(I) Replicate and Longitudinal SNP comparison to find optimal AF change threshold% for in-host SNPs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This notebook was created to find the optimal $\\Delta AF$ threshold for SNPs between paired samples using the *Replicate* Samples to approximate the noise in allele frequency calculations between samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import vcf\n",
    "\n",
    "%matplotlib inline\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.ticker as ticker\n",
    "from pylab import plot, show, savefig, xlim, figure, hold, ylim, legend, boxplot, setp, axes\n",
    "from itertools import compress\n",
    "from pylab import MaxNLocator\n",
    "import seaborn as sns; sns.set()\n",
    "from matplotlib.colors import LogNorm\n",
    "from matplotlib import gridspec\n",
    "import ast\n",
    "import itertools\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import fastcluster\n",
    "from sklearn import cluster, datasets\n",
    "import scipy.cluster.hierarchy as hier\n",
    "from sklearn.cluster import KMeans\n",
    "import time\n",
    "import sys\n",
    "\n",
    "import Bio\n",
    "from Bio.Alphabet import IUPAC\n",
    "from Bio.Blast.Applications import NcbiblastnCommandline\n",
    "from Bio.Blast import NCBIXML\n",
    "from Bio.Seq import Seq\n",
    "from Bio.SeqRecord import SeqRecord\n",
    "from Bio.SeqFeature import SeqFeature, FeatureLocation\n",
    "from Bio import pairwise2\n",
    "from Bio import SeqIO\n",
    "from Bio.Graphics import GenomeDiagram\n",
    "from Bio.SeqUtils import GC\n",
    "\n",
    "from Bio.Align.Applications import MuscleCommandline\n",
    "from StringIO import StringIO\n",
    "from Bio import AlignIO\n",
    "from Bio.Align import AlignInfo\n",
    "from Bio.Seq import MutableSeq\n",
    "import itertools\n",
    "\n",
    "import networkx as nx\n",
    "import scipy\n",
    "from collections import Counter\n",
    "from itertools import compress\n",
    "\n",
    "#for exporting to Adobe Illustrator\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "mpl.rcParams['ps.fonttype'] = 42\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", module=\"matplotlib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')\n",
    "plt.rcParams['lines.linewidth']=2\n",
    "plt.rcParams['axes.facecolor']='1.0'\n",
    "plt.rcParams['xtick.color']='black'\n",
    "plt.rcParams['axes.grid']=True\n",
    "plt.rcParams['axes.edgecolor']='black'\n",
    "plt.rcParams['grid.color']= '1.0'\n",
    "plt.rcParams.update({'font.size': 12})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import sample annotation for filtered Replicate & Longitudinal pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "replicate_sample_annotation = pd.read_csv('/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/CSV_files/sample_annotation_files/Replicate_fastq_path_names_and_JankyPipe_tags_filtered_final.csv' , sep = ',')\n",
    "longitudinal_sample_annotation = pd.read_csv('/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/CSV_files/sample_annotation_files/Longitudinal_fastq_path_names_and_JankyPipe_tags_filtered_final.csv' , sep = ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>patient_id</th>\n",
       "      <th>fastq_files</th>\n",
       "      <th>population</th>\n",
       "      <th>run_ID</th>\n",
       "      <th>sample_ID</th>\n",
       "      <th>sample_order</th>\n",
       "      <th>tag</th>\n",
       "      <th>isolate_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>replicate_pair_18</td>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>MMYK00000000</td>\n",
       "      <td>Peru4938</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru4938</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>replicate_pair_18</td>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/pools/01-R07...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-R0774</td>\n",
       "      <td>0</td>\n",
       "      <td>01-R0774</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          patient_id                                        fastq_files  \\\n",
       "0  replicate_pair_18  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "1  replicate_pair_18  /n/data1/hms/dbmi/farhat/fastq_db/pools/01-R07...   \n",
       "\n",
       "  population        run_ID sample_ID  sample_order       tag isolate_type  \n",
       "0     CP_REP  MMYK00000000  Peru4938             0  Peru4938    replicate  \n",
       "1     CP_REP           NaN  01-R0774             0  01-R0774    replicate  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "replicate_sample_annotation.head(n=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>fastq_files</th>\n",
       "      <th>population</th>\n",
       "      <th>run_ID</th>\n",
       "      <th>sample_ID</th>\n",
       "      <th>sample_order</th>\n",
       "      <th>tag</th>\n",
       "      <th>isolate_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P000183</td>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/walker/ERR03...</td>\n",
       "      <td>WALKER</td>\n",
       "      <td>ERR039337</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR039337</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P000183</td>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/walker/ERR03...</td>\n",
       "      <td>WALKER</td>\n",
       "      <td>ERR039338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR039338</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  patient_id                                        fastq_files population  \\\n",
       "0    P000183  /n/data1/hms/dbmi/farhat/fastq_db/walker/ERR03...     WALKER   \n",
       "1    P000183  /n/data1/hms/dbmi/farhat/fastq_db/walker/ERR03...     WALKER   \n",
       "\n",
       "      run_ID sample_ID  sample_order        tag  isolate_type  \n",
       "0  ERR039337       NaN             1  ERR039337  longitudinal  \n",
       "1  ERR039338       NaN             2  ERR039338  longitudinal  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longitudinal_sample_annotation.head(n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Import SNPs (with $\\Delta AF > 25 \\% $) for Replicate Sample and Longitudinal Sample Pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "replicate_pair_SNPs = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/pickled_files/variant_calling/replicate_SNPs/SNPs_between_isolates_delta_25.pkl')\n",
    "longitudinal_pair_SNPs = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/pickled_files/variant_calling/longitudinal_SNPs/SNPs_between_isolates_delta_25.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>population</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>ref_position</th>\n",
       "      <th>ref_allele</th>\n",
       "      <th>alt_allele</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>genomic_coord</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>alt_AF_diff</th>\n",
       "      <th>SNP_type</th>\n",
       "      <th>AA_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_68</td>\n",
       "      <td>1096600</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv0980c_Rv0981</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.27</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_68</td>\n",
       "      <td>2334234</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv2077c</td>\n",
       "      <td>61</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.72</td>\n",
       "      <td>N</td>\n",
       "      <td>G21C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_68</td>\n",
       "      <td>2372550</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>Rv2112c</td>\n",
       "      <td>20</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.27</td>\n",
       "      <td>N</td>\n",
