(H) Filter Replicate and Longitudinal Samples – Contamination with Fixed SNP Threshold.ipynb

{
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  {
   "cell_type": "code",
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##########################################################################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Functions* for retrieving fixed SNPs between each pair of *longitudinal* and *replicate* isolates (SNPs with $>= 75$% $\\Delta$ AF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##########################################################################################################################################################################################################################################"
   ]
  },
  {
   "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",
    "from matplotlib.gridspec 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",
    "import pickle\n",
    "\n",
    "#for exporting to Adobe Illustrator\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "mpl.rcParams['ps.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decide on a threshold for difference in Alternate Allele Frequencies to call SNPs between two isolates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "alt_AF_diff_threshold = 0.75 #x% "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load regions to exclude from analysis per EBR score across H37Rv (dropping sites with EBR score < 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/pickled_files/variant_calling/H37Rv_sites_to_drop.pkl', 'rb') as f:\n",
    "    H37Rv_positions_to_drop = pickle.load(f)\n",
    "    \n",
    "#convert to a set (faster to query)\n",
    "H37Rv_positions_to_drop = set(H37Rv_positions_to_drop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Cell* to annotate SNPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Important Packages\n",
    "################################################################################################################################################################################################\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sys\n",
    "import pickle\n",
    "\n",
    "import Bio\n",
    "from Bio.Alphabet import IUPAC\n",
    "from Bio.Seq import Seq\n",
    "from Bio.SeqRecord import SeqRecord\n",
    "from Bio.SeqFeature import SeqFeature, FeatureLocation\n",
    "from Bio import SeqIO\n",
    "from StringIO import StringIO\n",
    "from Bio import AlignIO\n",
    "from Bio.Align import AlignInfo\n",
    "from Bio.Seq import MutableSeq\n",
    "################################################################################################################################################################################################\n",
    "\n",
    "\n",
    "# Relevant Information for H37Rv sequence SNP functional annotation\n",
    "################################################################################################################################################################################################\n",
    "####### Collect all DNA and Amino Acid sequences corresponding to genes on H37Rv #######\n",
    "#load reference genome and reference annotation\n",
    "reference_genome = '/n/data1/hms/dbmi/farhat/bin/work-horse/bin/h37rv.fasta'\n",
    "for reference_genome in SeqIO.parse(reference_genome, \"fasta\"):\n",
    "    reference_genome.seq.alphabet = IUPAC.unambiguous_dna\n",
    "\n",
    "reference_genome_annotation = pd.read_csv('/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/H37Rv/h37rv_genome_summary.txt', '\\t').set_index('name')\n",
    "\n",
    "####### Function to translate coding DNA sequences ####### \n",
    "def translate(gene_id, sequence):\n",
    "\n",
    "    #find which strand the gene is located on and translate\n",
    "    strand = reference_genome_annotation.loc[gene_id, 'strand']\n",
    "    if strand == '+':\n",
    "        protein_sequence = sequence.translate(table=\"Bacterial\", cds=False)\n",
    "    elif strand == '-':\n",
    "        protein_sequence = sequence.reverse_complement().translate(table=\"Bacterial\", cds=False)\n",
    "\n",
    "    return protein_sequence\n",
    "\n",
    "####### Load in dictionaries for SNP annotation #######\n",
    "with open('/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/pickled_files/dicts_for_SNP_annotation/H37Rv_gene_seq_records.pickle', 'rb') as handle:\n",
    "    ref_gene_sequences_records = pickle.load(handle)\n",
    "    \n",
    "with open('/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/pickled_files/dicts_for_SNP_annotation/H37Rv_protein_seq_records.pickle', 'rb') as handle:\n",
    "    ref_protein_sequences_records = pickle.load(handle)\n",
    "    \n",
    "with open('/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/pickled_files/dicts_for_SNP_annotation/H37Rv_coord_gene_mapping.pickle', 'rb') as handle:\n",
    "    ReferencePosition_Gene_mapping = pickle.load(handle)\n",
    "    \n",
    "####### get Gene Categories #######\n",
    "gene_categories = pd.read_csv('/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/CSV_files/gene_categories/gene_categories.csv').set_index('name')\n",
    "gene_categories_dict = dict([gene_id , gene_category] for gene_id, gene_category in zip(list(gene_categories.gene_id) , list(gene_categories.Gene_Category)))\n",
    "\n",
    "####### get Gene Symbols #######\n",
    "gene_symbol_dict = dict([gene_id , gene_symbol] for gene_id, gene_symbol in zip(list(reference_genome_annotation.symbol.index) , list( reference_genome_annotation.symbol )))\n",
    "################################################################################################################################################################################################\n",
    "\n",
    "\n",
    "# Function to annotate Intergenic SNPs\n",
    "################################################################################################################################################################################################\n",
    "def find_flanking_genes_for_intergenic_region(intergenic_ref_pos): \n",
    "\n",
    "    #this function finds the genes flagging an intergenic region given a reference position\n",
    "\n",
    "    #find gene immediately in the 5' direction\n",
    "    for i in range(0 , 100000):\n",
    "\n",
    "        #move toward 5' direction\n",
    "        if ReferencePosition_Gene_mapping[intergenic_ref_pos - i] != []:\n",
    "\n",
    "            gene_to_left = ReferencePosition_Gene_mapping[intergenic_ref_pos - i][0]\n",
    "            break\n",
    "\n",
    "    #find gene immediately in the 3' direction       \n",
    "    for i in range(0 , 100000):\n",
    "\n",
    "        #move toward 3' direction\n",
    "        try:\n",
    "            if ReferencePosition_Gene_mapping[intergenic_ref_pos + i] != []:\n",
    "\n",
    "                gene_to_right = ReferencePosition_Gene_mapping[intergenic_ref_pos + i][0]\n",
    "                break\n",
    "        \n",
    "        #KeyError means we have hit the 'end' of the chromosome, the intergenic region at then end of H37Rv in 5' > 3' orientation \n",
    "        #since TB chromosome is circular the gene to the 'right' is Rv0001    \n",
    "        except KeyError:\n",
    "            \n",
    "            gene_to_right = 'Rv0001'\n",
    "            break\n",
    "    \n",
    "    return gene_to_left + '_' + gene_to_right\n",
    "################################################################################################################################################################################################\n",
    "\n",
    "\n",
    "# Function to determine whether SNPs are Synonymous or Non-Synonymous; Returns gene coordinate, codon position, AA changes, Gene Category & Symbol\n",
    "################################################################################################################################################################################################\n",
    "def SNP_annotate(ref_seq_position , alt_allele_i):\n",
    "    \n",
    "    '''\n",
    "    This function takes as input a reference position on H37Rv located within a \n",
    "    gene and an alternate allele and returns whether the base change \n",
    "    would correspond to a different Amino Acid sequence that results \n",
    "    from translating the DNA sequence into an AA sequence.\n",
    "    \n",
    "    '''\n",
    "    gene_intergenic_id_list = []\n",
    "    genomic_coord_list = []\n",
    "    gene_category_list = []\n",
    "    gene_symbol_list = []\n",
    "    Syn_NSyn_list = []\n",
    "    AA_change_list = []\n",
    "    \n",
    "    #get the Reference Allele from the complete H37Rv reference genome, indexing starts from 0\n",
    "    ref_allele_i = reference_genome.seq[int(ref_seq_position) - 1] \n",
    "    \n",
    "    #find the gene that SNP occurs on; check list corresponding to H37Rv coordinate to see if there are any genes associated with RefPosition\n",
    "    if len(ReferencePosition_Gene_mapping[ref_seq_position]) > 0:\n",
    "\n",
    "        #iterate through all genes that ReferencePosition is mapped to (i.e. SNP might correspond to 2 genes)\n",
    "        for gene_intergenic_id in ReferencePosition_Gene_mapping[ref_seq_position]:\n",
    "\n",
    "            #find genomic coordinate of SNP relative to gene (subtract 1 since reference seq starts counting at 1)\n",
    "            gene_relative_coord = (ref_seq_position - 1) - min( reference_genome_annotation.loc[gene_intergenic_id , 'chromStart'] , reference_genome_annotation.loc[gene_intergenic_id , 'chromEnd'] )\n",
    "            \n",
    "            #find the genomic coordinate (relative to the gene, in the 5' to 3' direction)\n",
    "            strand = reference_genome_annotation.loc[gene_intergenic_id, 'strand']\n",
    "            if strand == '+':\n",
    "                 genomic_5_to_3_coord = (ref_seq_position) - reference_genome_annotation.loc[gene_intergenic_id , 'chromStart']\n",
    "\n",
    "            elif strand == '-':\n",
    "                 genomic_5_to_3_coord = (reference_genome_annotation.loc[gene_intergenic_id , 'chromEnd']) - (ref_seq_position-1)\n",
    "                    \n",
    "            #find gene category (if one exists)\n",
    "            try:\n",
    "                gene_category_i = gene_categories_dict[gene_intergenic_id]\n",
    "            except KeyError:\n",
    "                gene_category_i = 'None'\n",
    "            \n",
    "            #find gene symbol (if one exists)\n",
    "            try:\n",
    "                gene_symbol_i = gene_symbol_dict[gene_intergenic_id]\n",
    "            except KeyError:\n",
    "                gene_symbol_i = 'None'\n",
    "            \n",
