Ext_Fig4_agg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c301b927",
   "metadata": {},
   "outputs": [],
   "source": [
    "import bbi\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from matplotlib.colors import LogNorm, Normalize\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "deee7143",
   "metadata": {},
   "outputs": [],
   "source": [
    "#adapted from\n",
    "# As input pybbi takes bigWig (bw) file, which is equivalent to bedgraph, but compressed and efficient.\n",
    "# If you have bedgraph files, you can do conversion from bedgraph to bigWig with kentUtils:\n",
    "# git clone https://github.com/ENCODE-DCC/kentUtils\n",
    "#./kentUtils/bin/linux.x86_64/bedGraphToBigWig file.bedgraph chrom.sizes file.bw\n",
    "# Note that for his conversion you need the chrom.sizes file for your species with chromosomes lengths\n",
    "# You can download them from: https://github.com/pkerpedjiev/negspy/tree/master/negspy/data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2fb61230",
   "metadata": {},
   "outputs": [],
   "source": [
    "atac1='/home/allana.schooley-umw/as38w/Ranger/atac/nfatac_wbl_R1R3/result/filtered/240424_endsfiles/ens_1bp/Mp.cov_1.s.bw'\n",
    "atac2='/home/allana.schooley-umw/as38w/Ranger/atac/nfatac_wbl_R1R3/result/filtered/240424_endsfiles/ens_1bp/mGp.cov_1.s.bw'\n",
    "atac3='/home/allana.schooley-umw/as38w/Ranger/atac/nfatac_wbl_R1R3/result/filtered/240424_endsfiles/ens_1bp/pGp.cov_1.s.bw'\n",
    "\n",
    "atac1s='/home/allana.schooley-umw/as38w/Ranger/atac/nfatac_wbl_R1R3/result/filtered/240424_endsfiles/ens_1bp/Mp.L120_1.cov.s.bw'\n",
    "atac2s='/home/allana.schooley-umw/as38w/Ranger/atac/nfatac_wbl_R1R3/result/filtered/240424_endsfiles/ens_1bp/mGp.L120_1.cov.s.bw'\n",
    "atac3s='/home/allana.schooley-umw/as38w/Ranger/atac/nfatac_wbl_R1R3/result/filtered/240424_endsfiles/ens_1bp/pGp.L120_1.cov.s.bw'\n",
    "\n",
    "atac1m='/home/allana.schooley-umw/as38w/Ranger/atac/nfatac_wbl_R1R3/result/filtered/240424_endsfiles/ens_1bp/Mp.G150_1.cov.s.bw'\n",
    "atac2m='/home/allana.schooley-umw/as38w/Ranger/atac/nfatac_wbl_R1R3/result/filtered/240424_endsfiles/ens_1bp/mGp.G150_1.cov.s.bw'\n",
    "atac3m='/home/allana.schooley-umw/as38w/Ranger/atac/nfatac_wbl_R1R3/result/filtered/240424_endsfiles/ens_1bp/pGp.G150_1.cov.bw'\n",
    "\n",
    "path1=atac1\n",
    "path2=atac2\n",
    "path3=atac3\n",
    "\n",
    "path4=atac1s\n",
    "path5=atac2s\n",
    "path6=atac3s\n",
    "\n",
    "path7=atac1m\n",
    "path8=atac2m\n",
    "path9=atac3m\n",
    "\n",
    "window = 500 # window size (one-sided)\n",
    "nbins = 100 # number of bins in the window\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f246626d-a8c1-49a2-ac3b-eaac54b01824",
   "metadata": {},
   "source": [
    "# Example for TSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "25839c44-5f7f-4830-89e9-a3e30974749d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11110 11110\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_863902/792823853.py:17: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sitesf['mid_shift']=tss['mid']-1000\n",
      "/tmp/ipykernel_863902/792823853.py:19: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sitesr['mid_shift']=tss['mid']+1000\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>strand</th>\n",
       "      <th>name</th>\n",
       "      <th>start_Rseq</th>\n",
       "      <th>en_Rseq</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>FPKM</th>\n",
       "      <th>TPM</th>\n",
       "      <th>TSS_start</th>\n",
       "      <th>TSS_end</th>\n",
       "      <th>TSS_start_ext</th>\n",
       "      <th>TSS_end_ext</th>\n",
       "      <th>mid</th>\n",
       "      <th>mid_shift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16995</th>\n",
       "      <td>chr13</td>\n",
       "      <td>51767992</td>\n",
       "      <td>51804163</td>\n",
       "      <td>-</td>\n",
       "      <td>DHRS12</td>\n",
       "      <td>51767993</td>\n",
       "      <td>51804163</td>\n",
       "      <td>7.351691</td>\n",
       "      <td>1.000361</td>\n",
       "      <td>2.120352</td>\n",
       "      <td>51804162</td>\n",
       "      <td>51804164</td>\n",
       "      <td>51799162</td>\n",
       "      <td>51809164</td>\n",
       "      <td>51804163</td>\n",
       "      <td>51805163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16996</th>\n",
       "      <td>chr1</td>\n",
       "      <td>148599806</td>\n",
       "      <td>148679764</td>\n",
       "      <td>-</td>\n",
       "      <td>NOTCH2NLB</td>\n",
       "      <td>148599807</td>\n",
       "      <td>148679764</td>\n",
       "      <td>17.153709</td>\n",
       "      <td>1.000406</td>\n",
       "      <td>2.120445</td>\n",
       "      <td>148679763</td>\n",
       "      <td>148679765</td>\n",
       "      <td>148674763</td>\n",
       "      <td>148684765</td>\n",
       "      <td>148679764</td>\n",
       "      <td>148680764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16998</th>\n",
       "      <td>chr13</td>\n",
       "      <td>112967483</td>\n",
       "      <td>112968638</td>\n",
       "      <td>-</td>\n",
       "      <td>MCF2L-AS1</td>\n",
       "      <td>112967484</td>\n",
       "      <td>112968638</td>\n",
       "      <td>17.432043</td>\n",
       "      <td>1.001571</td>\n",
       "      <td>2.122917</td>\n",
       "      <td>112968637</td>\n",
       "      <td>112968639</td>\n",
       "      <td>112963637</td>\n",
       "      <td>112973639</td>\n",
       "      <td>112968638</td>\n",
       "      <td>112969638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17000</th>\n",
       "      <td>chr7</td>\n",
       "      <td>149867693</td>\n",
       "      <td>149873862</td>\n",
       "      <td>-</td>\n",
       "      <td>ATP6V0E2-AS1</td>\n",
       "      <td>149867694</td>\n",
       "      <td>149873862</td>\n",
       "      <td>17.489712</td>\n",
       "      <td>1.004885</td>\n",
       "      <td>2.12994</td>\n",