       "      <td>P7R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_68</td>\n",
       "      <td>3244804</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv2930</td>\n",
       "      <td>1108</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>fadD26</td>\n",
       "      <td>0.66</td>\n",
       "      <td>N</td>\n",
       "      <td>S370A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_68</td>\n",
       "      <td>3736179</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv3343c</td>\n",
       "      <td>757</td>\n",
       "      <td>PE/PPE</td>\n",
       "      <td>PPE54</td>\n",
       "      <td>0.69</td>\n",
       "      <td>S</td>\n",
       "      <td>L253L</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  population         patient_id  ref_position ref_allele alt_allele  \\\n",
       "0     CP_REP  replicate_pair_68       1096600          A          G   \n",
       "1     CP_REP  replicate_pair_68       2334234          C          A   \n",
       "2     CP_REP  replicate_pair_68       2372550          G          C   \n",
       "3     CP_REP  replicate_pair_68       3244804          T          G   \n",
       "4     CP_REP  replicate_pair_68       3736179          G          A   \n",
       "\n",
       "          gene_id genomic_coord  gene_category gene_symbol  alt_AF_diff  \\\n",
       "0  Rv0980c_Rv0981          None           None        None         0.27   \n",
       "1         Rv2077c            61  Non-Essential         NaN         0.72   \n",
       "2         Rv2112c            20  Non-Essential         NaN         0.27   \n",
       "3          Rv2930          1108  Non-Essential      fadD26         0.66   \n",
       "4         Rv3343c           757         PE/PPE       PPE54         0.69   \n",
       "\n",
       "  SNP_type AA_change  \n",
       "0        I      None  \n",
       "1        N      G21C  \n",
       "2        N       P7R  \n",
       "3        N     S370A  \n",
       "4        S     L253L  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "replicate_pair_SNPs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>ref_position</th>\n",
       "      <th>ref_allele</th>\n",
       "      <th>alt_allele</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>genomic_coord</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>alt_AF_diff</th>\n",
       "      <th>SNP_type</th>\n",
       "      <th>AA_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CASALI</td>\n",
       "      <td>P251</td>\n",
       "      <td>761139</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv0667</td>\n",
       "      <td>1333</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>rpoB</td>\n",
       "      <td>1.00</td>\n",
       "      <td>N</td>\n",
       "      <td>H445D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CASALI</td>\n",
       "      <td>P251</td>\n",
       "      <td>861378</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv0768</td>\n",
       "      <td>467</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>aldA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>N</td>\n",
       "      <td>I156S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CASALI</td>\n",
       "      <td>P251</td>\n",
       "      <td>4402310</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv3913</td>\n",
       "      <td>583</td>\n",
       "      <td>Essential</td>\n",
       "      <td>trxB2</td>\n",
       "      <td>0.48</td>\n",
       "      <td>N</td>\n",
       "      <td>I195V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CETR</td>\n",
       "      <td>2968</td>\n",
       "      <td>656329</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>Rv0565c</td>\n",
       "      <td>1142</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.50</td>\n",
       "      <td>N</td>\n",
       "      <td>V381G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CETR</td>\n",
       "      <td>2968</td>\n",
       "      <td>656956</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Rv0565c</td>\n",
       "      <td>515</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.34</td>\n",
       "      <td>N</td>\n",
       "      <td>W172*</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  population patient_id  ref_position ref_allele alt_allele  gene_id  \\\n",
       "0     CASALI       P251        761139          C          G   Rv0667   \n",
       "1     CASALI       P251        861378          T          G   Rv0768   \n",
       "2     CASALI       P251       4402310          A          G   Rv3913   \n",
       "3       CETR       2968        656329          A          C  Rv0565c   \n",
       "4       CETR       2968        656956          C          T  Rv0565c   \n",
       "\n",
       "  genomic_coord          gene_category gene_symbol  alt_AF_diff SNP_type  \\\n",
       "0          1333  Antibiotic Resistance        rpoB         1.00        N   \n",
       "1           467          Non-Essential        aldA         1.00        N   \n",
       "2           583              Essential       trxB2         0.48        N   \n",
       "3          1142          Non-Essential         NaN         0.50        N   \n",
       "4           515          Non-Essential         NaN         0.34        N   \n",
       "\n",
       "  AA_change  \n",
       "0     H445D  \n",
       "1     I156S  \n",
       "2     I195V  \n",
       "3     V381G  \n",
       "4     W172*  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longitudinal_pair_SNPs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len( set(replicate_sample_annotation.patient_id) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop any glpK mutants that occur between any replicate pairs and longitudinal pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### glpK mutants present in replicate pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93, 12)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(replicate_pair_SNPs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>ref_position</th>\n",
       "      <th>ref_allele</th>\n",
       "      <th>alt_allele</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>genomic_coord</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>alt_AF_diff</th>\n",
       "      <th>SNP_type</th>\n",
       "      <th>AA_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_68</td>\n",
       "      <td>4138398</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv3696c</td>\n",
       "      <td>1358</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>glpK</td>\n",
       "      <td>0.81</td>\n",
       "      <td>N</td>\n",
       "      <td>A453V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_11</td>\n",
       "      <td>4139083</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv3696c</td>\n",
       "      <td>673</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>glpK</td>\n",
       "      <td>0.38</td>\n",
       "      <td>N</td>\n",
       "      <td>R225W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_32</td>\n",
       "      <td>4138326</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv3696c</td>\n",
       "      <td>1430</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>glpK</td>\n",
       "      <td>0.47</td>\n",
       "      <td>N</td>\n",
       "      <td>L477P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>TR_REP</td>\n",
       "      <td>P12_3</td>\n",
       "      <td>4138995</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv3696c</td>\n",
       "      <td>761</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>glpK</td>\n",
       "      <td>0.29</td>\n",
       "      <td>N</td>\n",
       "      <td>P254L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>TR_REP</td>\n",
       "      <td>P12_2</td>\n",
       "      <td>4138995</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv3696c</td>\n",