    "            #alternate allele is an actual base\n",
    "            if alt_allele_i in ['A','C','G','T']:\n",
    "\n",
    "                #translate into protein sequence with the SNP in place if not InDel or intergenic region\n",
    "                SNP_change = alt_allele_i\n",
    "\n",
    "                #ALTERNATE allele (is it Syn or NSyn?)\n",
    "                #get sequence from dictionary of sequences (and convert to mutable object)\n",
    "                test_gene_sequence = ref_gene_sequences_records[gene_intergenic_id].seq.tomutable()\n",
    "\n",
    "                #change reference gene sequence by the SNP in the query sequence\n",
    "                test_gene_sequence[int(gene_relative_coord)] = SNP_change\n",
    "\n",
    "                #convert back immutable object\n",
    "                test_gene_sequence = test_gene_sequence.toseq()\n",
    "\n",
    "                #translate sequence into amino acid seq\n",
    "                test_protein_sequence = translate(gene_intergenic_id , test_gene_sequence)\n",
    "\n",
    "                #store the H37Rv AA seq to compare against\n",
    "                H37Rv_AA_sequence = ref_protein_sequences_records[gene_intergenic_id].seq\n",
    "\n",
    "                #get the codon number where the SNP occurs within\n",
    "                ## take the genomic coordinate (relative to the gene, in the 5' to 3' direction), divide by 3, then take the ceiling of this number (will be fraction if SNP occurs in 1st or 2nd position on codon)\n",
    "                strand = reference_genome_annotation.loc[gene_intergenic_id, 'strand']\n",
    "                if strand == '+':\n",
    "                     genomic_5_to_3_coord = (ref_seq_position) - reference_genome_annotation.loc[gene_intergenic_id , 'chromStart']\n",
    "\n",
    "                elif strand == '-':\n",
    "                     genomic_5_to_3_coord = (reference_genome_annotation.loc[gene_intergenic_id , 'chromEnd']) - (ref_seq_position-1)\n",
    "\n",
    "                codon_coord = int(np.ceil( float( genomic_5_to_3_coord) / 3.0 ))\n",
    "\n",
    "                #compare to AA seq of original gene\n",
    "                if test_protein_sequence == H37Rv_AA_sequence:\n",
    "\n",
    "                    SNP_type = 'S'\n",
    "\n",
    "                    #get the AA before & after\n",
    "                    AA_change = H37Rv_AA_sequence[codon_coord-1] + str(codon_coord) + test_protein_sequence[codon_coord-1]\n",
    "\n",
    "                else:\n",
    "                    SNP_type = 'N'\n",
    "\n",
    "                    #get the AA before & after\n",
    "                    AA_change = H37Rv_AA_sequence[codon_coord-1] + str(codon_coord) + test_protein_sequence[codon_coord-1]\n",
    "                    \n",
    "            #alternate allele is a dummy (Base Call completely supports the Reference Allele)       \n",
    "            else:\n",
    "                \n",
    "                SNP_type = 'None'\n",
    "                AA_change = 'None'\n",
    "\n",
    "            #store relevant info in lists    \n",
    "            gene_intergenic_id_list.append(gene_intergenic_id)\n",
    "            genomic_coord_list.append(genomic_5_to_3_coord)\n",
    "            gene_category_list.append(gene_category_i)\n",
    "            gene_symbol_list.append(gene_symbol_i)\n",
    "            Syn_NSyn_list.append(SNP_type)\n",
    "            AA_change_list.append(AA_change)\n",
    "    \n",
    "    #if no gene in H37Rv corresponds to the Reference Position for SNP, then SNP must be intergenic\n",
    "    else:\n",
    "        \n",
    "        gene_intergenic_id = find_flanking_genes_for_intergenic_region(ref_seq_position)\n",
    "        genomic_5_to_3_coord = 'None'\n",
    "        gene_category_i = 'None'\n",
    "        gene_symbol_i = 'None'\n",
    "        SNP_type = 'I'\n",
    "        AA_change = 'None'\n",
    "        \n",
    "        #store relevant info in lists    \n",
    "        gene_intergenic_id_list.append(gene_intergenic_id)\n",
    "        genomic_coord_list.append(genomic_5_to_3_coord)\n",
    "        gene_category_list.append(gene_category_i)\n",
    "        gene_symbol_list.append(gene_symbol_i)\n",
    "        Syn_NSyn_list.append(SNP_type)\n",
    "        AA_change_list.append(AA_change)\n",
    "    \n",
    "    #if there is only a single gene associated with this SNP, just return the individual elememts\n",
    "    if len(gene_intergenic_id_list) == 1:\n",
    "        return [ref_allele_i , gene_intergenic_id , genomic_5_to_3_coord , gene_category_i , gene_symbol_i , SNP_type , AA_change]\n",
    "    \n",
    "    #else if there are two genes associated with this SNP, return elements for each SNP annotation in a list\n",
    "    elif len(gene_intergenic_id_list) > 1:\n",
    "        return [ref_allele_i , gene_intergenic_id_list , genomic_coord_list , gene_category_list , gene_symbol_list , Syn_NSyn_list , AA_change_list]\n",
    "################################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Function* to get SNPs between paired isolates (filtered for $\\Delta AF$, MGE and low EBR score regions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_filtered_SNPs_between_isolates(isolate_pair_ID , alt_AF_diff_threshold):\n",
    "\n",
    "    '''\n",
    "    This function only the fixed SNP variants that occur between a given isolate pair \n",
    "    by loading in the pickled DataFrame for isolate pair and comparing alternate allele frequencies called in each isolate.\n",
    "    (Differing Base Calls that have an Alternate Allele Frequencies >= x% different). \n",
    "    This function also drops regions flagged as Mobile Genetic Elements & Regions of poor Illumina mapping / variant calling\n",
    "    per Empirical Base Recall (EBR) scores across H37Rv.\n",
    "    '''\n",
    "\n",
    "    ################################################################################\n",
    "    ### get SNPs between pair of isolates\n",
    "    ################################################################################\n",
    "\n",
    "    population = sample_annotation.loc[isolate_pair_ID , 'population'][0]\n",
    "\n",
    "    #load in the differing Base Calls for the isolate pair from pickle\n",
    "    different_base_calls_between_isolates = pd.read_pickle(SNP_variant_dir + population + '_' + isolate_pair_ID + '/base_calls_different_between_isolates.pkl')\n",
    "\n",
    "    ################################################################################\n",
    "    ### Drop SNPs with change in AF < x%\n",
    "    ################################################################################\n",
    "\n",
    "    #FILTER out paired Base Calls that have a difference in Alternate Allele Frequency of less than x%\n",
    "    alt_AF_isolate_A = different_base_calls_between_isolates.loc[range(0 , np.shape(different_base_calls_between_isolates)[0] , 2) , 'alt_AF']\n",
    "    alt_AF_isolate_B = different_base_calls_between_isolates.loc[range(1 , np.shape(different_base_calls_between_isolates)[0] , 2) , 'alt_AF']\n",
    "    alt_AF_diff_btwn_paired_isolates = abs(alt_AF_isolate_A.values - alt_AF_isolate_B.values)\n",
    "\n",
    "    isolate_A_Base_Call_indices_small_change_alt_AF = list(alt_AF_isolate_A[alt_AF_diff_btwn_paired_isolates < alt_AF_diff_threshold].index)\n",
    "    isolate_B_Base_Call_indices_small_change_alt_AF = list(alt_AF_isolate_B[alt_AF_diff_btwn_paired_isolates < alt_AF_diff_threshold].index)\n",
    "    Base_Call_Indices_SMALL_Alt_AF_Diff = isolate_A_Base_Call_indices_small_change_alt_AF + isolate_B_Base_Call_indices_small_change_alt_AF\n",
    "\n",
    "    #drop paired Base Calls w/ corresponding change in Alterante Allele Frequency < x%\n",
    "    different_base_calls_between_isolates.drop(Base_Call_Indices_SMALL_Alt_AF_Diff , axis = 0 , inplace = True)\n",
    "\n",
    "    #reset index of filtered SNP DataFrame\n",
    "    different_base_calls_between_isolates.reset_index(inplace = True, drop = True)\n",
    "\n",
    "    ################################################################################\n",
    "    ### Drop SNPs with change in regions with low EBR scores\n",
    "    ################################################################################\n",
    "\n",
    "    #Drop Base Calls in H37Rv sites with low EBR score (make sure there is at least 1 SNP)\n",
    "    if np.shape(different_base_calls_between_isolates)[0] > 0:\n",
    "\n",
    "        #create a boolean filter for SNPs to keep\n",
    "        SNPs_to_keep_filter = [SNP_i_ref_pos not in H37Rv_positions_to_drop for SNP_i_ref_pos in different_base_calls_between_isolates.ref_position]\n",
    "\n",
    "        #filter out SNPs in H37Rv sites with low EBR scores and reset index\n",
    "        different_base_calls_between_isolates = different_base_calls_between_isolates[SNPs_to_keep_filter]\n",
    "        different_base_calls_between_isolates.reset_index(inplace = True, drop = True)\n",
    "\n",
    "    ################################################################################\n",
    "    ### Annotate SNPs & Drop SNPs in MGE regions\n",
    "    ################################################################################\n",
    "\n",
    "    gene_id_list = []\n",
    "    gene_coord_list = []\n",
    "    gene_category_list = []\n",
    "    gene_symbol_list = []\n",
    "    SNP_ftype_list = []\n",
    "    AA_change_list = []\n",
    "\n",
    "    #Annotate Filtered Base Calls (make sure there is at least 1 SNP)\n",
    "    if np.shape(different_base_calls_between_isolates)[0] > 0:\n",
    "\n",
    "        for ref_position_i , alt_base_i in zip(list(different_base_calls_between_isolates.ref_position) , list(different_base_calls_between_isolates.alt_base)):\n",
    "\n",
    "            #annotate SNP\n",
    "            gene_id_i , gene_coord_i , gene_category_i , gene_symbol_i , SNP_ftype_i , AA_change_i = SNP_annotate(ref_position_i , alt_base_i)[1:]\n",
    "\n",
    "            gene_id_list.append(gene_id_i)\n",
    "            gene_coord_list.append(gene_coord_i)\n",
    "            gene_category_list.append(gene_category_i)\n",
    "            gene_symbol_list.append(gene_symbol_i)\n",
    "            SNP_ftype_list.append(SNP_ftype_i)\n",
    "            AA_change_list.append(AA_change_i)\n",
    "\n",
    "        #create columns to store SNP annotation info\n",
    "        different_base_calls_between_isolates['gene_id'] = gene_id_list\n",
    "        different_base_calls_between_isolates['gene_coord'] = gene_coord_list\n",
    "        different_base_calls_between_isolates['gene_category'] = gene_category_list\n",
    "        different_base_calls_between_isolates['gene_symbol'] = gene_symbol_list\n",