       "      <td>149873861</td>\n",
       "      <td>149873863</td>\n",
       "      <td>149868861</td>\n",
       "      <td>149878863</td>\n",
       "      <td>149873862</td>\n",
       "      <td>149874862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17003</th>\n",
       "      <td>chr9</td>\n",
       "      <td>90609831</td>\n",
       "      <td>90642824</td>\n",
       "      <td>-</td>\n",
       "      <td>DIRAS2</td>\n",
       "      <td>90609832</td>\n",
       "      <td>90642824</td>\n",
       "      <td>17.528137</td>\n",
       "      <td>1.007093</td>\n",
       "      <td>2.134619</td>\n",
       "      <td>90642823</td>\n",
       "      <td>90642825</td>\n",
       "      <td>90637823</td>\n",
       "      <td>90647825</td>\n",
       "      <td>90642824</td>\n",
       "      <td>90643824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28211</th>\n",
       "      <td>chr17</td>\n",
       "      <td>19190028</td>\n",
       "      <td>19190245</td>\n",
       "      <td>-</td>\n",
       "      <td>SNORD3C</td>\n",
       "      <td>19190029</td>\n",
       "      <td>19190245</td>\n",
       "      <td>83680.953125</td>\n",
       "      <td>4807.953613</td>\n",
       "      <td>10190.870117</td>\n",
       "      <td>19190244</td>\n",
       "      <td>19190246</td>\n",
       "      <td>19185244</td>\n",
       "      <td>19195246</td>\n",
       "      <td>19190245</td>\n",
       "      <td>19191245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28212</th>\n",
       "      <td>chr17</td>\n",
       "      <td>19112419</td>\n",
       "      <td>19112636</td>\n",
       "      <td>-</td>\n",
       "      <td>SNORD3D</td>\n",
       "      <td>19112420</td>\n",
       "      <td>19112636</td>\n",
       "      <td>128503.679688</td>\n",
       "      <td>7383.277344</td>\n",
       "      <td>15649.490234</td>\n",
       "      <td>19112635</td>\n",
       "      <td>19112637</td>\n",
       "      <td>19107635</td>\n",
       "      <td>19117637</td>\n",
       "      <td>19112636</td>\n",
       "      <td>19113636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28213</th>\n",
       "      <td>chr14</td>\n",
       "      <td>20343070</td>\n",
       "      <td>20343411</td>\n",
       "      <td>-</td>\n",
       "      <td>RPPH1</td>\n",
       "      <td>20343071</td>\n",
       "      <td>20343411</td>\n",
       "      <td>133232.234375</td>\n",
       "      <td>7654.960449</td>\n",
       "      <td>16225.344727</td>\n",
       "      <td>20343410</td>\n",
       "      <td>20343412</td>\n",
       "      <td>20338410</td>\n",
       "      <td>20348412</td>\n",
       "      <td>20343411</td>\n",
       "      <td>20344411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28214</th>\n",
       "      <td>chr9</td>\n",
       "      <td>35657750</td>\n",
       "      <td>35658018</td>\n",
       "      <td>-</td>\n",
       "      <td>RMRP</td>\n",
       "      <td>35657751</td>\n",
       "      <td>35658018</td>\n",
       "      <td>352675.84375</td>\n",
       "      <td>20263.261719</td>\n",
       "      <td>42949.722656</td>\n",
       "      <td>35658017</td>\n",
       "      <td>35658019</td>\n",
       "      <td>35653017</td>\n",
       "      <td>35663019</td>\n",
       "      <td>35658018</td>\n",
       "      <td>35659018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28217</th>\n",
       "      <td>chr14</td>\n",
       "      <td>49862550</td>\n",
       "      <td>49862849</td>\n",
       "      <td>-</td>\n",
       "      <td>RN7SL2</td>\n",
       "      <td>49862551</td>\n",
       "      <td>49862849</td>\n",
       "      <td>551316.875</td>\n",
       "      <td>31676.333984</td>\n",
       "      <td>67140.710938</td>\n",
       "      <td>49862848</td>\n",
       "      <td>49862850</td>\n",
       "      <td>49857848</td>\n",
       "      <td>49867850</td>\n",
       "      <td>49862849</td>\n",
       "      <td>49863849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5439 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chrom      start        end strand          name start_Rseq    en_Rseq  \\\n",
       "16995  chr13   51767992   51804163      -        DHRS12   51767993   51804163   \n",
       "16996   chr1  148599806  148679764      -     NOTCH2NLB  148599807  148679764   \n",
       "16998  chr13  112967483  112968638      -     MCF2L-AS1  112967484  112968638   \n",
       "17000   chr7  149867693  149873862      -  ATP6V0E2-AS1  149867694  149873862   \n",
       "17003   chr9   90609831   90642824      -        DIRAS2   90609832   90642824   \n",
       "...      ...        ...        ...    ...           ...        ...        ...   \n",
       "28211  chr17   19190028   19190245      -       SNORD3C   19190029   19190245   \n",
       "28212  chr17   19112419   19112636      -       SNORD3D   19112420   19112636   \n",
       "28213  chr14   20343070   20343411      -         RPPH1   20343071   20343411   \n",
       "28214   chr9   35657750   35658018      -          RMRP   35657751   35658018   \n",
       "28217  chr14   49862550   49862849      -        RN7SL2   49862551   49862849   \n",
       "\n",
       "            Coverage          FPKM           TPM  TSS_start    TSS_end  \\\n",
       "16995       7.351691      1.000361      2.120352   51804162   51804164   \n",
       "16996      17.153709      1.000406      2.120445  148679763  148679765   \n",
       "16998      17.432043      1.001571      2.122917  112968637  112968639   \n",
       "17000      17.489712      1.004885       2.12994  149873861  149873863   \n",
       "17003      17.528137      1.007093      2.134619   90642823   90642825   \n",
       "...              ...           ...           ...        ...        ...   \n",
       "28211   83680.953125   4807.953613  10190.870117   19190244   19190246   \n",
       "28212  128503.679688   7383.277344  15649.490234   19112635   19112637   \n",
       "28213  133232.234375   7654.960449  16225.344727   20343410   20343412   \n",