       "      <td>761</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>glpK</td>\n",
       "      <td>0.29</td>\n",
       "      <td>N</td>\n",
       "      <td>P254L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_30</td>\n",
       "      <td>4139424</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv3696c</td>\n",
       "      <td>332</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>glpK</td>\n",
       "      <td>0.28</td>\n",
       "      <td>N</td>\n",
       "      <td>A111V</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   population         patient_id  ref_position ref_allele alt_allele  gene_id  \\\n",
       "5      CP_REP  replicate_pair_68       4138398          G          A  Rv3696c   \n",
       "11     CP_REP  replicate_pair_11       4139083          G          A  Rv3696c   \n",
       "17     CP_REP  replicate_pair_32       4138326          A          G  Rv3696c   \n",
       "21     TR_REP              P12_3       4138995          G          A  Rv3696c   \n",
       "22     TR_REP              P12_2       4138995          G          A  Rv3696c   \n",
       "86     CP_REP  replicate_pair_30       4139424          G          A  Rv3696c   \n",
       "\n",
       "   genomic_coord  gene_category gene_symbol  alt_AF_diff SNP_type AA_change  \n",
       "5           1358  Non-Essential        glpK         0.81        N     A453V  \n",
       "11           673  Non-Essential        glpK         0.38        N     R225W  \n",
       "17          1430  Non-Essential        glpK         0.47        N     L477P  \n",
       "21           761  Non-Essential        glpK         0.29        N     P254L  \n",
       "22           761  Non-Essential        glpK         0.29        N     P254L  \n",
       "86           332  Non-Essential        glpK         0.28        N     A111V  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "replicate_pair_SNPs[replicate_pair_SNPs.gene_symbol == 'glpK']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float( len(set(replicate_sample_annotation.patient_id)) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There were 5 *glpK* mutants (the 2 mutants from the Trauner et. al. dataset count as one since there were 3 isolates compared pair-wise) that were found among 62 replicate pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop *glpK* mutants from the rest of the analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "replicate_pair_SNPs = replicate_pair_SNPs[replicate_pair_SNPs.gene_symbol != 'glpK']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87, 12)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(replicate_pair_SNPs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### glpK mutants present in longitudinal pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(longitudinal_pair_SNPs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>ref_position</th>\n",
       "      <th>ref_allele</th>\n",
       "      <th>alt_allele</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>genomic_coord</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>alt_AF_diff</th>\n",
       "      <th>SNP_type</th>\n",
       "      <th>AA_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>CETR</td>\n",
       "      <td>2321</td>\n",
       "      <td>4138645</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv3696c</td>\n",
       "      <td>1111</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>glpK</td>\n",
       "      <td>0.93</td>\n",
       "      <td>N</td>\n",
       "      <td>P371S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1148</th>\n",
       "      <td>CETR</td>\n",
       "      <td>2026</td>\n",
       "      <td>4138398</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv3696c</td>\n",
       "      <td>1358</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>glpK</td>\n",
       "      <td>0.79</td>\n",
       "      <td>N</td>\n",
       "      <td>A453V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1511</th>\n",
       "      <td>CETR</td>\n",
       "      <td>1972</td>\n",
       "      <td>4138599</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv3696c</td>\n",
       "      <td>1157</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>glpK</td>\n",
       "      <td>0.43</td>\n",
       "      <td>N</td>\n",
       "      <td>R386L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1512</th>\n",
       "      <td>CETR</td>\n",
       "      <td>1972</td>\n",
       "      <td>4138945</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv3696c</td>\n",
       "      <td>811</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>glpK</td>\n",
       "      <td>0.41</td>\n",
       "      <td>N</td>\n",
       "      <td>C271R</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     population patient_id  ref_position ref_allele alt_allele  gene_id  \\\n",
       "124        CETR       2321       4138645          G          A  Rv3696c   \n",
       "1148       CETR       2026       4138398          G          A  Rv3696c   \n",
       "1511       CETR       1972       4138599          C          A  Rv3696c   \n",
       "1512       CETR       1972       4138945          A          G  Rv3696c   \n",
       "\n",
       "     genomic_coord  gene_category gene_symbol  alt_AF_diff SNP_type AA_change  \n",
       "124           1111  Non-Essential        glpK         0.93        N     P371S  \n",
       "1148          1358  Non-Essential        glpK         0.79        N     A453V  \n",
       "1511          1157  Non-Essential        glpK         0.43        N     R386L  \n",
       "1512           811  Non-Essential        glpK         0.41        N     C271R  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longitudinal_pair_SNPs[longitudinal_pair_SNPs.gene_symbol == 'glpK']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float( len(set(longitudinal_sample_annotation.patient_id)) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There were 4 *glpK* mutants that were found among 200 longitudinal pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop *glpK* mutants from the rest of the analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "longitudinal_pair_SNPs = longitudinal_pair_SNPs[longitudinal_pair_SNPs.gene_symbol != 'glpK']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1595, 12)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(longitudinal_pair_SNPs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split SNPs up into different types of replicate pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SNPs between CETR-POOLS replicate pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cetr_pools_replicates = replicate_pair_SNPs[replicate_pair_SNPs.population == 'CP_REP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87, 12)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(cetr_pools_replicates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SNPs between Re-Sequenced (due to low coverage) replicate pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "low_cov_reseq_replicates = replicate_pair_SNPs[replicate_pair_SNPs.population == 'LC_REP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 12)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(low_cov_reseq_replicates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SNPs between Trauner Patient 12 sputum sample replicate pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "trauner_P12_replicates = replicate_pair_SNPs[replicate_pair_SNPs.population == 'TR_REP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 12)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(trauner_P12_replicates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replicate Sample Pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax = plt.subplots()\n",
    "\n",