    "        different_base_calls_between_isolates['SNP_ftype'] = SNP_ftype_list\n",
    "        different_base_calls_between_isolates['AA_change'] = AA_change_list\n",
    "\n",
    "        #FILTER out Base Calls in MGE regions (Mobile Genentic Elements)\n",
    "        SNPs_to_drop_filter = [] #True if SNP is located within an MGE region\n",
    "\n",
    "        for gene_id_i in list(different_base_calls_between_isolates.gene_category):\n",
    "\n",
    "            #only 1 or 0 genes associated with this SNP\n",
    "            if (type(gene_id_i) == str) and (gene_id_i == 'Mobile Genetic Element'):\n",
    "\n",
    "                SNPs_to_drop_filter.append(True)\n",
    "\n",
    "            #two genes associated with this SNP\n",
    "            elif (type(gene_id_i) == list) and ('Mobile Genetic Element' in gene_id_i):\n",
    "\n",
    "                SNPs_to_drop_filter.append(True)\n",
    "\n",
    "            #SNP not in an MGE region so don't drop\n",
    "            else:\n",
    "\n",
    "                SNPs_to_drop_filter.append(False)\n",
    "\n",
    "        #create a boolean filter for SNPs to keep\n",
    "        SNPs_to_keep_filter = [not MGE_SNP for MGE_SNP in SNPs_to_drop_filter]\n",
    "\n",
    "        #filter out SNPs in MGE regions and reset index\n",
    "        different_base_calls_between_isolates = different_base_calls_between_isolates[SNPs_to_keep_filter]\n",
    "        different_base_calls_between_isolates.reset_index(inplace = True, drop = True)\n",
    "\n",
    "    #No SNPs detected between this pair of isolates (empty DataFrame)\n",
    "    else:\n",
    "\n",
    "        different_base_calls_between_isolates['gene_id'] = \"\"\n",
    "        different_base_calls_between_isolates['gene_coord'] = \"\"\n",
    "        different_base_calls_between_isolates['gene_category'] = \"\"\n",
    "        different_base_calls_between_isolates['gene_symbol'] = \"\"\n",
    "        different_base_calls_between_isolates['SNP_ftype'] = \"\"\n",
    "        different_base_calls_between_isolates['AA_change'] = \"\"\n",
    "    \n",
    "    return different_base_calls_between_isolates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##########################################################################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Longitudinal Sample Pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##########################################################################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Import Sample Annotation file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "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_with_Kraken_and_F2.csv' , sep  = ',').set_index('patient_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "SNP_variant_dir = '/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/pickled_files/variant_calling/longitudinal_SNPs/all_SNPs_between_longitudinal_pairs/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>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",
       "    <tr>\n",
       "      <th>patient_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P07</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/trauner/ERR1...</td>\n",
       "      <td>TRAUNER</td>\n",
       "      <td>ERR1352324</td>\n",
       "      <td>SAMEA3920989</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR1352324</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P07</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/trauner/ERR1...</td>\n",
       "      <td>TRAUNER</td>\n",
       "      <td>ERR1352328</td>\n",
       "      <td>SAMEA3920993</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR1352328</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P08</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/trauner/ERR1...</td>\n",
       "      <td>TRAUNER</td>\n",
       "      <td>ERR1352329</td>\n",
       "      <td>SAMEA3920994</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR1352329</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P08</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/trauner/ERR1...</td>\n",
       "      <td>TRAUNER</td>\n",
       "      <td>ERR1352333</td>\n",
       "      <td>SAMEA3920998</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR1352333</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P06</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/trauner/ERR1...</td>\n",
       "      <td>TRAUNER</td>\n",
       "      <td>ERR1352319</td>\n",
       "      <td>SAMEA3920984</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR1352319</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  fastq_files population  \\\n",
       "patient_id                                                                 \n",
       "P07         /n/data1/hms/dbmi/farhat/fastq_db/trauner/ERR1...    TRAUNER   \n",
       "P07         /n/data1/hms/dbmi/farhat/fastq_db/trauner/ERR1...    TRAUNER   \n",
       "P08         /n/data1/hms/dbmi/farhat/fastq_db/trauner/ERR1...    TRAUNER   \n",
       "P08         /n/data1/hms/dbmi/farhat/fastq_db/trauner/ERR1...    TRAUNER   \n",
       "P06         /n/data1/hms/dbmi/farhat/fastq_db/trauner/ERR1...    TRAUNER   \n",
       "\n",
       "                run_ID     sample_ID  sample_order         tag  isolate_type  \n",
       "patient_id                                                                    \n",
       "P07         ERR1352324  SAMEA3920989             1  ERR1352324  longitudinal  \n",
       "P07         ERR1352328  SAMEA3920993             2  ERR1352328  longitudinal  \n",
       "P08         ERR1352329  SAMEA3920994             1  ERR1352329  longitudinal  \n",
       "P08         ERR1352333  SAMEA3920998             2  ERR1352333  longitudinal  \n",
       "P06         ERR1352319  SAMEA3920984             1  ERR1352319  longitudinal  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_annotation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "244\n"
     ]
    }
   ],
   "source": [
    "num_isolate_pair_IDs = np.shape(sample_annotation)[0] / 2\n",
    "print num_isolate_pair_IDs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "isolate_pair_ID_list = list(set(sample_annotation.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Call function to collect SNPs passing Difference in Alternate Allele Frequency Threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "10\n",
      "15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rv76/anaconda2/lib/python2.7/site-packages/Bio/Seq.py:2715: BiopythonWarning: Partial codon, len(sequence) not a multiple of three. Explicitly trim the sequence or add trailing N before translation. This may become an error in future.\n",
      "  BiopythonWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "Base_Call_variants_btwn_isolates_big_change_in_alt_AF = []\n",
    "\n",
    "isolate_pair_index = 0\n",
    "\n",
    "#iterate through isolate pairs, collect all SNP variants arising between each pair of isolates\n",
    "for isolate_pair_ID in isolate_pair_ID_list:\n",
    "    \n",
    "    #retrieve filtered paired Base Calls with a change in Alternate Allele Frequency > threshold\n",
    "    Base_Call_variants_btwn_isolates_big_change_in_alt_AF_pair_i = get_filtered_SNPs_between_isolates(isolate_pair_ID , alt_AF_diff_threshold)\n",
    "    \n",
    "    #store relevant Base Call info in list of DataFrames (1 for each isolate pair)\n",
    "    Base_Call_variants_btwn_isolates_big_change_in_alt_AF.append(Base_Call_variants_btwn_isolates_big_change_in_alt_AF_pair_i)\n",
    "    isolate_pair_index += 1\n",
    "    \n",
    "    if isolate_pair_index % 5 == 0:\n",
    "        print isolate_pair_index\n",
    "        \n",
    "#concatenate DataFrames for each subject into 1 DataFrame\n",
    "Base_Call_variants_btwn_isolates_big_change_in_alt_AF = pd.concat(Base_Call_variants_btwn_isolates_big_change_in_alt_AF , axis = 0)\n",
    "\n",
    "Base_Call_variants_btwn_isolates_big_change_in_alt_AF.reset_index(inplace = True , drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Filter*: Drop paired Base Calls if both Base Calls in a pair support *different* Alternate Alleles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#list that stores the indices of paired Base Calls with DIFFERENT Alternate Alleles\n",
    "Base_Calls_to_Drop = []\n",
    "\n",
    "#iterate through each PAIR of corresponding Base Calls from paired isolates\n",
    "for isolate_A_Base_Call_i , isolate_B_Base_Call_i in zip(range(0 , np.shape(Base_Call_variants_btwn_isolates_big_change_in_alt_AF)[0] , 2) , range(1 , np.shape(Base_Call_variants_btwn_isolates_big_change_in_alt_AF)[0] , 2) ):\n",
    "    \n",
    "    #pull info that both Base Calls should have in COMMON\n",
    "    isolate_A_Base_Call_info = list( Base_Call_variants_btwn_isolates_big_change_in_alt_AF.loc[isolate_A_Base_Call_i , ['ref_base','ref_position','gene_id','genomic_coord','population','patient_id']] ) \n",
    "    isolate_B_Base_Call_info = list( Base_Call_variants_btwn_isolates_big_change_in_alt_AF.loc[isolate_B_Base_Call_i , ['ref_base','ref_position','gene_id','genomic_coord','population','patient_id']] ) \n",
    "    \n",
    "    #make sure Base Calls Match with respect to Reference Base, Reference Position, gene ID, Genomic Coordinate, Gene Category, Symbol, Population & Patient ID\n",
    "    if isolate_A_Base_Call_info == isolate_B_Base_Call_info:\n",
    "        \n",
    "        #pull alternate Allele for each of the paired isolates\n",
    "        isolate_A_Alt_Base = Base_Call_variants_btwn_isolates_big_change_in_alt_AF.loc[isolate_A_Base_Call_i , 'alt_base']\n",
    "        isolate_B_Alt_Base = Base_Call_variants_btwn_isolates_big_change_in_alt_AF.loc[isolate_B_Base_Call_i , 'alt_base']\n",
    "        \n",
    "        #Check to see if there is a 'Z' in the pair of Alternate Alleles, if so one of the Base Calls supported the Reference Base (so there was no Alternate Allele)\n",
    "        if (isolate_A_Alt_Base == 'Z') or (isolate_B_Alt_Base == 'Z'):\n",
    "            pass\n",
    "        \n",
    "        #if neither Alternate Allele is a 'Z', then check to see that the Alternate Allele Bases Match\n",
    "        elif isolate_A_Alt_Base == isolate_B_Alt_Base:\n",
    "            pass\n",
    "        \n",
    "        #if the Alternate Alleles DON'T match and both Base Calls supported Alternate Alleles (not the Reference), then we can't compare the Allele Frequencies of these Alternate Alleles (since they're different)\n",
    "        else:\n",
    "            Base_Calls_to_Drop = Base_Calls_to_Drop + [isolate_A_Base_Call_i , isolate_B_Base_Call_i]\n",
    "        \n",
    "    #print indices of Base Calls and see what went wrong if the paired Base Calls have different information that the Calls should have in Common (Ref Position, Ref Base, Gene ID, Patient ID, etc.)\n",