       "28214   352675.84375  20263.261719  42949.722656   35658017   35658019   \n",
       "28217     551316.875  31676.333984  67140.710938   49862848   49862850   \n",
       "\n",
       "       TSS_start_ext  TSS_end_ext        mid  mid_shift  \n",
       "16995       51799162     51809164   51804163   51805163  \n",
       "16996      148674763    148684765  148679764  148680764  \n",
       "16998      112963637    112973639  112968638  112969638  \n",
       "17000      149868861    149878863  149873862  149874862  \n",
       "17003       90637823     90647825   90642824   90643824  \n",
       "...              ...          ...        ...        ...  \n",
       "28211       19185244     19195246   19190245   19191245  \n",
       "28212       19107635     19117637   19112636   19113636  \n",
       "28213       20338410     20348412   20343411   20344411  \n",
       "28214       35653017     35663019   35658018   35659018  \n",
       "28217       49857848     49867850   49862849   49863849  \n",
       "\n",
       "[5439 rows x 16 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#table crossed with on genes. NB strands are already flipped\n",
    "tss=pd.read_csv('/home/allana.schooley-umw/as38w/Ranger/RNAseq/RG0-FPKM.at.hg38.refGene.exons.reducedchroms.bed',sep='\\t', \\\n",
    "                  names=['chrom','start','end','strand','name','start_Rseq','en_Rseq','Coverage','FPKM','TPM','TSS_start','TSS_end','TSS_start_ext','TSS_end_ext'])\n",
    "chroms=['chr1','chr2','chr3','chr4','chr5','chr6','chr7','chr8','chr9','chr10','chr11','chr12','chr13','chr14','chr15','chr16','chr17','chr18','chr19','chr20','chr21','chr22']\n",
    "tss=tss[tss['chrom'].isin(chroms)].drop_duplicates(subset=['chrom','start','end'])\n",
    "\n",
    "tss['FPKM']=tss['FPKM'].astype(float)\n",
    "tss['TSS_start']=tss['TSS_start'].astype(int)\n",
    "tss['TSS_end']=tss['TSS_end'].astype(int)\n",
    "tss['TSS_start_ext']=tss['TSS_start']-5000\n",
    "tss['TSS_end_ext']=tss['TSS_end']+5000\n",
    "tss['mid']=(tss['TSS_start']+((tss['TSS_end']-tss['TSS_start'])/2)).astype(int)\n",
    "\n",
    "tss=tss[tss['FPKM']>1]\n",
    "\n",
    "sitesf=tss[tss['strand']=='+']\n",
    "sitesf['mid_shift']=tss['mid']-1000\n",
    "sitesr=tss[tss['strand']=='-']\n",
    "sitesr['mid_shift']=tss['mid']+1000\n",
    "\n",
    "#tss=tss[['chrom','TSS_start','TSS_end','strand','FPKM']]\n",
    "print(len(sitesf)+len(sitesr),len(tss))\n",
    "sitesr\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88277477-0708-48de-a2ce-3c831e0c3415",
   "metadata": {},
   "source": [
    "# MCD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "33dfc361-0f7a-47bc-b89e-91c7c6e9114b",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>cluster</th>\n",
       "      <th>size</th>\n",
       "      <th>valency</th>\n",
       "      <th>peak_start</th>\n",
       "      <th>peak_end</th>\n",
       "      <th>size.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr1</td>\n",
       "      <td>1020000</td>\n",
       "      <td>1030000</td>\n",
       "      <td>0</td>\n",
       "      <td>30000</td>\n",
       "      <td>3</td>\n",
       "      <td>1010000</td>\n",
       "      <td>1040000</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr1</td>\n",
       "      <td>1900000</td>\n",
       "      <td>1910000</td>\n",
       "      <td>1</td>\n",
       "      <td>30000</td>\n",
       "      <td>2</td>\n",
       "      <td>1890000</td>\n",
       "      <td>1920000</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>6600000</td>\n",
       "      <td>6610000</td>\n",
       "      <td>2</td>\n",
       "      <td>30000</td>\n",
       "      <td>2</td>\n",
       "      <td>6590000</td>\n",
       "      <td>6620000</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr1</td>\n",
       "      <td>7770000</td>\n",
       "      <td>7780000</td>\n",
       "      <td>3</td>\n",
       "      <td>30000</td>\n",
       "      <td>2</td>\n",
       "      <td>7760000</td>\n",
       "      <td>7790000</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr1</td>\n",
       "      <td>7990000</td>\n",
       "      <td>8000000</td>\n",
       "      <td>4</td>\n",
       "      <td>40000</td>\n",
       "      <td>2</td>\n",
       "      <td>7980000</td>\n",
       "      <td>8020000</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100</th>\n",
       "      <td>chr22</td>\n",
       "      <td>45170000</td>\n",
       "      <td>45180000</td>\n",
       "      <td>2100</td>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>45140000</td>\n",
       "      <td>45190000</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101</th>\n",
       "      <td>chr22</td>\n",
       "      <td>46070000</td>\n",
       "      <td>46080000</td>\n",
       "      <td>2101</td>\n",
       "      <td>130000</td>\n",
       "      <td>11</td>\n",
       "      <td>46020000</td>\n",
       "      <td>46150000</td>\n",
       "      <td>130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2102</th>\n",
       "      <td>chr22</td>\n",
       "      <td>46750000</td>\n",
       "      <td>46760000</td>\n",
       "      <td>2102</td>\n",
       "      <td>30000</td>\n",
       "      <td>2</td>\n",
       "      <td>46740000</td>\n",
       "      <td>46770000</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2103</th>\n",
       "      <td>chr22</td>\n",
       "      <td>49940000</td>\n",
       "      <td>49950000</td>\n",
       "      <td>2103</td>\n",
       "      <td>70000</td>\n",
       "      <td>5</td>\n",
       "      <td>49920000</td>\n",
       "      <td>49990000</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2104</th>\n",
       "      <td>chr22</td>\n",
       "      <td>50290000</td>\n",
       "      <td>50300000</td>\n",
       "      <td>2104</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>50260000</td>\n",
       "      <td>50320000</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2105 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      chrom     start       end  cluster    size  valency  peak_start  \\\n",