    "bins = np.arange( np.min(replicate_pair_SNPs.alt_AF_diff) , np.max(replicate_pair_SNPs.alt_AF_diff) + 0.01 , 0.01)\n",
    "n , bins , patches = plt.hist(replicate_pair_SNPs.alt_AF_diff , bins = bins , color = 'k' , rwidth = 1.0 , edgecolor='white', linewidth=1.25)\n",
    "\n",
    "plt.title('All Filtered Replicate Pairs', fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "plt.xlabel('$\\Delta$ AF', fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "plt.ylabel('Number of SNPs', fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "ax.tick_params(labelcolor = 'k')\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10.0, 5.5)\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.tick_params(axis='y', which='major', labelsize=12 , labelcolor = 'k')\n",
    "plt.tick_params(axis='x', which='major', labelsize=12 , labelcolor = 'k')\n",
    "\n",
    "ax.set_xlim(0.24 , 1.01)\n",
    "\n",
    "file_name = '/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/figures/replicate_vs_longitudinal_analysis/distribution_of_delta_AFs_for_all_replicate_SNPs.pdf'\n",
    "plt.savefig(file_name, bbox_inches='tight', dpi = 300 , transparent = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Longitudinal Pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax = plt.subplots()\n",
    "\n",
    "bins = np.arange( np.min(longitudinal_pair_SNPs.alt_AF_diff) , np.max(longitudinal_pair_SNPs.alt_AF_diff) + 0.01 , 0.01)\n",
    "n , bins , patches = plt.hist(longitudinal_pair_SNPs.alt_AF_diff , bins = bins , color = 'k' , rwidth = 1.0 , edgecolor='white', linewidth=1.25)\n",
    "\n",
    "plt.title('All Filtered Longitudinal Pairs', fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "plt.xlabel('$\\Delta$ AF', fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "plt.ylabel('Number of SNPs', fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "ax.tick_params(labelcolor = 'k')\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10.0, 5.5)\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.tick_params(axis='y', which='major', labelsize=12 , labelcolor = 'k')\n",
    "plt.tick_params(axis='x', which='major', labelsize=12 , labelcolor = 'k')\n",
    "\n",
    "ax.set_xlim(0.24 , 1.01)\n",
    "\n",
    "file_name = '/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/figures/replicate_vs_longitudinal_analysis/distribution_of_delta_AFs_for_all_longitudinal_SNPs.pdf'\n",
    "plt.savefig(file_name, bbox_inches='tight', dpi = 300 , transparent = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Probe into the SNPs from the different types of replicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Low Coverage Samples that were Re-Sequenced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Replicate Pairs = 0\n"
     ]
    }
   ],
   "source": [
    "print 'Number of Replicate Pairs = ' + str( len(set(low_cov_reseq_replicates.patient_id)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series( Counter( list( replicate_pair_SNPs[replicate_pair_SNPs.population == 'LC_REP'].patient_id) ) ).sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CETR - POOLS replicate pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Replicate Pairs = 34\n"
     ]
    }
   ],
   "source": [
    "print 'Number of Replicate Pairs = ' + str( len(set(cetr_pools_replicates.patient_id)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax = plt.subplots()\n",
    "\n",
    "n , bins , patches = plt.hist(replicate_pair_SNPs[replicate_pair_SNPs.population == 'CP_REP'].alt_AF_diff , bins = 58 , color = 'k' , rwidth = 1.0 , edgecolor='white', linewidth=0.5)\n",
    "\n",
    "plt.title('CETR-POOLS Replicate Pairs', fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "plt.xlabel('$\\Delta$ AF', fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "plt.ylabel('Number of SNPs', fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "ax.tick_params(labelcolor = 'k')\n",
    "#ax.set_yscale(\"log\", nonposy='clip')\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10.0, 5.5)\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "replicate_pair_0     18\n",
       "replicate_pair_27    12\n",
       "replicate_pair_68     5\n",
       "replicate_pair_11     4\n",
       "replicate_pair_10     3\n",
       "replicate_pair_12     3\n",
       "replicate_pair_61     3\n",
       "replicate_pair_59     3\n",
       "replicate_pair_29     2\n",
       "replicate_pair_13     2\n",
       "replicate_pair_14     2\n",
       "replicate_pair_16     2\n",
       "replicate_pair_18     2\n",
       "replicate_pair_2      2\n",
       "replicate_pair_24     2\n",
       "replicate_pair_4      2\n",
       "replicate_pair_71     2\n",
       "replicate_pair_50     2\n",
       "replicate_pair_40     1\n",
       "replicate_pair_15     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series( Counter( list( replicate_pair_SNPs[replicate_pair_SNPs.population == 'CP_REP'].patient_id) ) ).sort_values(ascending = False).head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Allele Frequence Changes for SNPs from low-coverage replicate pairs\n",
    "SNP_AF_deltas = replicate_pair_SNPs[replicate_pair_SNPs.population == 'CP_REP'].alt_AF_diff.values\n",
    "\n",
    "#create array of different Allele Frequency change thresholds\n",
    "AF_delta_thresholds = np.arange(0.25 , 1.0 , 0.01)\n",
    "\n",
    "#store the number of SNPs that pass the threshold\n",
    "SNPs_passed_threshold = []\n",
    "\n",
    "for delta_thershold in AF_delta_thresholds:\n",
    "    \n",
    "    SNPs_passed_threshold.append( float( sum(SNP_AF_deltas >= delta_thershold) ) )\n",
    "    \n",
    "#plot threshold vs. number of SNPs that pass threshold\n",
    "fig , ax = plt.subplots()\n",
    "\n",
    "plt.plot( AF_delta_thresholds , SNPs_passed_threshold , color = 'black' , linewidth=2.0 )\n",
    "\n",
    "plt.title('CETR-POOLS Replicate Pairs', fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "plt.xlabel('$\\Delta$ AF Threshold', fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "plt.ylabel('Number of SNPs', fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "ax.tick_params(labelcolor = 'k')\n",
    "plt.tick_params(axis='y', which='major', labelsize=12 , labelcolor = 'k')\n",
    "plt.tick_params(axis='x', which='major', labelsize=12 , labelcolor = 'k')\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10.0, 5.5)\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>ref_position</th>\n",
       "      <th>ref_allele</th>\n",
       "      <th>alt_allele</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>genomic_coord</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>alt_AF_diff</th>\n",
       "      <th>SNP_type</th>\n",
       "      <th>AA_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_68</td>\n",
       "      <td>2334234</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv2077c</td>\n",
       "      <td>61</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.72</td>\n",
       "      <td>N</td>\n",
       "      <td>G21C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_24</td>\n",
       "      <td>2396877</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv2136c_Rv2137c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.70</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_13</td>\n",
       "      <td>3820446</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>Rv3401_Rv3402c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.70</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_13</td>\n",
       "      <td>3820449</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv3401_Rv3402c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.69</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_68</td>\n",
       "      <td>3736179</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv3343c</td>\n",
       "      <td>757</td>\n",
       "      <td>PE/PPE</td>\n",
       "      <td>PPE54</td>\n",