    "    else:\n",
    "        print (isolate_A_Base_Call_i , isolate_B_Base_Call_i)\n",
    "        \n",
    "#Drop Paired Base Calls that supported different Alternate Alleles\n",
    "Base_Call_variants_btwn_isolates_big_change_in_alt_AF.drop(Base_Calls_to_Drop , axis = 0 , inplace = True)\n",
    "\n",
    "#reset index\n",
    "Base_Call_variants_btwn_isolates_big_change_in_alt_AF.reset_index(inplace = True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>ref_base</th>\n",
       "      <th>alt_base</th>\n",
       "      <th>ref_position</th>\n",
       "      <th>quality</th>\n",
       "      <th>SNP_type</th>\n",
       "      <th>PASS_filter</th>\n",
       "      <th>INFO</th>\n",
       "      <th>alt_AF</th>\n",
       "      <th>depth</th>\n",
       "      <th>tag</th>\n",
       "      <th>population</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_coord</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>SNP_ftype</th>\n",
       "      <th>AA_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>Z</td>\n",
       "      <td>761139</td>\n",
       "      <td>3140</td>\n",
       "      <td>Ref_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 100, 0, 0], u'AC': [0], u'AF': [0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89</td>\n",
       "      <td>ERR369703</td>\n",
       "      <td>CASALI</td>\n",
       "      <td>P251</td>\n",
       "      <td>Rv0667</td>\n",
       "      <td>1333</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>rpoB</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>761139</td>\n",
       "      <td>1266</td>\n",
       "      <td>Alt_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 0, 100, 0], u'AC': [2], u'AF': [1....</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36</td>\n",
       "      <td>ERR600658</td>\n",
       "      <td>CASALI</td>\n",
       "      <td>P251</td>\n",
       "      <td>Rv0667</td>\n",
       "      <td>1333</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>rpoB</td>\n",
       "      <td>N</td>\n",
       "      <td>H445D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T</td>\n",
       "      <td>Z</td>\n",
       "      <td>861378</td>\n",
       "      <td>3116</td>\n",
       "      <td>Ref_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 0, 0, 100], u'AC': [0], u'AF': [0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>ERR369703</td>\n",
       "      <td>CASALI</td>\n",
       "      <td>P251</td>\n",
       "      <td>Rv0768</td>\n",
       "      <td>467</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>aldA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>861378</td>\n",
       "      <td>1167</td>\n",
       "      <td>Alt_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 0, 100, 0], u'AC': [2], u'AF': [1....</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34</td>\n",
       "      <td>ERR600658</td>\n",
       "      <td>CASALI</td>\n",
       "      <td>P251</td>\n",
       "      <td>Rv0768</td>\n",
       "      <td>467</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>aldA</td>\n",
       "      <td>N</td>\n",
       "      <td>I156S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>G</td>\n",
       "      <td>Z</td>\n",
       "      <td>4993</td>\n",
       "      <td>4215</td>\n",
       "      <td>Ref_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 0, 100, 0], u'AC': [0], u'AF': [0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116</td>\n",
       "      <td>Peru4741</td>\n",
       "      <td>CETR</td>\n",
       "      <td>2718</td>\n",
       "      <td>Rv0004</td>\n",
       "      <td>560</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>4993</td>\n",
       "      <td>7575</td>\n",
       "      <td>Alt_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 0, 0, 100], u'AC': [2], u'AF': [1....</td>\n",
       "      <td>1.0</td>\n",
       "      <td>191</td>\n",
       "      <td>Peru5134</td>\n",
       "      <td>CETR</td>\n",
       "      <td>2718</td>\n",
       "      <td>Rv0004</td>\n",
       "      <td>560</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>G187V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G</td>\n",
       "      <td>Z</td>\n",
       "      <td>8040</td>\n",
       "      <td>6517</td>\n",
       "      <td>Ref_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 0, 100, 0], u'AC': [0], u'AF': [0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>173</td>\n",
       "      <td>Peru5134</td>\n",
       "      <td>CETR</td>\n",
       "      <td>2718</td>\n",
       "      <td>Rv0006</td>\n",
       "      <td>739</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>gyrA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>8040</td>\n",
       "      <td>4091</td>\n",
       "      <td>Alt_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [100, 0, 0, 0], u'AC': [2], u'AF': [1....</td>\n",
       "      <td>1.0</td>\n",
       "      <td>114</td>\n",
       "      <td>Peru4741</td>\n",
       "      <td>CETR</td>\n",
       "      <td>2718</td>\n",
       "      <td>Rv0006</td>\n",
       "      <td>739</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>gyrA</td>\n",
       "      <td>N</td>\n",
       "      <td>G247S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>Z</td>\n",
       "      <td>11370</td>\n",
       "      <td>3617</td>\n",
       "      <td>Ref_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 100, 0, 0], u'AC': [0], u'AF': [0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98</td>\n",
       "      <td>Peru4741</td>\n",
       "      <td>CETR</td>\n",
       "      <td>2718</td>\n",
       "      <td>Rvnt02_Rv0008c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>11370</td>\n",
       "      <td>8367</td>\n",
       "      <td>Alt_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 0, 0, 100], u'AC': [2], u'AF': [1....</td>\n",
       "      <td>1.0</td>\n",
       "      <td>211</td>\n",
       "      <td>Peru5134</td>\n",
       "      <td>CETR</td>\n",
       "      <td>2718</td>\n",
       "      <td>Rvnt02_Rv0008c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ref_base alt_base  ref_position  quality  SNP_type PASS_filter  \\\n",
       "0        C        Z        761139     3140  Ref_PASS          []   \n",
       "1        C        G        761139     1266  Alt_PASS          []   \n",
       "2        T        Z        861378     3116  Ref_PASS          []   \n",
       "3        T        G        861378     1167  Alt_PASS          []   \n",
       "4        G        Z          4993     4215  Ref_PASS          []   \n",
       "5        G        T          4993     7575  Alt_PASS          []   \n",
       "6        G        Z          8040     6517  Ref_PASS          []   \n",
       "7        G        A          8040     4091  Alt_PASS          []   \n",
       "8        C        Z         11370     3617  Ref_PASS          []   \n",
       "9        C        T         11370     8367  Alt_PASS          []   \n",
       "\n",
       "                                                INFO  alt_AF  depth  \\\n",
       "0  {u'QP': [0, 100, 0, 0], u'AC': [0], u'AF': [0....     0.0     89   \n",
       "1  {u'QP': [0, 0, 100, 0], u'AC': [2], u'AF': [1....     1.0     36   \n",
       "2  {u'QP': [0, 0, 0, 100], u'AC': [0], u'AF': [0....     0.0     90   \n",
       "3  {u'QP': [0, 0, 100, 0], u'AC': [2], u'AF': [1....     1.0     34   \n",
       "4  {u'QP': [0, 0, 100, 0], u'AC': [0], u'AF': [0....     0.0    116   \n",
       "5  {u'QP': [0, 0, 0, 100], u'AC': [2], u'AF': [1....     1.0    191   \n",
       "6  {u'QP': [0, 0, 100, 0], u'AC': [0], u'AF': [0....     0.0    173   \n",
       "7  {u'QP': [100, 0, 0, 0], u'AC': [2], u'AF': [1....     1.0    114   \n",
       "8  {u'QP': [0, 100, 0, 0], u'AC': [0], u'AF': [0....     0.0     98   \n",
       "9  {u'QP': [0, 0, 0, 100], u'AC': [2], u'AF': [1....     1.0    211   \n",
       "\n",
       "         tag population patient_id         gene_id gene_coord  \\\n",
       "0  ERR369703     CASALI       P251          Rv0667       1333   \n",
       "1  ERR600658     CASALI       P251          Rv0667       1333   \n",
       "2  ERR369703     CASALI       P251          Rv0768        467   \n",
       "3  ERR600658     CASALI       P251          Rv0768        467   \n",
       "4   Peru4741       CETR       2718          Rv0004        560   \n",
       "5   Peru5134       CETR       2718          Rv0004        560   \n",
       "6   Peru5134       CETR       2718          Rv0006        739   \n",
       "7   Peru4741       CETR       2718          Rv0006        739   \n",
       "8   Peru4741       CETR       2718  Rvnt02_Rv0008c       None   \n",
       "9   Peru5134       CETR       2718  Rvnt02_Rv0008c       None   \n",
       "\n",
       "           gene_category gene_symbol SNP_ftype AA_change  \n",
       "0  Antibiotic Resistance        rpoB      None      None  \n",
       "1  Antibiotic Resistance        rpoB         N     H445D  \n",
       "2          Non-Essential        aldA      None      None  \n",
       "3          Non-Essential        aldA         N     I156S  \n",
       "4          Non-Essential         NaN      None      None  \n",
       "5          Non-Essential         NaN         N     G187V  \n",
       "6  Antibiotic Resistance        gyrA      None      None  \n",
       "7  Antibiotic Resistance        gyrA         N     G247S  \n",
       "8                   None        None         I      None  \n",
       "9                   None        None         I      None  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Base_Call_variants_btwn_isolates_big_change_in_alt_AF.head(n = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61538, 18)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(Base_Call_variants_btwn_isolates_big_change_in_alt_AF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Re-Shape Filtered DataFrame (Paired Base Calls across all isolate pairs) to store one entry per SNP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "SNP_variants_between_paired_isolates = pd.DataFrame()\n",
    "\n",
    "#common information to both Base Calls (can just look at isolate A)\n",
    "population_dict = {}\n",
    "patient_id_dict = {}\n",
    "ref_position_dict = {}\n",
    "ref_allele_dict = {}\n",
    "gene_id_dict  = {}\n",
    "genomic_coord_dict = {}\n",
    "gene_category_dict = {}\n",
    "gene_symbol_dict = {}\n",
    "\n",
    "#look at info for both Base Calls\n",
    "alt_allele_dict = {}\n",
    "alt_AF_diff_dict = {}\n",
    "SNP_type_dict = {}\n",
    "AA_change_dict = {}\n",
    "\n",
    "SNP_index = 0\n",
    "#iterate through indices for isolate A (store common information for isolate pair A & B came from and Base Call), calculate different in Alternate Allele Frequencies, store Syn or NSyn info\n",