       "0      chr1   1020000   1030000        0   30000        3     1010000   \n",
       "1      chr1   1900000   1910000        1   30000        2     1890000   \n",
       "2      chr1   6600000   6610000        2   30000        2     6590000   \n",
       "3      chr1   7770000   7780000        3   30000        2     7760000   \n",
       "4      chr1   7990000   8000000        4   40000        2     7980000   \n",
       "...     ...       ...       ...      ...     ...      ...         ...   \n",
       "2100  chr22  45170000  45180000     2100   50000        2    45140000   \n",
       "2101  chr22  46070000  46080000     2101  130000       11    46020000   \n",
       "2102  chr22  46750000  46760000     2102   30000        2    46740000   \n",
       "2103  chr22  49940000  49950000     2103   70000        5    49920000   \n",
       "2104  chr22  50290000  50300000     2104   60000        4    50260000   \n",
       "\n",
       "      peak_end  size.1  \n",
       "0      1040000   30000  \n",
       "1      1920000   30000  \n",
       "2      6620000   30000  \n",
       "3      7790000   30000  \n",
       "4      8020000   40000  \n",
       "...        ...     ...  \n",
       "2100  45190000   50000  \n",
       "2101  46150000  130000  \n",
       "2102  46770000   30000  \n",
       "2103  49990000   70000  \n",
       "2104  50320000   60000  \n",
       "\n",
       "[2105 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IDa=pd.read_csv('/home/allana.schooley-umw/as38w/Ranger/nativecomps/240314_newIDs/5hr_2X_enrichment.pixel_derived.signal_peaks.bed',sep='\\t')\n",
    "IDa"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4279b534-569a-4eb6-8502-2a1011fb4eaf",
   "metadata": {},
   "source": [
    "# TSS at MCD\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8da3f782-af1b-4d66-a07d-ce1a8fe20d32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2795\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>strand</th>\n",
       "      <th>name</th>\n",
       "      <th>start_Rseq</th>\n",
       "      <th>en_Rseq</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>FPKM</th>\n",
       "      <th>TPM</th>\n",
       "      <th>...</th>\n",
       "      <th>size.1_</th>\n",
       "      <th>chrom_</th>\n",
       "      <th>start_</th>\n",
       "      <th>end_</th>\n",
       "      <th>cluster_</th>\n",
       "      <th>size_</th>\n",
       "      <th>valency_</th>\n",
       "      <th>peak_start_</th>\n",
       "      <th>peak_end_</th>\n",
       "      <th>size.1_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr19</td>\n",
       "      <td>37371098</td>\n",
       "      <td>37393066</td>\n",
       "      <td>+</td>\n",
       "      <td>ZNF527</td>\n",
       "      <td>37371099</td>\n",
       "      <td>37393066</td>\n",
       "      <td>17.427691</td>\n",
       "      <td>1.001321</td>\n",
       "      <td>2.122386</td>\n",
       "      <td>...</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>chr19</td>\n",
       "      <td>37360000</td>\n",
       "      <td>37370000</td>\n",
       "      <td>1998</td>\n",
       "      <td>30000</td>\n",
       "      <td>2</td>\n",
       "      <td>37350000</td>\n",
       "      <td>37380000</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr6</td>\n",
       "      <td>31739947</td>\n",
       "      <td>31764847</td>\n",
       "      <td>+</td>\n",
       "      <td>MSH5-SAPCD1</td>\n",
       "      <td>31739948</td>\n",
       "      <td>31764847</td>\n",
       "      <td>17.605137</td>\n",
       "      <td>1.011517</td>\n",
       "      <td>2.143996</td>\n",
       "      <td>...</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>chr6</td>\n",
       "      <td>31730000</td>\n",
       "      <td>31740000</td>\n",
       "      <td>928</td>\n",
       "      <td>50000</td>\n",
       "      <td>6</td>\n",
       "      <td>31710000</td>\n",
       "      <td>31760000</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr14</td>\n",
       "      <td>23561096</td>\n",
       "      <td>23568075</td>\n",
       "      <td>+</td>\n",
       "      <td>LOC102724814</td>\n",
       "      <td>23561097</td>\n",
       "      <td>23568075</td>\n",
       "      <td>17.849609</td>\n",
       "      <td>1.025563</td>\n",
       "      <td>2.173769</td>\n",
       "      <td>...</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>chr14</td>\n",
       "      <td>23550000</td>\n",
       "      <td>23560000</td>\n",
       "      <td>1711</td>\n",
       "      <td>50000</td>\n",
       "      <td>8</td>\n",
       "      <td>23530000</td>\n",
       "      <td>23580000</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr5</td>\n",
       "      <td>119268758</td>\n",
       "      <td>119399688</td>\n",
       "      <td>+</td>\n",
       "      <td>TNFAIP8</td>\n",
       "      <td>119268759</td>\n",
       "      <td>119399688</td>\n",
       "      <td>1.893057</td>\n",
       "      <td>1.036486</td>\n",
       "      <td>2.196921</td>\n",
       "      <td>...</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>chr5</td>\n",
       "      <td>119280000</td>\n",
       "      <td>119290000</td>\n",
       "      <td>820</td>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>119260000</td>\n",
       "      <td>119310000</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr19</td>\n",
       "      <td>47226941</td>\n",
       "      <td>47227021</td>\n",
       "      <td>+</td>\n",
       "      <td>MIR3190</td>\n",
       "      <td>47226942</td>\n",
       "      <td>47227021</td>\n",
       "      <td>18.11977</td>\n",
       "      <td>1.041085</td>\n",
       "      <td>2.20667</td>\n",
       "      <td>...</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>chr19</td>\n",
       "      <td>47250000</td>\n",
       "      <td>47260000</td>\n",
       "      <td>2009</td>\n",
       "      <td>40000</td>\n",