       "      <td>0.69</td>\n",
       "      <td>S</td>\n",
       "      <td>L253L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_11</td>\n",
       "      <td>4316195</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>Rv3843c</td>\n",
       "      <td>402</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.66</td>\n",
       "      <td>N</td>\n",
       "      <td>F134L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_68</td>\n",
       "      <td>3244804</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv2930</td>\n",
       "      <td>1108</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>fadD26</td>\n",
       "      <td>0.66</td>\n",
       "      <td>N</td>\n",
       "      <td>S370A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_12</td>\n",
       "      <td>3819146</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv3401</td>\n",
       "      <td>1105</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.63</td>\n",
       "      <td>N</td>\n",
       "      <td>P369T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_4</td>\n",
       "      <td>761004</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv0667</td>\n",
       "      <td>1198</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>rpoB</td>\n",
       "      <td>0.62</td>\n",
       "      <td>N</td>\n",
       "      <td>T400A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_12</td>\n",
       "      <td>292334</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Rv0243</td>\n",
       "      <td>164</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>fadA2</td>\n",
       "      <td>0.59</td>\n",
       "      <td>N</td>\n",
       "      <td>A55V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_12</td>\n",
       "      <td>1034894</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>Rv0927c_Rv0928</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.58</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_11</td>\n",
       "      <td>994196</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv0892</td>\n",
       "      <td>344</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.51</td>\n",
       "      <td>N</td>\n",
       "      <td>W115*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_11</td>\n",
       "      <td>101351</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv0092</td>\n",
       "      <td>769</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>ctpA</td>\n",
       "      <td>0.49</td>\n",
       "      <td>N</td>\n",
       "      <td>V257I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_50</td>\n",
       "      <td>1096600</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv0980c_Rv0981</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.47</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_27</td>\n",
       "      <td>2288821</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv2043c</td>\n",
       "      <td>421</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>pncA</td>\n",
       "      <td>0.46</td>\n",
       "      <td>N</td>\n",
       "      <td>Q141*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_47</td>\n",
       "      <td>1472159</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>Rvnr01</td>\n",
       "      <td>314</td>\n",
       "      <td>None</td>\n",
       "      <td>rrs</td>\n",
       "      <td>0.44</td>\n",
       "      <td>N</td>\n",
       "      <td>H105P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_27</td>\n",
       "      <td>6738</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv0005</td>\n",
       "      <td>1499</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>gyrB</td>\n",
       "      <td>0.44</td>\n",
       "      <td>N</td>\n",
       "      <td>T500N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_27</td>\n",
       "      <td>637214</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Rv0545c</td>\n",
       "      <td>250</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>pitA</td>\n",
       "      <td>0.44</td>\n",
       "      <td>N</td>\n",
       "      <td>A84T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_27</td>\n",
       "      <td>3091654</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv2783c</td>\n",
       "      <td>944</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>gpsI</td>\n",
       "      <td>0.43</td>\n",
       "      <td>N</td>\n",
       "      <td>D315A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_27</td>\n",
       "      <td>851976</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Rv0757</td>\n",
       "      <td>369</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>phoP</td>\n",
       "      <td>0.42</td>\n",
       "      <td>S</td>\n",
       "      <td>F123F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   population         patient_id  ref_position ref_allele alt_allele  \\\n",
       "1      CP_REP  replicate_pair_68       2334234          C          A   \n",
       "90     CP_REP  replicate_pair_24       2396877          A          G   \n",
       "14     CP_REP  replicate_pair_13       3820446          G          C   \n",
       "15     CP_REP  replicate_pair_13       3820449          T          G   \n",
       "4      CP_REP  replicate_pair_68       3736179          G          A   \n",
       "13     CP_REP  replicate_pair_11       4316195          G          C   \n",
       "3      CP_REP  replicate_pair_68       3244804          T          G   \n",
       "64     CP_REP  replicate_pair_12       3819146          C          A   \n",
       "56     CP_REP   replicate_pair_4        761004          A          G   \n",
       "62     CP_REP  replicate_pair_12        292334          C          T   \n",
       "63     CP_REP  replicate_pair_12       1034894          G          T   \n",
       "10     CP_REP  replicate_pair_11        994196          G          A   \n",
       "9      CP_REP  replicate_pair_11        101351          G          A   \n",
       "24     CP_REP  replicate_pair_50       1096600          A          G   \n",
       "77     CP_REP  replicate_pair_27       2288821          G          A   \n",
       "68     CP_REP  replicate_pair_47       1472159          A          C   \n",
       "73     CP_REP  replicate_pair_27          6738          C          A   \n",
       "74     CP_REP  replicate_pair_27        637214          C          T   \n",
       "79     CP_REP  replicate_pair_27       3091654          T          G   \n",
       "76     CP_REP  replicate_pair_27        851976          C          T   \n",
       "\n",
       "            gene_id genomic_coord          gene_category gene_symbol  \\\n",
       "1           Rv2077c            61          Non-Essential         NaN   \n",
       "90  Rv2136c_Rv2137c          None                   None        None   \n",
       "14   Rv3401_Rv3402c          None                   None        None   \n",
       "15   Rv3401_Rv3402c          None                   None        None   \n",
       "4           Rv3343c           757                 PE/PPE       PPE54   \n",
       "13          Rv3843c           402          Non-Essential         NaN   \n",
       "3            Rv2930          1108          Non-Essential      fadD26   \n",
       "64           Rv3401          1105          Non-Essential         NaN   \n",
       "56           Rv0667          1198  Antibiotic Resistance        rpoB   \n",
       "62           Rv0243           164          Non-Essential       fadA2   \n",
       "63   Rv0927c_Rv0928          None                   None        None   \n",
       "10           Rv0892           344          Non-Essential         NaN   \n",
       "9            Rv0092           769          Non-Essential        ctpA   \n",
       "24   Rv0980c_Rv0981          None                   None        None   \n",