    "for even_index in range(0 , np.shape(Base_Call_variants_btwn_isolates_big_change_in_alt_AF)[0] , 2):\n",
    "    \n",
    "    #Base Call info for isolate A\n",
    "    Base_Call_info_isolate_A = Base_Call_variants_btwn_isolates_big_change_in_alt_AF.loc[even_index , :]\n",
    "    #Base Call info for isolate B\n",
    "    Base_Call_info_isolate_B = Base_Call_variants_btwn_isolates_big_change_in_alt_AF.loc[even_index+1 , :]\n",
    "    \n",
    "    population_dict[SNP_index] = Base_Call_info_isolate_A.population\n",
    "    patient_id_dict[SNP_index] = Base_Call_info_isolate_A.patient_id\n",
    "    ref_position_dict[SNP_index] = Base_Call_info_isolate_A.ref_position\n",
    "    ref_allele_dict[SNP_index] = Base_Call_info_isolate_A.ref_base\n",
    "    gene_id_dict[SNP_index] = Base_Call_info_isolate_A.gene_id\n",
    "    genomic_coord_dict[SNP_index] = Base_Call_info_isolate_A.gene_coord\n",
    "    gene_category_dict[SNP_index] = Base_Call_info_isolate_A.gene_category\n",
    "    gene_symbol_dict[SNP_index] = Base_Call_info_isolate_A.gene_symbol\n",
    "    \n",
    "    #get alternate allele\n",
    "    alt_allele_calls = [Base_Call_info_isolate_A.alt_base , Base_Call_info_isolate_B.alt_base]\n",
    "    try:\n",
    "        alt_allele_calls.remove('Z')\n",
    "    except ValueError:\n",
    "        pass\n",
    "    alt_allele_dict[SNP_index] = alt_allele_calls[0]\n",
    "\n",
    "    #get difference in Alternate Allele Frequencies\n",
    "    alt_AF_diff_dict[SNP_index] = abs(Base_Call_info_isolate_A.alt_AF - Base_Call_info_isolate_B.alt_AF)\n",
    "    \n",
    "    #get type of SNP \n",
    "    if 'S' in [Base_Call_info_isolate_A.SNP_ftype , Base_Call_info_isolate_B.SNP_ftype]:\n",
    "        SNP_type_dict[SNP_index] = 'S'\n",
    "        \n",
    "    elif 'N' in [Base_Call_info_isolate_A.SNP_ftype , Base_Call_info_isolate_B.SNP_ftype]:\n",
    "        SNP_type_dict[SNP_index] = 'N'\n",
    "        \n",
    "    elif 'I' in [Base_Call_info_isolate_A.SNP_ftype , Base_Call_info_isolate_B.SNP_ftype]:\n",
    "        SNP_type_dict[SNP_index] = 'I'\n",
    "        \n",
    "    #get AA change\n",
    "    AA_change_calls = [Base_Call_info_isolate_A.AA_change , Base_Call_info_isolate_B.AA_change]\n",
    "    try:\n",
    "        AA_change_calls.remove('None')\n",
    "    except ValueError:\n",
    "        pass\n",
    "    AA_change_dict[SNP_index] = AA_change_calls[0]\n",
    "        \n",
    "    SNP_index += 1\n",
    "    \n",
    "#convert dictionaries into series\n",
    "population = pd.Series(population_dict)\n",
    "patient_id = pd.Series(patient_id_dict)\n",
    "ref_position = pd.Series(ref_position_dict)\n",
    "ref_allele = pd.Series(ref_allele_dict)\n",
    "alt_allele = pd.Series(alt_allele_dict)\n",
    "gene_id = pd.Series(gene_id_dict)\n",
    "genomic_coord = pd.Series(genomic_coord_dict)\n",
    "gene_category = pd.Series(gene_category_dict)\n",
    "gene_symbol = pd.Series(gene_symbol_dict)\n",
    "alt_AF_diff = pd.Series(alt_AF_diff_dict)\n",
    "SNP_type = pd.Series(SNP_type_dict)\n",
    "AA_change = pd.Series(AA_change_dict)\n",
    "        \n",
    "#create DataFrame\n",
    "SNP_variants_between_paired_isolates['population'] = population\n",
    "SNP_variants_between_paired_isolates['patient_id'] = patient_id\n",
    "SNP_variants_between_paired_isolates['ref_position'] = ref_position \n",
    "SNP_variants_between_paired_isolates['ref_allele'] = ref_allele\n",
    "SNP_variants_between_paired_isolates['alt_allele'] = alt_allele\n",
    "SNP_variants_between_paired_isolates['gene_id'] = gene_id\n",
    "SNP_variants_between_paired_isolates['genomic_coord'] = genomic_coord\n",
    "SNP_variants_between_paired_isolates['gene_category'] = gene_category\n",
    "SNP_variants_between_paired_isolates['gene_symbol'] = gene_symbol\n",
    "SNP_variants_between_paired_isolates['alt_AF_diff'] = alt_AF_diff\n",
    "SNP_variants_between_paired_isolates['SNP_type'] = SNP_type\n",
    "SNP_variants_between_paired_isolates['AA_change'] = AA_change"
   ]
  },
  {
   "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>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>1.0</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>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>I156S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CETR</td>\n",
       "      <td>2718</td>\n",
       "      <td>4993</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>Rv0004</td>\n",
       "      <td>560</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>G187V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CETR</td>\n",
       "      <td>2718</td>\n",
       "      <td>8040</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv0006</td>\n",
       "      <td>739</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>gyrA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>G247S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CETR</td>\n",
       "      <td>2718</td>\n",
       "      <td>11370</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Rvnt02_Rv0008c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.0</td>\n",
       "      <td>I</td>\n",
       "      <td>None</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       CETR       2718          4993          G          T          Rv0004   \n",
       "3       CETR       2718          8040          G          A          Rv0006   \n",
       "4       CETR       2718         11370          C          T  Rvnt02_Rv0008c   \n",
       "\n",
       "  genomic_coord          gene_category gene_symbol  alt_AF_diff SNP_type  \\\n",
       "0          1333  Antibiotic Resistance        rpoB          1.0        N   \n",
       "1           467          Non-Essential        aldA          1.0        N   \n",
       "2           560          Non-Essential         NaN          1.0        N   \n",
       "3           739  Antibiotic Resistance        gyrA          1.0        N   \n",
       "4          None                   None        None          1.0        I   \n",
       "\n",
       "  AA_change  \n",
       "0     H445D  \n",
       "1     I156S  \n",
       "2     G187V  \n",
       "3     G247S  \n",
       "4      None  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SNP_variants_between_paired_isolates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30769, 12)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(SNP_variants_between_paired_isolates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KPS_2         1723\n",
       "KPS_50        1602\n",
       "KPS_73        1594\n",
       "KPS_29        1554\n",
       "3018C         1153\n",
       "KPS_30        1116\n",
       "P000035       1097\n",
       "3430          1096\n",
       "3439          1091\n",
       "KPS_36        1089\n",
       "3100U         1088\n",
       "3103J         1086\n",
       "KPS_64        1051\n",
       "KPS_70        1009\n",
       "KPS_31         861\n",
       "KPS_67         844\n",
       "4085U          788\n",
       "1682           780\n",
       "I0004240-3     778\n",
       "2492           756\n",
       "2718           748\n",
       "KPS_59         726\n",
       "KPS_18         690\n",
       "KPS_56         667\n",
       "4078X          646\n",
       "KPS_52         568\n",
       "1620           563\n",
       "4              530\n",
       "I0004304-7     524\n",
       "I0000221-7     436\n",
       "I0004172-8     277\n",
       "15             274\n",
       "KPS_11         255\n",
       "1657           237\n",
       "B              228\n",
       "3725           207\n",
       "1678           180\n",
       "I0003710-6     174\n",
       "KPS_80         137\n",
       "KPS_46         125\n",
       "I0001031-9      98\n",
       "KPS_41          93\n",
       "2047T           46\n",
       "1941            19\n",
       "P000155          7\n",
       "KPS_79           7\n",
       "KPS_10           7\n",
       "P000227          6\n",
       "2511             6\n",
       "P052             6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isolate_pairs = list(set( sample_annotation.index ))\n",
    "\n",
    "num_fSNPs_per_isolate_pair = {}\n",
    "for isolate_pair in isolate_pairs:\n",
    "    \n",
    "    num_fSNPs_per_isolate_pair[isolate_pair] = np.shape(SNP_variants_between_paired_isolates[SNP_variants_between_paired_isolates.patient_id == isolate_pair])[0]\n",
    "    \n",
    "#convert dict to series\n",
    "num_fSNPs_per_isolate_pair = pd.Series(num_fSNPs_per_isolate_pair)\n",
    "\n",
    "#sort values\n",
    "num_fSNPs_per_isolate_pair.sort_values(ascending = False , inplace = True)\n",
    "\n",
    "num_fSNPs_per_isolate_pair.head(n=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copy series for down-stream analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_fSNPs_per_longitudinal_isolate_pair = num_fSNPs_per_isolate_pair.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Subset* to the isolate pairs that we're going to drop (at threshold of > 7 SNPs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among subjects that we're going to drop (Reinfections) what is the median fSNP distance and IQR?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "708.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_fSNPs_per_isolate_pair[num_fSNPs_per_isolate_pair > 7].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250.5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_fSNPs_per_isolate_pair[num_fSNPs_per_isolate_pair > 7].quantile(0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1086.5"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_fSNPs_per_isolate_pair[num_fSNPs_per_isolate_pair > 7].quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "isolate_pair_IDs_for_dropped_isolates = list( set( list( num_fSNPs_per_isolate_pair[num_fSNPs_per_isolate_pair > 7].index ) ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2718', 'KPS_46', 'KPS_41', '3103J', '4085U', 'KPS_80', 'I0004304-7', '3439', 'KPS_11', '3430', 'KPS_2', '1941', 'KPS_56', '1682', 'I0000221-7', '4', '1678', 'KPS_36', 'KPS_30', 'KPS_31', '4078X', 'B', 'KPS_59', 'KPS_70', 'KPS_73', 'KPS_52', 'KPS_50', 'I0001031-9', '3725', 'KPS_18', '1620', '15', 'I0004240-3', 'I0004172-8', '2492', '1657', 'KPS_67', '3100U', 'KPS_29', 'KPS_64', '3018C', '2047T', 'I0003710-6', 'P000035']\n"
     ]
    }
   ],
   "source": [
    "print isolate_pair_IDs_for_dropped_isolates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(isolate_pair_IDs_for_dropped_isolates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>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",
       "    <tr>\n",
       "      <th>patient_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4078X</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR17...</td>\n",