       "      <td>4</td>\n",
       "      <td>47230000</td>\n",
       "      <td>47270000</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432</th>\n",
       "      <td>chr11</td>\n",
       "      <td>809966</td>\n",
       "      <td>812876</td>\n",
       "      <td>+</td>\n",
       "      <td>RPLP2</td>\n",
       "      <td>809967</td>\n",
       "      <td>812876</td>\n",
       "      <td>26734.267578</td>\n",
       "      <td>1536.037842</td>\n",
       "      <td>3255.76416</td>\n",
       "      <td>...</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>chr11</td>\n",
       "      <td>830000</td>\n",
       "      <td>840000</td>\n",
       "      <td>1425</td>\n",
       "      <td>100000</td>\n",
       "      <td>6</td>\n",
       "      <td>760000</td>\n",
       "      <td>860000</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>chr1</td>\n",
       "      <td>153990754</td>\n",
       "      <td>153992155</td>\n",
       "      <td>+</td>\n",
       "      <td>RPS27</td>\n",
       "      <td>153990755</td>\n",
       "      <td>153992155</td>\n",
       "      <td>17151.001953</td>\n",
       "      <td>1646.211182</td>\n",
       "      <td>3489.285889</td>\n",
       "      <td>...</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>chr1</td>\n",
       "      <td>153960000</td>\n",
       "      <td>153970000</td>\n",
       "      <td>80</td>\n",
       "      <td>70000</td>\n",
       "      <td>18</td>\n",
       "      <td>153930000</td>\n",
       "      <td>154000000</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1434</th>\n",
       "      <td>chr6</td>\n",
       "      <td>33272074</td>\n",
       "      <td>33276511</td>\n",
       "      <td>+</td>\n",
       "      <td>RPS18</td>\n",
       "      <td>33272075</td>\n",
       "      <td>33276511</td>\n",
       "      <td>32330.914062</td>\n",
       "      <td>1857.597534</td>\n",
       "      <td>3937.337402</td>\n",
       "      <td>...</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>chr6</td>\n",
       "      <td>33280000</td>\n",
       "      <td>33290000</td>\n",
       "      <td>933</td>\n",
       "      <td>90000</td>\n",
       "      <td>11</td>\n",
       "      <td>33240000</td>\n",
       "      <td>33330000</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>chr2</td>\n",
       "      <td>84905655</td>\n",
       "      <td>84906671</td>\n",
       "      <td>+</td>\n",
       "      <td>TMSB10</td>\n",
       "      <td>84905656</td>\n",
       "      <td>84906671</td>\n",
       "      <td>36505.265625</td>\n",
       "      <td>2097.438232</td>\n",
       "      <td>4445.699707</td>\n",
       "      <td>...</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>chr2</td>\n",
       "      <td>84930000</td>\n",
       "      <td>84940000</td>\n",
       "      <td>299</td>\n",
       "      <td>80000</td>\n",
       "      <td>9</td>\n",
       "      <td>84890000</td>\n",
       "      <td>84970000</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>chr14</td>\n",
       "      <td>49586579</td>\n",
       "      <td>49586878</td>\n",
       "      <td>+</td>\n",
       "      <td>RN7SL1</td>\n",
       "      <td>49586580</td>\n",
       "      <td>49586878</td>\n",
       "      <td>467574.59375</td>\n",
       "      <td>26864.857422</td>\n",
       "      <td>56942.371094</td>\n",
       "      <td>...</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>chr14</td>\n",
       "      <td>49610000</td>\n",
       "      <td>49620000</td>\n",
       "      <td>1725</td>\n",
       "      <td>70000</td>\n",
       "      <td>10</td>\n",
       "      <td>49580000</td>\n",
       "      <td>49650000</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1404 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      chrom      start        end strand          name start_Rseq    en_Rseq  \\\n",
       "0     chr19   37371098   37393066      +        ZNF527   37371099   37393066   \n",
       "1      chr6   31739947   31764847      +   MSH5-SAPCD1   31739948   31764847   \n",
       "2     chr14   23561096   23568075      +  LOC102724814   23561097   23568075   \n",
       "3      chr5  119268758  119399688      +       TNFAIP8  119268759  119399688   \n",
       "4     chr19   47226941   47227021      +       MIR3190   47226942   47227021   \n",
       "...     ...        ...        ...    ...           ...        ...        ...   \n",
       "1432  chr11     809966     812876      +         RPLP2     809967     812876   \n",
       "1433   chr1  153990754  153992155      +         RPS27  153990755  153992155   \n",
       "1434   chr6   33272074   33276511      +         RPS18   33272075   33276511   \n",
       "1435   chr2   84905655   84906671      +        TMSB10   84905656   84906671   \n",
       "1436  chr14   49586579   49586878      +        RN7SL1   49586580   49586878   \n",
       "\n",
       "          Coverage          FPKM           TPM  ...   size.1_  chrom_  \\\n",
       "0        17.427691      1.001321      2.122386  ...   30000.0   chr19   \n",
       "1        17.605137      1.011517      2.143996  ...   50000.0    chr6   \n",
       "2        17.849609      1.025563      2.173769  ...   50000.0   chr14   \n",
       "3         1.893057      1.036486      2.196921  ...   50000.0    chr5   \n",
       "4         18.11977      1.041085       2.20667  ...   40000.0   chr19   \n",
       "...            ...           ...           ...  ...       ...     ...   \n",
       "1432  26734.267578   1536.037842    3255.76416  ...  100000.0   chr11   \n",
       "1433  17151.001953   1646.211182   3489.285889  ...   70000.0    chr1   \n",
       "1434  32330.914062   1857.597534   3937.337402  ...   90000.0    chr6   \n",
       "1435  36505.265625   2097.438232   4445.699707  ...   80000.0    chr2   \n",
       "1436  467574.59375  26864.857422  56942.371094  ...   70000.0   chr14   \n",
       "\n",
       "         start_       end_  cluster_   size_ valency_  peak_start_  peak_end_  \\\n",