       "77          Rv2043c           421  Antibiotic Resistance        pncA   \n",
       "68           Rvnr01           314                   None         rrs   \n",
       "73           Rv0005          1499  Antibiotic Resistance        gyrB   \n",
       "74          Rv0545c           250          Non-Essential        pitA   \n",
       "79          Rv2783c           944          Non-Essential        gpsI   \n",
       "76           Rv0757           369          Non-Essential        phoP   \n",
       "\n",
       "    alt_AF_diff SNP_type AA_change  \n",
       "1          0.72        N      G21C  \n",
       "90         0.70        I      None  \n",
       "14         0.70        I      None  \n",
       "15         0.69        I      None  \n",
       "4          0.69        S     L253L  \n",
       "13         0.66        N     F134L  \n",
       "3          0.66        N     S370A  \n",
       "64         0.63        N     P369T  \n",
       "56         0.62        N     T400A  \n",
       "62         0.59        N      A55V  \n",
       "63         0.58        I      None  \n",
       "10         0.51        N     W115*  \n",
       "9          0.49        N     V257I  \n",
       "24         0.47        I      None  \n",
       "77         0.46        N     Q141*  \n",
       "68         0.44        N     H105P  \n",
       "73         0.44        N     T500N  \n",
       "74         0.44        N      A84T  \n",
       "79         0.43        N     D315A  \n",
       "76         0.42        S     F123F  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cetr_pools_replicates.sort_values(by='alt_AF_diff' , ascending = False).head(n=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trauner Patient-12 replicate pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Replicate Pairs = 0\n"
     ]
    }
   ],
   "source": [
    "print 'Number of Replicate Pairs = ' + str( len(set(trauner_P12_replicates.patient_id)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series( Counter( list( replicate_pair_SNPs[replicate_pair_SNPs.population == 'TRAUNER'].patient_id) ) ).sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Avg Number of SNPs per Replicate Pair and Longitudinal Pair"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Allele Frequence Changes for SNPs from replicate pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "SNP_AF_deltas = replicate_pair_SNPs.alt_AF_diff.values\n",
    "\n",
    "#create array of different Allele Frequency change thresholds\n",
    "AF_delta_thresholds = np.arange(0.25 , 1.00 , 0.01)\n",
    "AF_delta_thresholds = [round(threshold,2) for threshold in AF_delta_thresholds]\n",
    "\n",
    "#store the number of SNPs that pass the threshold\n",
    "SNPs_passed_threshold = []\n",
    "\n",
    "for delta_thershold in AF_delta_thresholds:\n",
    "    \n",
    "    SNPs_passed_threshold.append( float( sum(SNP_AF_deltas >= delta_thershold) ) )\n",
    "\n",
    "num_replicate_pairs = float( len(set(replicate_sample_annotation.patient_id)) )\n",
    "avg_SNPs_passed_threshold_replicate = np.array( SNPs_passed_threshold ) / num_replicate_pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Allele Frequence Changes for SNPs from longitudinal pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "SNP_AF_deltas = longitudinal_pair_SNPs.alt_AF_diff.values\n",
    "\n",
    "#create array of different Allele Frequency change thresholds\n",
    "AF_delta_thresholds = np.arange(0.25 , 1.00 , 0.01)\n",
    "AF_delta_thresholds = [round(threshold,2) for threshold in AF_delta_thresholds]\n",
    "\n",
    "#store the number of SNPs that pass the threshold\n",
    "SNPs_passed_threshold = []\n",
    "\n",
    "for delta_thershold in AF_delta_thresholds:\n",
    "    \n",
    "    SNPs_passed_threshold.append( float( sum(SNP_AF_deltas >= delta_thershold) ) )\n",
    "\n",
    "num_longitudinal_pairs = float( len(set(longitudinal_sample_annotation.patient_id)) )\n",
    "avg_SNPs_passed_threshold_longitudinal = np.array( SNPs_passed_threshold ) / num_longitudinal_pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set $\\Delta AF$ threshold ($\\Delta AF_T$) where SNPs from replicate pairs represent $\\approx 5 \\%$ of SNPs between pooled replicate and longitudinal isolate pairs, weighted by the number of pairs in each group, find $\\Delta AF_T$ such that"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\frac{ [(\\text{Number of Replicate SNPs} \\ge \\Delta AF_T) / (\\text{Number of Replicate Pairs})]}{[(\\text{Number of Replicate SNPs} \\ge \\Delta AF_T) / (\\text{Number of Replicate Pairs})] + [(\\text{Number of Longitudinal SNPs} \\ge \\Delta AF_T) / (\\text{Number of Longitudinal Pairs})]} = \\frac{[\\text{Avg Number of SNPs per Replicate Pair}]}{[\\text{Avg Number of SNPs per Replicate Pair}] + [\\text{Avg Number of SNPs per Longitudinal Pair}]} \\approx 5\\% $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot threshold vs. number of SNPs that pass threshold\n",
    "################################################################################################################################################\n",
    "fig , ax1 = plt.subplots()\n",
    "\n",
    "ax1.plot( AF_delta_thresholds , avg_SNPs_passed_threshold_replicate , color = 'xkcd:bright red' , linewidth=2.5 , label = 'Replicate')\n",
    "ax1.plot( AF_delta_thresholds , avg_SNPs_passed_threshold_longitudinal , color = 'xkcd:grass green' , linewidth=2.5 , label = 'Longitudinal' )\n",
    "\n",
    "ax1.set_xlabel('$\\Delta$ AF Threshold', fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "ax1.set_ylabel('Average Number of SNPs per Pair', fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "\n",
    "ax1.tick_params(labelcolor = 'k')\n",
    "ax1.tick_params(axis='x', which='major', labelsize=12 , labelcolor = 'k')\n",
    "ax1.tick_params(axis='y', which='major', labelsize=12 , labelcolor = 'k')\n",
    "\n",
    "ax1.set_xlim(0.24 , 1.0)\n",
    "ax1.legend(loc='right')\n",
    "################################################################################################################################################\n",
    "\n",
    "#second super imposed plot, graph the ratio of average number of longitudinal SNPs / avg number of replicate SNPs\n",
    "################################################################################################################################################\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "#for each threshold, divide the avg number of SNPs from replicate isolates OVER the avg number of SNPs from replicate isolates + the avg number of SNPs from longitudinal isolates\n",
    "weighted_proportion_SNPs_from_replicates = avg_SNPs_passed_threshold_replicate / (avg_SNPs_passed_threshold_replicate + avg_SNPs_passed_threshold_longitudinal)\n",
    "ax2.plot( AF_delta_thresholds , weighted_proportion_SNPs_from_replicates , color = 'xkcd:bright blue' , linewidth=2.5 , label = 'Weighted Proportion of Replicate SNPs')\n",
    "\n",
    "#convert weighted proportion SNPs from replicates to percentages & round to the nearest whole number\n",
    "weighted_percentage_replicate_SNPs_rounded = np.array( [int(np.round(float(x) , 0)) for x in 100*weighted_proportion_SNPs_from_replicates] )\n",
    "\n",
    "#find the delta & proportion at which weighted proportion of replicate SNPs is about 5% of weighted SNPs from replicate + longitudinal pairs\n",
    "delta_AF_threshold = list(compress(AF_delta_thresholds, list( weighted_percentage_replicate_SNPs_rounded <= 5 )))[0]\n",
    "weighted_proportion_SNPs_from_replicates_at_threshold = list(compress(weighted_proportion_SNPs_from_replicates, list( weighted_percentage_replicate_SNPs_rounded <= 5 )))[0]\n",
    "\n",
    "ax2.plot([delta_AF_threshold , delta_AF_threshold] , [0 , weighted_proportion_SNPs_from_replicates_at_threshold] , color = 'k' , linewidth = 1.5 , linestyle = '--' , alpha = 0.5)\n",