       "      <td>BRYANT</td>\n",
       "      <td>ERR171141</td>\n",
       "      <td>201366.0</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR171141</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4078X</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR96...</td>\n",
       "      <td>BRYANT</td>\n",
       "      <td>ERR966619</td>\n",
       "      <td>200253.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR966619</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2047T</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR10...</td>\n",
       "      <td>BRYANT</td>\n",
       "      <td>ERR108140</td>\n",
       "      <td>402106.0</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR108140</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2047T</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR96...</td>\n",
       "      <td>BRYANT</td>\n",
       "      <td>ERR966621</td>\n",
       "      <td>401063.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR966621</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4085U</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR17...</td>\n",
       "      <td>BRYANT</td>\n",
       "      <td>ERR171137</td>\n",
       "      <td>201113.0</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR171137</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3018C</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR10...</td>\n",
       "      <td>BRYANT</td>\n",
       "      <td>ERR108143</td>\n",
       "      <td>300191.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR108143</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3103J</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR10...</td>\n",
       "      <td>BRYANT</td>\n",
       "      <td>ERR108153</td>\n",
       "      <td>401594.0</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR108153</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4085U</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR17...</td>\n",
       "      <td>BRYANT</td>\n",
       "      <td>ERR171136</td>\n",
       "      <td>200470.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR171136</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3100U</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR10...</td>\n",
       "      <td>BRYANT</td>\n",
       "      <td>ERR108152</td>\n",
       "      <td>300659.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR108152</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3100U</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR10...</td>\n",
       "      <td>BRYANT</td>\n",
       "      <td>ERR108151</td>\n",
       "      <td>302642.0</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR108151</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3103J</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR10...</td>\n",
       "      <td>BRYANT</td>\n",
       "      <td>ERR108154</td>\n",
       "      <td>400554.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR108154</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3018C</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR10...</td>\n",
       "      <td>BRYANT</td>\n",
       "      <td>ERR108144</td>\n",
       "      <td>302235.0</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR108144</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMUQ00000000</td>\n",
       "      <td>Peru5037</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru5037</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMUR00000000</td>\n",
       "      <td>Peru5036</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru5036</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I0003710-6</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMBH00000000</td>\n",
       "      <td>Peru4104</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru4104</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I0004240-3</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MQHZ00000000</td>\n",
       "      <td>Peru3381</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru3381</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I0003710-6</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MQJB00000000</td>\n",
       "      <td>Peru3370</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru3370</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I0004240-3</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MQCK00000000</td>\n",
       "      <td>Peru2963</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru2963</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2718</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MRDO00000000</td>\n",
       "      <td>Peru5134</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru5134</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1620</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMVG00000000</td>\n",
       "      <td>Peru5021</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru5021</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I0000221-7</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MQFV00000000</td>\n",
       "      <td>Peru3424</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru3424</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3725</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMHW00000000</td>\n",
       "      <td>Peru5153</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru5153</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3725</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMTC00000000</td>\n",
       "      <td>Peru4553</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru4553</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I0001031-9</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>CP018304</td>\n",
       "      <td>Peru3377</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru3377</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I0001031-9</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MQCR00000000</td>\n",
       "      <td>Peru2970</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru2970</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1620</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMUN00000000</td>\n",
       "      <td>Peru4516</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru4516</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I0000221-7</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MQLO00000000</td>\n",
       "      <td>Peru3312</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru3312</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I0004172-8</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MQCX00000000</td>\n",
       "      <td>Peru2976</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru2976</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1682</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMPS00000000</td>\n",
       "      <td>Peru4652</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru4652</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2718</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMGA00000000</td>\n",
       "      <td>Peru4741</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru4741</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I0004304-7</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MQEY00000000</td>\n",
       "      <td>Peru3055</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru3055</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I0004304-7</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MQJA00000000</td>\n",
       "      <td>Peru3379</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru3379</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3439</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMJZ00000000</td>\n",
       "      <td>Peru5090</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru5090</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1682</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMUP00000000</td>\n",
       "      <td>Peru5038</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru5038</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMUB00000000</td>\n",
       "      <td>Peru4528</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru4528</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMUC00000000</td>\n",
       "      <td>Peru4527</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru4527</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3439</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMIC00000000</td>\n",
       "      <td>Peru5147</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru5147</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3430</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMID00000000</td>\n",
       "      <td>Peru5146</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru5146</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2492</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMIN00000000</td>\n",
       "      <td>Peru5128</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru5128</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2492</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMKR00000000</td>\n",
       "      <td>Peru5072</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru5072</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMJC00000000</td>\n",
       "      <td>Peru5113</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru5113</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMPF00000000</td>\n",
       "      <td>Peru4665</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru4665</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I0004172-8</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MQIO00000000</td>\n",
       "      <td>Peru3373</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru3373</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3430</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMKB00000000</td>\n",
