "      <td>0.062468</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.752960</td>\n",
       "      <td>-0.008759</td>\n",
       "      <td>0.333283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tpm_y</th>\n",
       "      <td>-0.013575</td>\n",
       "      <td>-0.013575</td>\n",
       "      <td>-0.035348</td>\n",
       "      <td>-0.035348</td>\n",
       "      <td>-0.159041</td>\n",
       "      <td>-0.159037</td>\n",
       "      <td>0.750752</td>\n",
       "      <td>0.678015</td>\n",
       "      <td>-0.159041</td>\n",
       "      <td>-0.159041</td>\n",
       "      <td>0.752960</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.026598</td>\n",
       "      <td>0.548186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log_normSumCov</th>\n",
       "      <td>-0.101864</td>\n",
       "      <td>-0.101864</td>\n",
       "      <td>0.887678</td>\n",
       "      <td>0.887678</td>\n",
       "      <td>0.009917</td>\n",
       "      <td>0.009915</td>\n",
       "      <td>-0.000963</td>\n",
       "      <td>-0.019282</td>\n",
       "      <td>0.009917</td>\n",
       "      <td>0.009917</td>\n",
       "      <td>-0.008759</td>\n",
       "      <td>-0.026598</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log_HEKtpm</th>\n",
       "      <td>-0.034935</td>\n",
       "      <td>-0.034935</td>\n",
       "      <td>0.005357</td>\n",
       "      <td>0.005357</td>\n",
       "      <td>-0.400155</td>\n",
       "      <td>-0.400147</td>\n",
       "      <td>0.554972</td>\n",
       "      <td>0.559861</td>\n",
       "      <td>-0.400155</td>\n",
       "      <td>-0.400155</td>\n",
       "      <td>0.333283</td>\n",
       "      <td>0.548186</td>\n",
       "      <td>0.029343</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  bStart      bEnd    sumCov  normed_sumCov  length_x  \\\n",
       "bStart          1.000000  1.000000 -0.128826      -0.128826  0.055221   \n",
       "bEnd            1.000000  1.000000 -0.128826      -0.128826  0.055221   \n",
       "sumCov         -0.128826 -0.128826  1.000000       1.000000  0.020236   \n",
       "normed_sumCov  -0.128826 -0.128826  1.000000       1.000000  0.020236   \n",
       "length_x        0.055221  0.055221  0.020236       0.020236  1.000000   \n",
       "eff_length_x    0.055222  0.055222  0.020235       0.020235  1.000000   \n",
       "est_counts_x   -0.012867 -0.012867 -0.011136      -0.011136 -0.042385   \n",
       "tpm_x          -0.018082 -0.018082 -0.028992      -0.028992 -0.163833   \n",
       "length_y        0.055221  0.055221  0.020236       0.020236  1.000000   \n",
       "eff_length_y    0.055221  0.055221  0.020236       0.020236  1.000000   \n",
       "est_counts_y    0.008307  0.008307 -0.015113      -0.015113  0.062468   \n",
       "tpm_y          -0.013575 -0.013575 -0.035348      -0.035348 -0.159041   \n",
       "log_normSumCov -0.101864 -0.101864  0.887678       0.887678  0.009917   \n",
       "log_HEKtpm     -0.034935 -0.034935  0.005357       0.005357 -0.400155   \n",
       "\n",
       "                eff_length_x  est_counts_x     tpm_x  length_y  eff_length_y  \\\n",
       "bStart              0.055222     -0.012867 -0.018082  0.055221      0.055221   \n",
       "bEnd                0.055222     -0.012867 -0.018082  0.055221      0.055221   \n",
       "sumCov              0.020235     -0.011136 -0.028992  0.020236      0.020236   \n",
       "normed_sumCov       0.020235     -0.011136 -0.028992  0.020236      0.020236   \n",
       "length_x            1.000000     -0.042385 -0.163833  1.000000      1.000000   \n",
       "eff_length_x        1.000000     -0.042382 -0.163820  1.000000      1.000000   \n",
       "est_counts_x       -0.042382      1.000000  0.720931 -0.042385     -0.042385   \n",
       "tpm_x              -0.163820      0.720931  1.000000 -0.163833     -0.163833   \n",
       "length_y            1.000000     -0.042385 -0.163833  1.000000      1.000000   \n",
       "eff_length_y        1.000000     -0.042385 -0.163833  1.000000      1.000000   \n",
       "est_counts_y        0.062468      0.751611  0.322287  0.062468      0.062468   \n",
       "tpm_y              -0.159037      0.750752  0.678015 -0.159041     -0.159041   \n",
       "log_normSumCov      0.009915     -0.000963 -0.019282  0.009917      0.009917   \n",
       "log_HEKtpm         -0.400147      0.554972  0.559861 -0.400155     -0.400155   \n",
       "\n",
       "                est_counts_y     tpm_y  log_normSumCov  log_HEKtpm  \n",