    "ax2.plot([delta_AF_threshold , 1.0] , [weighted_proportion_SNPs_from_replicates_at_threshold , weighted_proportion_SNPs_from_replicates_at_threshold] , color = 'k' , linewidth = 1.5 , linestyle = '--', alpha = 0.5)\n",
    "\n",
    "\n",
    "ax2.spines['top'].set_visible(False)\n",
    "plt.rcParams[\"axes.grid\"] = False\n",
    "\n",
    "ax2.tick_params(labelcolor = 'k')\n",
    "ax2.tick_params(axis='x', which='major', labelsize=12 , labelcolor = 'k')\n",
    "ax2.tick_params(axis='y', which='major', labelsize=12 , labelcolor = 'k')\n",
    "\n",
    "ax2.legend(loc='upper right')\n",
    "################################################################################################################################################\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10.0, 5.5)\n",
    "fig.tight_layout()\n",
    "\n",
    "file_name = '/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/figures/replicate_vs_longitudinal_analysis/avg_num_SNPs_vs_delta_AF_threshold.pdf'\n",
    "plt.savefig(file_name, bbox_inches='tight', dpi = 300 , transparent = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7\n"
     ]
    }
   ],
   "source": [
    "print delta_AF_threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05268703898840885\n"
     ]
    }
   ],
   "source": [
    "print weighted_proportion_SNPs_from_replicates_at_threshold #there are over 20x as many avg SNPs per longitudinal pair than there are avg SNPs per replicate pair"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of Replicate Pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62.0\n"
     ]
    }
   ],
   "source": [
    "num_replicate_pairs = float( len(set(replicate_sample_annotation.patient_id)) )\n",
    "print num_replicate_pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of SNPs between Replicate Pairs with $\\Delta AF \\ge 70\\%$  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "num_replicate_SNPs_above_threshold = sum( replicate_pair_SNPs.alt_AF_diff.values >= 0.70 )\n",
    "print num_replicate_SNPs_above_threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of Longitudinal Pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200.0\n"
     ]
    }
   ],
   "source": [
    "num_longitudinal_pairs = float( len(set(longitudinal_sample_annotation.patient_id)) )\n",
    "print num_longitudinal_pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of SNPs between Longitudinal Pairs with $\\Delta AF \\ge 70\\%$  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "174\n"
     ]
    }
   ],
   "source": [
    "num_longitudinal_SNPs_above_threshold = sum( longitudinal_pair_SNPs.alt_AF_diff.values >= 0.70 )\n",
    "print num_longitudinal_SNPs_above_threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sanity check that this $\\Delta AF \\ge 70\\%$ results in $\\approx 5\\%$ SNPs from Replicate Pairs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.268703898840886"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "( (float(num_replicate_SNPs_above_threshold) / num_replicate_pairs) /  ( (float(num_replicate_SNPs_above_threshold) / num_replicate_pairs) + (float(num_longitudinal_SNPs_above_threshold) / num_longitudinal_pairs) ) ) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at some of the longitudinal SNPs passing this threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>ref_position</th>\n",
       "      <th>ref_allele</th>\n",
       "      <th>alt_allele</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>genomic_coord</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>alt_AF_diff</th>\n",
       "      <th>SNP_type</th>\n",
       "      <th>AA_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>WALKER</td>\n",
       "      <td>P000226</td>\n",
       "      <td>2154695</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv1908c</td>\n",
       "      <td>1417</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>katG</td>\n",
       "      <td>0.70</td>\n",
       "      <td>N</td>\n",
       "      <td>V473F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>WALKER</td>\n",
       "      <td>P000226</td>\n",
       "      <td>1247263</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>Rv1124</td>\n",
       "      <td>137</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>ephC</td>\n",
       "      <td>0.71</td>\n",
       "      <td>N</td>\n",
       "      <td>C46F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>CETR</td>\n",
       "      <td>3451</td>\n",
       "      <td>9582</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv0006</td>\n",
       "      <td>2281</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>gyrA</td>\n",
       "      <td>0.71</td>\n",
       "      <td>N</td>\n",
       "      <td>R761G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>WITNEY</td>\n",
       "      <td>16</td>\n",
       "      <td>4326401</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv3854c</td>\n",
       "      <td>1073</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>ethA</td>\n",
       "      <td>0.72</td>\n",
       "      <td>N</td>\n",
       "      <td>G358A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432</th>\n",
       "      <td>GUERRA</td>\n",
       "      <td>KPS_17</td>\n",
       "      <td>3220190</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv2913c</td>\n",
       "      <td>1509</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.72</td>\n",
       "      <td>S</td>\n",
       "      <td>R503R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>CETR</td>\n",
       "      <td>3451</td>\n",
       "      <td>2288697</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>Rv2043c</td>\n",
       "      <td>545</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>pncA</td>\n",
       "      <td>0.72</td>\n",
       "      <td>N</td>\n",
       "      <td>L182W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>GUERRA</td>\n",
       "      <td>KPS_24</td>\n",
       "      <td>851701</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv0757</td>\n",
       "      <td>94</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>phoP</td>\n",
       "      <td>0.72</td>\n",
       "      <td>N</td>\n",
       "      <td>I32V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>TRAUNER</td>\n",
       "      <td>P11</td>\n",
       "      <td>1767490</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>Rv1563c</td>\n",
       "      <td>1943</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>treY</td>\n",
       "      <td>0.73</td>\n",
       "      <td>N</td>\n",
       "      <td>T648S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>CETR</td>\n",
       "      <td>3451</td>\n",
       "      <td>2062292</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>Rv1818c</td>\n",
       "      <td>383</td>\n",
       "      <td>PE/PPE</td>\n",
       "      <td>PE_PGRS33</td>\n",
       "      <td>0.73</td>\n",
       "      <td>N</td>\n",
       "      <td>T128N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>CETR</td>\n",
       "      <td>3451</td>\n",
       "      <td>3301183</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv2950c</td>\n",
       "      <td>1273</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>fadD29</td>\n",
       "      <td>0.73</td>\n",
       "      <td>N</td>\n",
       "      <td>V425F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1472</th>\n",
       "      <td>CASALI</td>\n",
       "      <td>P305</td>\n",
       "      <td>812422</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Rv0716</td>\n",
       "      <td>364</td>\n",
       "      <td>Essential</td>\n",
       "      <td>rplE</td>\n",
       "      <td>0.74</td>\n",
       "      <td>N</td>\n",