       "0      37360000   37370000      1998   30000        2     37350000   37380000   \n",
       "1      31730000   31740000       928   50000        6     31710000   31760000   \n",
       "2      23550000   23560000      1711   50000        8     23530000   23580000   \n",
       "3     119280000  119290000       820   50000        2    119260000  119310000   \n",
       "4      47250000   47260000      2009   40000        4     47230000   47270000   \n",
       "...         ...        ...       ...     ...      ...          ...        ...   \n",
       "1432     830000     840000      1425  100000        6       760000     860000   \n",
       "1433  153960000  153970000        80   70000       18    153930000  154000000   \n",
       "1434   33280000   33290000       933   90000       11     33240000   33330000   \n",
       "1435   84930000   84940000       299   80000        9     84890000   84970000   \n",
       "1436   49610000   49620000      1725   70000       10     49580000   49650000   \n",
       "\n",
       "      size.1_  \n",
       "0       30000  \n",
       "1       50000  \n",
       "2       50000  \n",
       "3       50000  \n",
       "4       40000  \n",
       "...       ...  \n",
       "1432   100000  \n",
       "1433    70000  \n",
       "1434    90000  \n",
       "1435    80000  \n",
       "1436    70000  \n",
       "\n",
       "[1404 rows x 34 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import bioframe as bf\n",
    "\n",
    "sites_IDf=bf.overlap(sitesf,IDa,cols1=['chrom','TSS_start_ext','TSS_end_ext'],cols2=['chrom','peak_start','peak_end']).dropna(subset='valency_').drop_duplicates(['chrom','start','end'])\n",
    "sites_IDr=bf.overlap(sitesr,IDa,cols1=['chrom','TSS_start_ext','TSS_end_ext'],cols2=['chrom','peak_start','peak_end']).dropna(subset='valency_').drop_duplicates(['chrom','start','end'])\n",
    "sitesf=sites_IDf\n",
    "sitesr=sites_IDr\n",
    "print(len(sites_IDf)+len(sites_IDr))\n",
    "sites_IDf\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1688b4a2-b88a-4e1d-8e0f-e0ba6630586f",
   "metadata": {},
   "source": [
    "# run forward\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "89a2e1fa-8907-42ba-a7f0-47c09e491d22",
   "metadata": {},
   "outputs": [],
   "source": [
    "sites=sitesf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bdfbaf22-159f-4972-beea-b0a83ebe1809",
   "metadata": {},
   "outputs": [],
   "source": [
    "chroms=sites.chrom\n",
    "starts=sites.mid-window\n",
    "ends=sites.mid+window\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "88b7ef04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creation of numpy 2D array with annotation snippets\n",
    "s1 = bbi.stackup(path1, chroms, starts, ends, bins=nbins) \n",
    "s1[np.isnan(s1)] = 0\n",
    "s2 = bbi.stackup(path2, chroms, starts, ends, bins=nbins) \n",
    "s2[np.isnan(s2)] = 0\n",
    "s3 = bbi.stackup(path3, chroms, starts, ends, bins=nbins) \n",
    "s3[np.isnan(s3)] = 0\n",
    "\n",
    "s4 = bbi.stackup(path4, chroms, starts, ends, bins=nbins) \n",
    "s4[np.isnan(s4)] = 0\n",
    "s5 = bbi.stackup(path5, chroms, starts, ends, bins=nbins) \n",
    "s5[np.isnan(s5)] = 0\n",
    "s6 = bbi.stackup(path6, chroms, starts, ends, bins=nbins) \n",
    "s6[np.isnan(s6)] = 0\n",
    "\n",
    "s7 = bbi.stackup(path7, chroms, starts, ends, bins=nbins) \n",
    "s7[np.isnan(s7)] = 0\n",
    "s8 = bbi.stackup(path8, chroms, starts, ends, bins=nbins) \n",
    "s8[np.isnan(s8)] = 0\n",
    "s9 = bbi.stackup(path9, chroms, starts, ends, bins=nbins) \n",
    "s9[np.isnan(s9)] = 0\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "382b491f-49ac-4d61-a18e-77aa162cf432",
   "metadata": {},
   "source": [
    "# forward upstream window to normalise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "984dcad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "chroms=sites.chrom\n",
    "starts=sites.mid_shift-window\n",
    "ends=sites.mid_shift+window\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c00e8c15-e4c4-4487-a61e-2da585a478ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creation of numpy 2D array with annotation snippets\n",
    "s1b = bbi.stackup(path1, chroms, starts, ends, bins=nbins) \n",
    "s1b[np.isnan(s1b)] = 0\n",
    "s2b = bbi.stackup(path2, chroms, starts, ends, bins=nbins) \n",
    "s2b[np.isnan(s2b)] = 0\n",
    "s3b = bbi.stackup(path3, chroms, starts, ends, bins=nbins) \n",
    "s3b[np.isnan(s3b)] = 0\n",
    "\n",
    "s4b = bbi.stackup(path4, chroms, starts, ends, bins=nbins) \n",
    "s4b[np.isnan(s4b)] = 0\n",
    "s5b = bbi.stackup(path5, chroms, starts, ends, bins=nbins) \n",
    "s5b[np.isnan(s5b)] = 0\n",
    "s6b = bbi.stackup(path6, chroms, starts, ends, bins=nbins) \n",
    "s6b[np.isnan(s6b)] = 0\n",
    "\n",
    "s7b = bbi.stackup(path7, chroms, starts, ends, bins=nbins) \n",
    "s7b[np.isnan(s7b)] = 0\n",
    "s8b = bbi.stackup(path8, chroms, starts, ends, bins=nbins) \n",
    "s8b[np.isnan(s8b)] = 0\n",
    "s9b = bbi.stackup(path9, chroms, starts, ends, bins=nbins) \n",
    "s9b[np.isnan(s9b)] = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d95f4a01-2909-44d0-894a-d33e49a58955",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1n=s1/s1b.mean()\n",
    "s2n=s2/s2b.mean()\n",
    "s3n=s3/s3b.mean()\n",
    "s4n=s4/s4b.mean()\n",
    "s5n=s5/s5b.mean()\n",
    "s6n=s6/s6b.mean()\n",
    "s7n=s7/s7b.mean()\n",
    "s8n=s8/s8b.mean()\n",
    "s9n=s9/s9b.mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "68749222",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ordering\n",
    "for_order1 = s1n[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order1 = np.argsort(for_order1)[::-1]\n",
    "for_order2 = s2n[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order2 = np.argsort(for_order2)[::-1]\n",
    "for_order3 = s3n[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order3 = np.argsort(for_order3)[::-1]\n",
    "\n",
    "for_order4 = s4n[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order4 = np.argsort(for_order4)[::-1]\n",
