{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### compare alignment statisictics with TCGA\n",
    "\n",
    "The purpose of this notebook is to show that my fast SNP extraction pipeline didn't screw up the counts too much, by showing the high correlation of read counts over all genomic position between TCGA pipeline and my pipeline for one sample 2b0048e0-a062-40d2-a1e1-4bb763ea0ead at base resolution, it is an exome file with the most reads in TCGA LGG cohort. The reason why I picked it is just because it got a lot of reads, the first cherry in my eyes. \n",
    "\n",
    "The data contain patients allelic fraction information, to ensure hippa compliance, data is not provided in here. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfor TCGA alginment, filter down to only sites that overlahps \\n'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "for TCGA alginment, filter down to only sites that overlaps \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "inMySnpDir='./2b0048e0-a062-40d2-a1e1-4bb763ea0ead.snp.txt.gz'\n",
    "#count directly from TCGA data\n",
    "\"\"\"\n",
    "the following file contain all the sites from TCGA bam \n",
    "\"\"\"\n",
    "tcgaSnpDir='./2b0048e0-a062-40d2-a1e1-4bb763ea0ead.tcga.txt.gz'\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import gzip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r-- 1 btsui users 3.4G Jan 24 11:33 ./2b0048e0-a062-40d2-a1e1-4bb763ea0ead.tcga.txt.gz\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lah ./2b0048e0-a062-40d2-a1e1-4bb763ea0ead.tcga.txt.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!mkdir /nrnb/users/btsui/Data/tcga_extracted_lgg_snp_from_aligned_tcga_bam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "f=gzip.open(inMySnpDir,'r')\n",
    "myList=[]\n",
    "for l in f:\n",
    "    splitL=l.split('\\t')\n",
    "    myList.append(splitL[0]+'-'+splitL[1]) \n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mySnpSites=set(myList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "overlapTcgaDir='./tmp.tcga.txt.gz'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### find tcga overlapping sites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "inf=gzip.open(tcgaSnpDir,'r')\n",
    "outf=gzip.open(overlapTcgaDir,'w')\n",
    "\n",
    "#with open(tcgaSnpDir)as f:\n",
    "for i,l in enumerate(inf):\n",
    "    splitL=l.split('\\t')\n",
    "    name=splitL[0].replace('chr','')+'-'+splitL[1]\n",
    "    if name in mySnpSites:\n",
    "        splitL[0]=splitL[0].replace('chr','')\n",
    "        outf.write(\"\\t\".join(splitL))\n",
    "    if (i%(10**9))==0:\n",
    "        print i\n",
    "inf.close()\n",
    "outf.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot overlaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fname=inSrrDir+inSrr+'.txt.snp.gz'\n",
    "def readBamRead(fname):\n",
    "    tmpDf_all=pd.read_csv(fname,sep='\\s+',header=None,names=np.arange(50),index_col=None,error_bad_lines=False)\n",
    "    myCols=['Chr','Pos','Ref','rd_all','','A','C','G','T','N']\n",
    "    tmpDf=tmpDf_all.iloc[:,:len(myCols)]\n",
    "    tmpDf.columns=myCols\n",
    "    tmpDf2=tmpDf.set_index(['Chr','Pos'])\n",
    "    myBases=['A','C','G','T']\n",
    "    myL=[]\n",
    "    for base in myBases:\n",
    "            splitL=tmpDf2[base].str.split(':',expand=True)\n",
    "            ### extract  the read count and base quality\n",
    "            tmpDf5=splitL[[1,3]].astype(np.float)\n",
    "            tmpDf5.columns=['ReadDepth','AverageBaseQuality']\n",
    "            myL.append(tmpDf5)\n",
    "    tmpDf6=pd.concat(myL,keys=myBases,axis=0,names=['base'])\n",
    "    tmpDf6.columns.name='features'\n",
    "    mergedDf=tmpDf6.astype(np.uint16)\n",
    "    non_zero_df=mergedDf[mergedDf['ReadDepth']>0]\n",
    "    return non_zero_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcgaDf=readBamRead(overlapTcgaDir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "myDf=readBamRead(inMySnpDir)\n",
    "### drop identical duplicates\n",
    "myDf=myDf.groupby(['base','Chr','Pos']).first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "### compare the two file. \n",
    "ctrl_label='TCGA aligned: Read count per (base, position)'\n",
    "case_label='My pipeline aligned: Read count per (base, position)'\n",
    "myMergedStatDf=pd.concat([tcgaDf['ReadDepth'],myDf['ReadDepth']],axis=1,keys=[case_label,ctrl_label])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### check correlation of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "inPlotDf=np.log2(myMergedStatDf.dropna()+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All alleles (n=270987)\n"
     ]
    }
   ],
   "source": [
    "print 'All alleles (n='+str(inPlotDf.shape[0])+')'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RfopP5ku+6XNqDhpJiYhIbilIiYg0SKHQ6B7kn4KUiEidtbTAyJEwalT/f3fevFOGVRq6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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb7dd005510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.jointplot(data=inPlotDf,x=ctrl_label,y=case_label,kind='hex',xlim=[0,10],ylim=[0,10])\n",
    "g.savefig('./TCGA_compare.pdf')\n",
    "g.savefig('./TCGA_compare.png',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: Counts of the ACGT are highly similar between my simplified pipeline as compared to TCGA. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot alternative alleles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### identify reference allelels\n",
    "tmpDf_all=pd.read_csv('./2b0048e0-a062-40d2-a1e1-4bb763ea0ead.snp.txt.gz',\n",
    "                      sep='\\s+',header=None,names=np.arange(50),index_col=None,error_bad_lines=False)\n",
    "myCols=['Chr','Pos','Ref','rd_all','','A','C','G','T','N']\n",
    "tmpDf=tmpDf_all.iloc[:,:len(myCols)]\n",
    "tmpDf.columns=myCols\n",
    "myReindexedDf=tmpDf.set_index(['Ref','Chr','Pos'])\n",
    "refIndex=myReindexedDf.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "##  extract non reference alleles\n",
    "altAllelDf=inPlotDf[~inPlotDf.index.isin(refIndex)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "m1=altAllelDf[ctrl_label]>1.0\n",
    "inPlotDf2=altAllelDf[m1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alternative alleles only (n=31370)\n"
     ]
    }
   ],
   "source": [
    "print 'Alternative alleles only (n='+str(inPlotDf2.shape[0])+')'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Am4EN8UZ3f61qvRKR1JLlNzZvhjVrwmTdmFlu4m7yNb298NprYfIuhHlNbW25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\nM7PO+gOB2FT1AKUhfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb7de8e67d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.jointplot(data=inPlotDf2,x=ctrl_label,y=case_label,kind='hex',xlim=[0,10],ylim=[0,10])\n",
    "g.savefig('./TCGA_compare.alternative_allele.pdf')\n",
    "g.savefig('./TCGA_compare.alternative_allele.png',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: \n",
    "Some people might wonder \n",
    "Counts of the ACGT are highly similar between my simplified pipeline as compared to TCGA even among just the reference alleles. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}