       "      <td>R122C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>GUERRA</td>\n",
       "      <td>KPS_48</td>\n",
       "      <td>969152</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Rv0872c</td>\n",
       "      <td>1093</td>\n",
       "      <td>PE/PPE</td>\n",
       "      <td>PE_PGRS15</td>\n",
       "      <td>0.75</td>\n",
       "      <td>N</td>\n",
       "      <td>E365K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>CETR</td>\n",
       "      <td>2511</td>\n",
       "      <td>561359</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>Rv0470c</td>\n",
       "      <td>353</td>\n",
       "      <td>Antigen</td>\n",
       "      <td>pcaA</td>\n",
       "      <td>0.75</td>\n",
       "      <td>N</td>\n",
       "      <td>V118G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>WITNEY</td>\n",
       "      <td>30</td>\n",
       "      <td>3688276</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv3302c</td>\n",
       "      <td>1167</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>glpD2</td>\n",
       "      <td>0.76</td>\n",
       "      <td>S</td>\n",
       "      <td>T389T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>CETR</td>\n",
       "      <td>3451</td>\n",
       "      <td>2288878</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv2043c</td>\n",
       "      <td>364</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>pncA</td>\n",
       "      <td>0.77</td>\n",
       "      <td>N</td>\n",
       "      <td>Q122*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1412</th>\n",
       "      <td>GUERRA</td>\n",
       "      <td>KPS_17</td>\n",
       "      <td>1273552</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv1146</td>\n",
       "      <td>198</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>mmpL13b</td>\n",
       "      <td>0.79</td>\n",
       "      <td>S</td>\n",
       "      <td>L66L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>CASALI</td>\n",
       "      <td>P377</td>\n",
       "      <td>104962</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv0095c</td>\n",
       "      <td>254</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "      <td>N</td>\n",
       "      <td>A85V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>WALKER</td>\n",
       "      <td>P000226</td>\n",
       "      <td>2322095</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Rv2064</td>\n",
       "      <td>645</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>cobG</td>\n",
       "      <td>0.80</td>\n",
       "      <td>S</td>\n",
       "      <td>S215S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>GUERRA</td>\n",
       "      <td>KPS_79</td>\n",
       "      <td>886005</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv0792c</td>\n",
       "      <td>642</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "      <td>S</td>\n",
       "      <td>V214V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BRYANT</td>\n",
       "      <td>4146V</td>\n",
       "      <td>3547988</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv3179</td>\n",
       "      <td>371</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.81</td>\n",
       "      <td>N</td>\n",
       "      <td>L124R</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     population patient_id  ref_position ref_allele alt_allele  gene_id  \\\n",
       "852      WALKER    P000226       2154695          C          A  Rv1908c   \n",
       "850      WALKER    P000226       1247263          G          T   Rv1124   \n",
       "1080       CETR       3451          9582          A          G   Rv0006   \n",
       "1138     WITNEY         16       4326401          C          G  Rv3854c   \n",
       "1432     GUERRA     KPS_17       3220190          G          A  Rv2913c   \n",
       "1082       CETR       3451       2288697          A          C  Rv2043c   \n",
       "938      GUERRA     KPS_24        851701          A          G   Rv0757   \n",
       "820     TRAUNER        P11       1767490          G          C  Rv1563c   \n",
       "1081       CETR       3451       2062292          G          T  Rv1818c   \n",
       "1084       CETR       3451       3301183          C          A  Rv2950c   \n",
       "1472     CASALI       P305        812422          C          T   Rv0716   \n",
       "730      GUERRA     KPS_48        969152          C          T  Rv0872c   \n",
       "1238       CETR       2511        561359          A          C  Rv0470c   \n",
       "906      WITNEY         30       3688276          G          A  Rv3302c   \n",
       "1083       CETR       3451       2288878          G          A  Rv2043c   \n",
       "1412     GUERRA     KPS_17       1273552          G          A   Rv1146   \n",
       "1007     CASALI       P377        104962          G          A  Rv0095c   \n",
       "853      WALKER    P000226       2322095          C          T   Rv2064   \n",
       "865      GUERRA     KPS_79        886005          C          G  Rv0792c   \n",
       "24       BRYANT      4146V       3547988          T          G   Rv3179   \n",
       "\n",
       "     genomic_coord          gene_category gene_symbol  alt_AF_diff SNP_type  \\\n",
       "852           1417  Antibiotic Resistance        katG         0.70        N   \n",
       "850            137          Non-Essential        ephC         0.71        N   \n",
       "1080          2281  Antibiotic Resistance        gyrA         0.71        N   \n",
       "1138          1073  Antibiotic Resistance        ethA         0.72        N   \n",
       "1432          1509          Non-Essential         NaN         0.72        S   \n",
       "1082           545  Antibiotic Resistance        pncA         0.72        N   \n",
       "938             94          Non-Essential        phoP         0.72        N   \n",
       "820           1943          Non-Essential        treY         0.73        N   \n",
       "1081           383                 PE/PPE   PE_PGRS33         0.73        N   \n",
       "1084          1273          Non-Essential      fadD29         0.73        N   \n",
       "1472           364              Essential        rplE         0.74        N   \n",
       "730           1093                 PE/PPE   PE_PGRS15         0.75        N   \n",
       "1238           353                Antigen        pcaA         0.75        N   \n",
       "906           1167          Non-Essential       glpD2         0.76        S   \n",
       "1083           364  Antibiotic Resistance        pncA         0.77        N   \n",
       "1412           198          Non-Essential     mmpL13b         0.79        S   \n",
       "1007           254          Non-Essential         NaN         0.80        N   \n",
       "853            645          Non-Essential        cobG         0.80        S   \n",
       "865            642          Non-Essential         NaN         0.80        S   \n",
       "24             371          Non-Essential         NaN         0.81        N   \n",
       "\n",
       "     AA_change  \n",
       "852      V473F  \n",
       "850       C46F  \n",
       "1080     R761G  \n",
       "1138     G358A  \n",
       "1432     R503R  \n",
       "1082     L182W  \n",
       "938       I32V  \n",
       "820      T648S  \n",
       "1081     T128N  \n",
       "1084     V425F  \n",
       "1472     R122C  \n",
       "730      E365K  \n",
       "1238     V118G  \n",
       "906      T389T  \n",
       "1083     Q122*  \n",
       "1412      L66L  \n",
       "1007      A85V  \n",
       "853      S215S  \n",
       "865      V214V  \n",
       "24       L124R  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longitudinal_pair_SNPs[longitudinal_pair_SNPs.alt_AF_diff >= 0.70].sort_values(by = 'alt_AF_diff').head(n=20)"
   ]
  }
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