       "bStart              0.008307 -0.013575       -0.101864   -0.034935  \n",
       "bEnd                0.008307 -0.013575       -0.101864   -0.034935  \n",
       "sumCov             -0.015113 -0.035348        0.887678    0.005357  \n",
       "normed_sumCov      -0.015113 -0.035348        0.887678    0.005357  \n",
       "length_x            0.062468 -0.159041        0.009917   -0.400155  \n",
       "eff_length_x        0.062468 -0.159037        0.009915   -0.400147  \n",
       "est_counts_x        0.751611  0.750752       -0.000963    0.554972  \n",
       "tpm_x               0.322287  0.678015       -0.019282    0.559861  \n",
       "length_y            0.062468 -0.159041        0.009917   -0.400155  \n",
       "eff_length_y        0.062468 -0.159041        0.009917   -0.400155  \n",
       "est_counts_y        1.000000  0.752960       -0.008759    0.333283  \n",
       "tpm_y               0.752960  1.000000       -0.026598    0.548186  \n",
       "log_normSumCov     -0.008759 -0.026598        1.000000    0.029343  \n",
       "log_HEKtpm          0.333283  0.548186        0.029343    1.000000  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfHEKDseq_tight = dfHEKDseq[cols]\n",
    "dfHEKfinal = pd.merge(left=dfHEKDseq_tight, right=dfExpMerge, left_on='name', right_on='ref', how='inner')\n",
    "dfHEKfinal = dfHEKfinal[dfHEKfinal['normed_sumCov']>0]\n",
    "dfHEKfinal['log_normSumCov'] = dfHEKfinal['normed_sumCov'].map(np.log10)\n",
    "dfHEKfinal['log_HEKtpm'] = dfHEKfinal['tpm_x'].map(np.log10)\n",
    "dfHEKfinal.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5189, 19)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfHEKfinal.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f895bfaaf28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,1, figsize=(8,7))\n",
    "sbn.regplot(x = 'log_normSumCov', y='log_HEKtpm', data=dfHEKfinal, ax = axes[0])\n",
    "sbn.kdeplot(data=dfHEKfinal['log_normSumCov'], data2=dfHEKfinal['log_HEKtpm'],shade=True, ax= axes[1])\n",
    "ax.set_xlim(-4,0)\n",
    "sbn.despine()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f894daa5668>]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f895ac037f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig ,ax = plt.subplots(1,1, figsize=(14,4))\n",
    "HEKkde = stats.gaussian_kde(dfHEKfinal['log_normSumCov'])\n",
    "x = np.linspace(-4.5, 0, 300)\n",
    "HEK_TSS_pdf = HEKkde.pdf(x)\n",
    "ax.plot(x, np.cumsum(HEK_TSS_pdf)/np.sum(HEK_TSS_pdf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/james/anaconda3/lib/python3.6/site-packages/pandas/core/base.py:1004: RuntimeWarning: divide by zero encountered in log10\n",
      "  new_values = map_f(values, mapper)\n"
     ]
    }
   ],
   "source": [
    "dfMatch = pd.merge(left=dfU2Dseq, right=dfExpU2, left_on='name', right_on='ref', how='inner')\n",
    "\n",
    "dfMatchnoZ = dfMatch[dfMatch['normed_sumCov']>0].reset_index(drop=True)\n",
    "dfMatchnoZ['log_DNase'] = dfMatchnoZ['normed_sumCov'].map(np.log10)\n",
    "dfMatchnoZ['log_Exp'] = dfMatchnoZ['tpm'].map(np.log10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3689, 22)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U2house = dfMatchnoZ[dfMatchnoZ['name'].apply(lambda x: x.split('.')[0]).isin(housekeep['refseq'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1258625817794421e-05"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfMatchnoZ['normed_sumCov'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dftop5k = dfMatchnoZ.sort_values(by='tpm', ascending=False).iloc[:5000,:].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2bc65ca1d0>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2bc6432cf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(8,6))\n",
    "ax.plot(dftop5k['normed_sumCov'].map(np.log10), dftop5k['tpm'].map(np.log10), 'ro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2bc65780b8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2bd16f9da0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbn.kdeplot(dfMatch['normed_sumCov'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.45760000e+04,   1.21800000e+04,   6.68600000e+03,\n",
       "          2.29300000e+03,   6.46000000e+02,   2.25000000e+02,\n",
       "          4.00000000e+01,   1.80000000e+01,   8.00000000e+00,\n",
       "          1.30000000e+01]),\n",
       " array([ 0.        ,  0.10194376,  0.20388751,  0.30583127,  0.40777503,\n",
       "         0.50971878,  0.61166254,  0.71360629,  0.81555005,  0.91749381,\n",