       "      <td>Peru5088</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru5088</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_67</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR18...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR181680</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR181680</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_18</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR18...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR181818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR181818</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_18</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR03...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR037517</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR037517</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_67</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR21...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR216962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR216962</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_29</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR176461</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR176461</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_29</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR16...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR163962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR163962</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_50</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR24...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR245654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR245654</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_50</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR18...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR181877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR181877</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_31</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR77...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR773799</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR773799</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_52</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR03...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR037483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR037483</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_46</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR176649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR176649</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_46</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR19...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR190334</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR190334</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_41</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR176764</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR176764</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_41</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR176535</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR176535</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_80</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR18...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR182056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR182056</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_80</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR12...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR124649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR124649</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_11</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR21...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR216977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR216977</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_11</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR47...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR473347</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR473347</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_2</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR21...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR212067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR212067</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_2</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR77...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR773785</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR773785</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_56</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR24...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR245774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR245774</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_52</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR21...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR212030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR212030</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_56</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR18...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR181868</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR181868</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_36</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR176646</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR176646</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_30</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR176598</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR176598</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_30</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR12...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR126598</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR126598</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_31</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR176735</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR176735</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_64</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR176638</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR176638</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_59</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR16...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR164014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR164014</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_59</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR47...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR473286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR473286</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_70</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR176623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR176623</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_70</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR176794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR176794</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_73</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR21...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR212090</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR212090</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_73</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR32...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR323049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR323049</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_36</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR176575</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR176575</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPS_64</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR18...</td>\n",
       "      <td>GUERRA</td>\n",
       "      <td>ERR181965</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR181965</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P000035</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/walker/ERR07...</td>\n",
       "      <td>WALKER</td>\n",
       "      <td>ERR072072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR072072</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P000035</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/walker/ERR04...</td>\n",
       "      <td>WALKER</td>\n",
       "      <td>ERR046783</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR046783</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/witney/ERR17...</td>\n",
       "      <td>WITNEY</td>\n",
       "      <td>ERR1750901</td>\n",
       "      <td>SAMEA23761168</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR1750901</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/witney/ERR17...</td>\n",
       "      <td>WITNEY</td>\n",
       "      <td>ERR1750880</td>\n",
       "      <td>SAMEA23745418</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR1750880</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/witney/ERR17...</td>\n",
       "      <td>WITNEY</td>\n",
       "      <td>ERR1750879</td>\n",
       "      <td>SAMEA23744668</td>\n",
       "      <td>1</td>\n",
       "      <td>ERR1750879</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/witney/ERR17...</td>\n",
       "      <td>WITNEY</td>\n",
       "      <td>ERR1750902</td>\n",
       "      <td>SAMEA23761918</td>\n",
       "      <td>2</td>\n",
       "      <td>ERR1750902</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/xu/SRR374266...</td>\n",
       "      <td>XU</td>\n",
       "      <td>SRR3742666</td>\n",
       "      <td>SAMN05328085</td>\n",
       "      <td>1</td>\n",
       "      <td>SRR3742666</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/xu/SRR374266...</td>\n",
       "      <td>XU</td>\n",
       "      <td>SRR3742668</td>\n",
       "      <td>SAMN05328087</td>\n",
       "      <td>2</td>\n",
       "      <td>SRR3742668</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  fastq_files population  \\\n",
       "patient_id                                                                 \n",
       "4078X       /n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR17...     BRYANT   \n",
       "4078X       /n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR96...     BRYANT   \n",
       "2047T       /n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR10...     BRYANT   \n",
       "2047T       /n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR96...     BRYANT   \n",
       "4085U       /n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR17...     BRYANT   \n",
       "3018C       /n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR10...     BRYANT   \n",
       "3103J       /n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR10...     BRYANT   \n",
       "4085U       /n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR17...     BRYANT   \n",
       "3100U       /n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR10...     BRYANT   \n",
       "3100U       /n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR10...     BRYANT   \n",
       "3103J       /n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR10...     BRYANT   \n",
       "3018C       /n/data1/hms/dbmi/farhat/fastq_db/bryant/ERR10...     BRYANT   \n",