    "for_order5 = s5n[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order5 = np.argsort(for_order5)[::-1]\n",
    "for_order6 = s6n[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order6 = np.argsort(for_order6)[::-1]\n",
    "\n",
    "for_order7 = s7n[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order7 = np.argsort(for_order7)[::-1]\n",
    "for_order8 = s8n[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order8 = np.argsort(for_order8)[::-1]\n",
    "for_order9 = s9n[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order9 = np.argsort(for_order9)[::-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0b2a698b-4090-4762-bd16-b374452b5aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1f=s1n[order1,:].sum(axis=0)/len(s1n)\n",
    "s2f=s2n[order1,:].sum(axis=0)/len(s2n)\n",
    "s3f=s3n[order1,:].sum(axis=0)/len(s3n)\n",
    "s4f=s4n[order1,:].sum(axis=0)/len(s4n)\n",
    "s5f=s5n[order1,:].sum(axis=0)/len(s5n)\n",
    "s6f=s6n[order1,:].sum(axis=0)/len(s6n)\n",
    "s7f=s7n[order1,:].sum(axis=0)/len(s7n)\n",
    "s8f=s8n[order1,:].sum(axis=0)/len(s8n)\n",
    "s9f=s9n[order1,:].sum(axis=0)/len(s9n)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "053acc28-e074-4a8e-bfdb-8e56411279dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x149760b593a0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2,ax3)=plt.subplots(1,3,figsize=(9,2),sharey=True)\n",
    "\n",
    "ax1.plot(s1f,label='mitotic',color='k',lw=0.5)\n",
    "ax1.plot(s2f, label = 'ctrl G1',color='blue',lw=0.5)\n",
    "ax1.plot(s3f,label= 'Δ RanGAP1',color='red',lw=0.5)\n",
    "\n",
    "ax2.plot(s4f,label='mitotic',color='k',lw=0.5)\n",
    "ax2.plot(s5f, label = 'ctrl G1',color='blue',lw=0.5)\n",
    "ax2.plot(s6f,label= 'Δ RanGAP1',color='red',lw=0.5)\n",
    "\n",
    "ax3.plot(s7f,label='mitotic',color='k',lw=0.5)\n",
    "ax3.plot(s8f, label = 'ctrl G1',color='blue',lw=0.5)\n",
    "ax3.plot(s9f,label= 'Δ RanGAP1',color='red',lw=0.5)\n",
    "\n",
    "for ax in ax1,ax2,ax3:\n",
    "    #ax.set_yscale('log')\n",
    "    #ax.set_ylim(0,0.25)\n",
    "    ax.set_xlim(0,100)\n",
    "    ax.set_xticks([0,25,50,75,100])\n",
    "    ax.set_xticklabels([-500,-250,0,250,500],visible=True,rotation=90,ha='center')\n",
    "    ax.xaxis.set_ticks_position(\"bottom\")\n",
    "    ax.grid(axis='x',color='grey', linewidth=0.5 , ls=':')\n",
    "    ax.set_xlabel('distance (bp)')  \n",
    "    \n",
    "ax1.set_ylabel('ctcf-f no TSS 2000bp') \n",
    "ax1.set_title('all')\n",
    "ax2.set_title('sub-nucleosome')\n",
    "ax3.set_title('>= mono-nucleosome')\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "ax.legend(bbox_to_anchor=(1.75, 1.025))\n",
    "\n",
    "#plt.savefig('.pdf',dpi=300,bbox_inches='tight')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "419350a3-b136-4c0b-8a42-69670421cdcd",
   "metadata": {},
   "source": [
    "# same but reversed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ba9e4746-74b2-4327-b20c-10a2813db00e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sites=sitesr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a9191486-2c50-4583-8478-ce8e200c9241",
   "metadata": {},
   "outputs": [],
   "source": [
    "chroms=sites.chrom\n",
    "starts=sites.mid-window\n",
    "ends=sites.mid+window\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5a988ec6-b832-4b32-b78f-4740508890d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creation of numpy 2D array with annotation snippets\n",
    "s1 = bbi.stackup(path1, chroms, starts, ends, bins=nbins) \n",
    "s1[np.isnan(s1)] = 0\n",
    "s2 = bbi.stackup(path2, chroms, starts, ends, bins=nbins) \n",
    "s2[np.isnan(s2)] = 0\n",
    "s3 = bbi.stackup(path3, chroms, starts, ends, bins=nbins) \n",
    "s3[np.isnan(s3)] = 0\n",
    "\n",
    "s4 = bbi.stackup(path4, chroms, starts, ends, bins=nbins) \n",
    "s4[np.isnan(s4)] = 0\n",
    "s5 = bbi.stackup(path5, chroms, starts, ends, bins=nbins) \n",
    "s5[np.isnan(s5)] = 0\n",
    "s6 = bbi.stackup(path6, chroms, starts, ends, bins=nbins) \n",
    "s6[np.isnan(s6)] = 0\n",
    "\n",
    "s7 = bbi.stackup(path7, chroms, starts, ends, bins=nbins) \n",
    "s7[np.isnan(s7)] = 0\n",
    "s8 = bbi.stackup(path8, chroms, starts, ends, bins=nbins) \n",
    "s8[np.isnan(s8)] = 0\n",
    "s9 = bbi.stackup(path9, chroms, starts, ends, bins=nbins) \n",
    "s9[np.isnan(s9)] = 0\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40a229a8-7357-4339-a3cc-9117223085b2",
   "metadata": {},
   "source": [
    "# upstream window for normalisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "329ddda3-6cfa-488f-82ef-5fcbbc447489",
   "metadata": {},
   "outputs": [],
   "source": [
    "chroms=sites.chrom\n",
    "starts=sites.mid_shift-window\n",
    "ends=sites.mid_shift+window\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0ba395fd-1840-4fc8-b04d-8752c718c92e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creation of numpy 2D array with annotation snippets\n",
    "s1b = bbi.stackup(path1, chroms, starts, ends, bins=nbins) \n",
    "s1b[np.isnan(s1b)] = 0\n",
    "s2b = bbi.stackup(path2, chroms, starts, ends, bins=nbins) \n",
    "s2b[np.isnan(s2b)] = 0\n",
    "s3b = bbi.stackup(path3, chroms, starts, ends, bins=nbins) \n",
    "s3b[np.isnan(s3b)] = 0\n",
    "\n",
    "s4b = bbi.stackup(path4, chroms, starts, ends, bins=nbins) \n",
    "s4b[np.isnan(s4b)] = 0\n",
    "s5b = bbi.stackup(path5, chroms, starts, ends, bins=nbins) \n",
    "s5b[np.isnan(s5b)] = 0\n",
    "s6b = bbi.stackup(path6, chroms, starts, ends, bins=nbins) \n",
    "s6b[np.isnan(s6b)] = 0\n",
    "\n",
    "s7b = bbi.stackup(path7, chroms, starts, ends, bins=nbins) \n",
    "s7b[np.isnan(s7b)] = 0\n",
    "s8b = bbi.stackup(path8, chroms, starts, ends, bins=nbins) \n",
    "s8b[np.isnan(s8b)] = 0\n",
    "s9b = bbi.stackup(path9, chroms, starts, ends, bins=nbins) \n",
    "s9b[np.isnan(s9b)] = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2e254c69-d9e5-428b-b759-01666bbd79a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1p=s1/s1b.mean()\n",