       "         1.01943756]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2bd16b1908>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(dfMatch['normed_sumCov'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfMatch['normed_sumCov_log'] = dfMatch['normed_sumCov'].map"
   ]
  },
  {
   "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>bStart</th>\n",
       "      <th>bEnd</th>\n",
       "      <th>score</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>sumCov</th>\n",
       "      <th>normed_sumCov</th>\n",
       "      <th>length</th>\n",
       "      <th>eff_length</th>\n",
       "      <th>est_counts</th>\n",
       "      <th>tpm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bStart</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.025615</td>\n",
       "      <td>-0.062962</td>\n",
       "      <td>-0.062962</td>\n",
       "      <td>0.056400</td>\n",
       "      <td>0.056404</td>\n",
       "      <td>0.005796</td>\n",
       "      <td>-0.006797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bEnd</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.025615</td>\n",
       "      <td>-0.062962</td>\n",
       "      <td>-0.062962</td>\n",
       "      <td>0.056400</td>\n",
       "      <td>0.056404</td>\n",
       "      <td>0.005796</td>\n",
       "      <td>-0.006797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.025310</td>\n",
       "      <td>-0.062962</td>\n",
       "      <td>-0.062962</td>\n",
       "      <td>0.056105</td>\n",
       "      <td>0.056109</td>\n",
       "      <td>0.005784</td>\n",
       "      <td>-0.006792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.025934</td>\n",
       "      <td>-0.062990</td>\n",
       "      <td>-0.062990</td>\n",
       "      <td>0.056679</td>\n",
       "      <td>0.056684</td>\n",
       "      <td>0.005782</td>\n",
       "      <td>-0.006809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.025615</td>\n",
       "      <td>0.025615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.025310</td>\n",
       "      <td>0.025934</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>0.555333</td>\n",
       "      <td>0.555316</td>\n",
       "      <td>0.048880</td>\n",
       "      <td>-0.011973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sumCov</th>\n",
       "      <td>-0.062962</td>\n",
       "      <td>-0.062962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.062962</td>\n",
       "      <td>-0.062990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.036645</td>\n",
       "      <td>0.036579</td>\n",
       "      <td>0.089827</td>\n",
       "      <td>0.011330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normed_sumCov</th>\n",
       "      <td>-0.062962</td>\n",
       "      <td>-0.062962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.062962</td>\n",
       "      <td>-0.062990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.095890</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.036645</td>\n",
       "      <td>0.036579</td>\n",
       "      <td>0.089827</td>\n",
       "      <td>0.011330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>0.056400</td>\n",
       "      <td>0.056400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.056105</td>\n",
       "      <td>0.056679</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.555333</td>\n",
       "      <td>0.036645</td>\n",
       "      <td>0.036645</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.046476</td>\n",
       "      <td>-0.025479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_length</th>\n",
       "      <td>0.056404</td>\n",
       "      <td>0.056404</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.056109</td>\n",
       "      <td>0.056684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.555316</td>\n",
       "      <td>0.036579</td>\n",
       "      <td>0.036579</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.046466</td>\n",
       "      <td>-0.025426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>est_counts</th>\n",
       "      <td>0.005796</td>\n",
       "      <td>0.005796</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005784</td>\n",
       "      <td>0.005782</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.048880</td>\n",
       "      <td>0.089827</td>\n",
       "      <td>0.089827</td>\n",
       "      <td>0.046476</td>\n",
       "      <td>0.046466</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tpm</th>\n",
       "      <td>-0.006797</td>\n",
