Coverage histogram from xmap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read xmap file into dataframe and trasfer relevant arguments to bedfile\n",
    "xmap=pd.read_csv(r\"C:\\Users\\Assaf\\Documents\\postdoctoral TAU\\XMAPFILE.xmap\", sep='\\t', header=None, skiprows=8)\n",
    "xmap.columns = xmap.iloc[0]\n",
    "xmap.head(10)\n",
    "xmap.dtypes\n",
    "xmapr=xmap.iloc[2:]\n",
    "xmapr.head()\n",
    "bedtable=xmapr[['RefContigID','RefStartPos', 'RefEndPos','Orientation']]\n",
    "bedtable['RefContigID']=(\"chr\"+ bedtable['RefContigID'])\n",
    "bedtable['RefStartPos']=bedtable['RefStartPos'].astype(float)\n",
    "bedtable['RefEndPos']=bedtable['RefEndPos'].astype(float)\n",
    "bedtable['RefEndPos']=bedtable['RefEndPos'].astype(int)\n",
    "bedtable['RefStartPos']=bedtable['RefStartPos'].astype(int)\n",
    "bedtable['RefContigID']=bedtable['RefContigID'].replace({'chr23':'chrX', 'chr24':'chrY' })\n",
    "bedtable.to_csv(r\"C:\\Users\\Assaf\\Documents\\postdoctoral TAU\\xmap3.bed\", index=False, header=False, sep='\\t')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Group bedgrapph by coverage values to generate histogram \n",
    "his=pd.read_csv(r\"C:\\Users\\Assaf\\Documents\\postdoctoral TAU\\xmap.bedgraph\",names=[\"chr\", \"Start\", \"End\", \"Coverage\"], sep='\\t')\n",
    "his['lenght']=(his['End']-his['Start'])\n",
    "f=his.groupby(['Coverage'])['lenght'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot number of bp per coverage value histogram\n",
    "f.plot(kind='bar', figsize=(15, 10))\n",
    "\n",
    "plt.xlabel('Coverage', fontsize=15, labelpad=15) # add to x-label to the plot\n",
    "plt.ylabel('BPs', fontsize=15, labelpad=15) # add y-label to the plot\n",
    "plt.rc('xtick', labelsize=15)\n",
    "plt.title('Coverage histograme', fontsize=20) # add title to the plot\n",
    "plt.yscale('log')\n",
    "#plt.show()\n",
    "plt.savefig(r\"C:\\Users\\Assaf\\Documents\\postdoctoral TAU\\xmapfig.jpg\")"
   ]
  },
  {
   "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",
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