    "s2p=s2/s2b.mean()\n",
    "s3p=s3/s3b.mean()\n",
    "s4p=s4/s4b.mean()\n",
    "s5p=s5/s5b.mean()\n",
    "s6p=s6/s6b.mean()\n",
    "s7p=s7/s7b.mean()\n",
    "s8p=s8/s8b.mean()\n",
    "s9p=s9/s9b.mean()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "62c2bb02-2f01-47bf-a4b8-d48a2c2fc5a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ordering\n",
    "for_order1 = s1p[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order1 = np.argsort(for_order1)[::-1]\n",
    "for_order2 = s2p[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order2 = np.argsort(for_order2)[::-1]\n",
    "for_order3 = s3p[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order3 = np.argsort(for_order3)[::-1]\n",
    "\n",
    "for_order4 = s4p[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order4 = np.argsort(for_order4)[::-1]\n",
    "for_order5 = s5p[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order5 = np.argsort(for_order5)[::-1]\n",
    "for_order6 = s6p[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order6 = np.argsort(for_order6)[::-1]\n",
    "\n",
    "for_order7 = s7p[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order7 = np.argsort(for_order7)[::-1]\n",
    "for_order8 = s8p[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order8 = np.argsort(for_order8)[::-1]\n",
    "for_order9 = s9p[:,8:13].sum(axis=1) # ordering by central values of stack\n",
    "order9 = np.argsort(for_order9)[::-1]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4a1e35e7-63b2-4e72-a270-994a10470769",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1r=s1p[order1,:].sum(axis=0)/len(s1p)\n",
    "s1r=np.flip(s1r)\n",
    "s2r=s2p[order1,:].sum(axis=0)/len(s2p)\n",
    "s2r=np.flip(s2r)\n",
    "s3r=s3p[order1,:].sum(axis=0)/len(s3p)\n",
    "s3r=np.flip(s3r)\n",
    "s4r=s4p[order1,:].sum(axis=0)/len(s4p)\n",
    "s4r=np.flip(s4r)\n",
    "s5r=s5p[order1,:].sum(axis=0)/len(s5p)\n",
    "s5r=np.flip(s5r)\n",
    "s6r=s6p[order1,:].sum(axis=0)/len(s6p)\n",
    "s6r=np.flip(s6r)\n",
    "s7r=s7p[order1,:].sum(axis=0)/len(s7p)\n",
    "s7r=np.flip(s7r)\n",
    "s8r=s8p[order1,:].sum(axis=0)/len(s8p)\n",
    "s8r=np.flip(s8r)\n",
    "s9r=s9p[order1,:].sum(axis=0)/len(s9p)\n",
    "s9r=np.flip(s9r)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7c2684a2-f54a-4f56-b6a6-18d5d83b91c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14975f936ca0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2,ax3)=plt.subplots(1,3,figsize=(9,2),sharey=True)\n",
    "\n",
    "ax1.plot(s1r,label='mitotic',color='k',lw=0.5)\n",
    "ax1.plot(s2r, label = 'ctrl G1',color='blue',lw=0.5)\n",
    "ax1.plot(s3r,label= 'Δ RanGAP1',color='red',lw=0.5)\n",
    "\n",
    "ax2.plot(s4r,label='mitotic',color='k',lw=0.5)\n",
    "ax2.plot(s5r, label = 'ctrl G1',color='blue',lw=0.5)\n",
    "ax2.plot(s6r,label= 'Δ RanGAP1',color='red',lw=0.5)\n",
    "\n",
    "ax3.plot(s7r,label='mitotic',color='k',lw=0.5)\n",
    "ax3.plot(s8r, label = 'ctrl G1',color='blue',lw=0.5)\n",
    "ax3.plot(s9r,label= 'Δ RanGAP1',color='red',lw=0.5)\n",
    "\n",
    "\n",
    "for ax in ax1,ax2,ax3:\n",
    "    #ax.set_yscale('log')\n",
    "    #ax.set_ylim(0,0.25)\n",
    "    ax.set_xlim(0,100)\n",
    "    ax.set_xticks([0,25,50,75,100])\n",
    "    ax.set_xticklabels([-500,-250,0,250,500],visible=True,rotation=90,ha='center')\n",
    "    ax.xaxis.set_ticks_position(\"bottom\")\n",
    "    ax.grid(axis='x',color='grey', linewidth=0.5 , ls=':')\n",
    "    ax.set_xlabel('distance (bp)')  \n",
    "    \n",
    "ax1.set_ylabel('enh 200bp tss') \n",
    "ax1.set_title('all')\n",
    "ax2.set_title('sub-nucleosome')\n",
    "ax3.set_title('>= mono-nucleosome')\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "ax.legend(bbox_to_anchor=(1.75, 1.025))\n",
    "\n",
    "#plt.savefig('.pdf',dpi=300,bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e01d63c4-9f23-4d71-a8a6-419b4f3038fe",
   "metadata": {},
   "source": [
    "#   F + R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4d03c09d-835a-4219-b9bc-e8186f0f4f5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1=(s1f+s1r)/2\n",
    "s2=(s2f+s2r)/2\n",
    "s3=(s3f+s3r)/2\n",
    "s4=(s4f+s4r)/2\n",
    "s5=(s5f+s5r)/2\n",
    "s6=(s6f+s6r)/2\n",
    "s7=(s7f+s7r)/2\n",
    "s8=(s8f+s8r)/2\n",
    "s9=(s9f+s9r)/2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "db85a69b-048f-4005-b6d7-9b0b4e857c4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14975eb0d370>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2,ax3)=plt.subplots(1,3,figsize=(9,2),sharey=True)\n",
    "\n",
    "ax1.plot(s1,label='mitotic',color='k',lw=0.5)\n",
    "ax1.plot(s2, label = 'ctrl G1',color='blue',lw=0.5)\n",
    "ax1.plot(s3,label= 'Δ RanGAP1',color='red',lw=0.5)\n",
    "\n",
    "ax2.plot(s4,label='mitotic',color='k',lw=0.5)\n",
    "ax2.plot(s5, label = 'ctrl G1',color='blue',lw=0.5)\n",
    "ax2.plot(s6,label= 'Δ RanGAP1',color='red',lw=0.5)\n",
    "\n",
    "ax3.plot(s7,label='mitotic',color='k',lw=0.5)\n",
    "ax3.plot(s8, label = 'ctrl G1',color='blue',lw=0.5)\n",
    "ax3.plot(s9,label= 'Δ RanGAP1',color='red',lw=0.5)\n",
    "\n",
    "for ax in ax1,ax2,ax3:\n",
    "    #ax.set_yscale('log')\n",
    "    #ax.set_ylim(1,5.5)\n",
    "    ax.set_xlim(0,100)\n",
    "    ax.set_xticks([0,25,50,75,100])\n",
    "    ax.set_xticklabels([-500,-250,0,250,500],visible=True,rotation=90,ha='center')\n",
    "    ax.xaxis.set_ticks_position(\"bottom\")\n",
    "    ax.grid(axis='x',color='grey', linewidth=0.5 , ls=':')\n",
    "    ax.set_xlabel('distance (bp)')  \n",
    "    \n",
    "ax1.set_ylabel('TSS') \n",
    "ax1.set_title('all')\n",
    "ax2.set_title('sub-nucleosome')\n",
    "ax3.set_title('>= mono-nucleosome')\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "ax.legend(bbox_to_anchor=(1.75, 1.025))\n",
    "\n",
    "#plt.savefig('.pdf',dpi=300,bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8083b8f-2961-4d6d-8b87-260ca0719dd5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}