       "      <td>-0.006797</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.006792</td>\n",
       "      <td>-0.006809</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.011973</td>\n",
       "      <td>0.011330</td>\n",
       "      <td>0.011330</td>\n",
       "      <td>-0.025479</td>\n",
       "      <td>-0.025426</td>\n",
       "      <td>0.197542</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 bStart      bEnd  score         6         7   8         9  \\\n",
       "bStart         1.000000  1.000000    NaN  0.999999  0.999999 NaN  0.025615   \n",
       "bEnd           1.000000  1.000000    NaN  0.999999  0.999999 NaN  0.025615   \n",
       "score               NaN       NaN    NaN       NaN       NaN NaN       NaN   \n",
       "6              0.999999  0.999999    NaN  1.000000  0.999999 NaN  0.025310   \n",
       "7              0.999999  0.999999    NaN  0.999999  1.000000 NaN  0.025934   \n",
       "8                   NaN       NaN    NaN       NaN       NaN NaN       NaN   \n",
       "9              0.025615  0.025615    NaN  0.025310  0.025934 NaN  1.000000   \n",
       "sumCov        -0.062962 -0.062962    NaN -0.062962 -0.062990 NaN  0.095890   \n",
       "normed_sumCov -0.062962 -0.062962    NaN -0.062962 -0.062990 NaN  0.095890   \n",
       "length         0.056400  0.056400    NaN  0.056105  0.056679 NaN  0.555333   \n",
       "eff_length     0.056404  0.056404    NaN  0.056109  0.056684 NaN  0.555316   \n",
       "est_counts     0.005796  0.005796    NaN  0.005784  0.005782 NaN  0.048880   \n",
       "tpm           -0.006797 -0.006797    NaN -0.006792 -0.006809 NaN -0.011973   \n",
       "\n",
       "                 sumCov  normed_sumCov    length  eff_length  est_counts  \\\n",
       "bStart        -0.062962      -0.062962  0.056400    0.056404    0.005796   \n",
       "bEnd          -0.062962      -0.062962  0.056400    0.056404    0.005796   \n",
       "score               NaN            NaN       NaN         NaN         NaN   \n",
       "6             -0.062962      -0.062962  0.056105    0.056109    0.005784   \n",
       "7             -0.062990      -0.062990  0.056679    0.056684    0.005782   \n",
       "8                   NaN            NaN       NaN         NaN         NaN   \n",
       "9              0.095890       0.095890  0.555333    0.555316    0.048880   \n",
       "sumCov         1.000000       1.000000  0.036645    0.036579    0.089827   \n",
       "normed_sumCov  1.000000       1.000000  0.036645    0.036579    0.089827   \n",
       "length         0.036645       0.036645  1.000000    1.000000    0.046476   \n",
       "eff_length     0.036579       0.036579  1.000000    1.000000    0.046466   \n",
       "est_counts     0.089827       0.089827  0.046476    0.046466    1.000000   \n",
       "tpm            0.011330       0.011330 -0.025479   -0.025426    0.197542   \n",
       "\n",
       "                    tpm  \n",
       "bStart        -0.006797  \n",
       "bEnd          -0.006797  \n",
       "score               NaN  \n",
       "6             -0.006792  \n",
       "7             -0.006809  \n",
       "8                   NaN  \n",
       "9             -0.011973  \n",
       "sumCov         0.011330  \n",
       "normed_sumCov  0.011330  \n",
       "length        -0.025479  \n",
       "eff_length    -0.025426  \n",
       "est_counts     0.197542  \n",
       "tpm            1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfMatch[dfMatch['tpm']>0.0001].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfExp = pd.read_csv('../raw_data/transcriptome/HEK/SRR3997492/abundance.tsv', sep='\\t')\n",
    "dfExp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f130f754978>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f130f7b3080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbn.kdeplot(dfExp[dfExp['tpm']>0]['tpm'].map(np.log10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(81404, 5)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfExp[dfExp['tpm']>0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f130f72e5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(15,5))\n",
    "plt.hist(dfExp[dfExp['tpm']>0]['tpm'].map(np.log10))\n",
    "ax.set_xticks(np.arange(-10,4,1))\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b94fef278>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b94ec7160>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax\n",
    "dfExp[(dfExp['tpm']>0) & (dfExp['tpm']>0.0001)]['tpm'].map(np.log10).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}