       "1678        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "1678        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "I0003710-6  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "I0004240-3  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "I0003710-6  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "I0004240-3  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "2718        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "1620        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "I0000221-7  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "3725        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "3725        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "I0001031-9  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "I0001031-9  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "1620        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "I0000221-7  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "I0004172-8  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "1682        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "2718        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "I0004304-7  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "I0004304-7  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "3439        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "1682        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "1657        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "1657        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "3439        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "3430        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "2492        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "2492        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "1941        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "1941        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "I0004172-8  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "3430        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "KPS_67      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR18...     GUERRA   \n",
       "KPS_18      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR18...     GUERRA   \n",
       "KPS_18      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR03...     GUERRA   \n",
       "KPS_67      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR21...     GUERRA   \n",
       "KPS_29      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...     GUERRA   \n",
       "KPS_29      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR16...     GUERRA   \n",
       "KPS_50      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR24...     GUERRA   \n",
       "KPS_50      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR18...     GUERRA   \n",
       "KPS_31      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR77...     GUERRA   \n",
       "KPS_52      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR03...     GUERRA   \n",
       "KPS_46      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...     GUERRA   \n",
       "KPS_46      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR19...     GUERRA   \n",
       "KPS_41      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...     GUERRA   \n",
       "KPS_41      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...     GUERRA   \n",
       "KPS_80      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR18...     GUERRA   \n",
       "KPS_80      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR12...     GUERRA   \n",
       "KPS_11      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR21...     GUERRA   \n",
       "KPS_11      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR47...     GUERRA   \n",
       "KPS_2       /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR21...     GUERRA   \n",
       "KPS_2       /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR77...     GUERRA   \n",
       "KPS_56      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR24...     GUERRA   \n",
       "KPS_52      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR21...     GUERRA   \n",
       "KPS_56      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR18...     GUERRA   \n",
       "KPS_36      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...     GUERRA   \n",
       "KPS_30      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...     GUERRA   \n",
       "KPS_30      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR12...     GUERRA   \n",
       "KPS_31      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...     GUERRA   \n",
       "KPS_64      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...     GUERRA   \n",
       "KPS_59      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR16...     GUERRA   \n",
       "KPS_59      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR47...     GUERRA   \n",
       "KPS_70      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...     GUERRA   \n",
       "KPS_70      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...     GUERRA   \n",
       "KPS_73      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR21...     GUERRA   \n",
       "KPS_73      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR32...     GUERRA   \n",
       "KPS_36      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR17...     GUERRA   \n",
       "KPS_64      /n/data1/hms/dbmi/farhat/fastq_db/guerra/ERR18...     GUERRA   \n",
       "P000035     /n/data1/hms/dbmi/farhat/fastq_db/walker/ERR07...     WALKER   \n",
       "P000035     /n/data1/hms/dbmi/farhat/fastq_db/walker/ERR04...     WALKER   \n",
       "15          /n/data1/hms/dbmi/farhat/fastq_db/witney/ERR17...     WITNEY   \n",
       "4           /n/data1/hms/dbmi/farhat/fastq_db/witney/ERR17...     WITNEY   \n",
       "4           /n/data1/hms/dbmi/farhat/fastq_db/witney/ERR17...     WITNEY   \n",
       "15          /n/data1/hms/dbmi/farhat/fastq_db/witney/ERR17...     WITNEY   \n",
       "B           /n/data1/hms/dbmi/farhat/fastq_db/xu/SRR374266...         XU   \n",
       "B           /n/data1/hms/dbmi/farhat/fastq_db/xu/SRR374266...         XU   \n",
       "\n",
       "                  run_ID      sample_ID  sample_order         tag  \\\n",
       "patient_id                                                          \n",
       "4078X          ERR171141       201366.0             2   ERR171141   \n",
       "4078X          ERR966619       200253.0             1   ERR966619   \n",
       "2047T          ERR108140       402106.0             2   ERR108140   \n",
       "2047T          ERR966621       401063.0             1   ERR966621   \n",
       "4085U          ERR171137       201113.0             2   ERR171137   \n",
       "3018C          ERR108143       300191.0             1   ERR108143   \n",
       "3103J          ERR108153       401594.0             2   ERR108153   \n",
       "4085U          ERR171136       200470.0             1   ERR171136   \n",
       "3100U          ERR108152       300659.0             1   ERR108152   \n",
       "3100U          ERR108151       302642.0             2   ERR108151   \n",
       "3103J          ERR108154       400554.0             1   ERR108154   \n",
       "3018C          ERR108144       302235.0             2   ERR108144   \n",
       "1678        MMUQ00000000       Peru5037             2    Peru5037   \n",
       "1678        MMUR00000000       Peru5036             1    Peru5036   \n",
       "I0003710-6  MMBH00000000       Peru4104             1    Peru4104   \n",
       "I0004240-3  MQHZ00000000       Peru3381             2    Peru3381   \n",
       "I0003710-6  MQJB00000000       Peru3370             2    Peru3370   \n",
       "I0004240-3  MQCK00000000       Peru2963             1    Peru2963   \n",
       "2718        MRDO00000000       Peru5134             1    Peru5134   \n",
       "1620        MMVG00000000       Peru5021             1    Peru5021   \n",
       "I0000221-7  MQFV00000000       Peru3424             2    Peru3424   \n",
       "3725        MMHW00000000       Peru5153             2    Peru5153   \n",
       "3725        MMTC00000000       Peru4553             1    Peru4553   \n",
       "I0001031-9      CP018304       Peru3377             2    Peru3377   \n",
       "I0001031-9  MQCR00000000       Peru2970             1    Peru2970   \n",
       "1620        MMUN00000000       Peru4516             2    Peru4516   \n",
       "I0000221-7  MQLO00000000       Peru3312             1    Peru3312   \n",
       "I0004172-8  MQCX00000000       Peru2976             1    Peru2976   \n",
       "1682        MMPS00000000       Peru4652             1    Peru4652   \n",
       "2718        MMGA00000000       Peru4741             2    Peru4741   \n",
       "I0004304-7  MQEY00000000       Peru3055             1    Peru3055   \n",
       "I0004304-7  MQJA00000000       Peru3379             2    Peru3379   \n",
       "3439        MMJZ00000000       Peru5090             1    Peru5090   \n",
       "1682        MMUP00000000       Peru5038             2    Peru5038   \n",
       "1657        MMUB00000000       Peru4528             2    Peru4528   \n",
       "1657        MMUC00000000       Peru4527             1    Peru4527   \n",
       "3439        MMIC00000000       Peru5147             2    Peru5147   \n",
       "3430        MMID00000000       Peru5146             1    Peru5146   \n",
       "2492        MMIN00000000       Peru5128             2    Peru5128   \n",
       "2492        MMKR00000000       Peru5072             1    Peru5072   \n",
       "1941        MMJC00000000       Peru5113             1    Peru5113   \n",
       "1941        MMPF00000000       Peru4665             2    Peru4665   \n",
       "I0004172-8  MQIO00000000       Peru3373             2    Peru3373   \n",
       "3430        MMKB00000000       Peru5088             2    Peru5088   \n",
       "KPS_67         ERR181680            NaN             1   ERR181680   \n",
       "KPS_18         ERR181818            NaN             2   ERR181818   \n",
       "KPS_18         ERR037517            NaN             1   ERR037517   \n",
       "KPS_67         ERR216962            NaN             2   ERR216962   \n",
       "KPS_29         ERR176461            NaN             1   ERR176461   \n",
       "KPS_29         ERR163962            NaN             2   ERR163962   \n",
       "KPS_50         ERR245654            NaN             2   ERR245654   \n",
       "KPS_50         ERR181877            NaN             1   ERR181877   \n",
       "KPS_31         ERR773799            NaN             2   ERR773799   \n",
       "KPS_52         ERR037483            NaN             1   ERR037483   \n",
       "KPS_46         ERR176649            NaN             1   ERR176649   \n",
       "KPS_46         ERR190334            NaN             2   ERR190334   \n",
       "KPS_41         ERR176764            NaN             1   ERR176764   \n",
       "KPS_41         ERR176535            NaN             2   ERR176535   \n",
       "KPS_80         ERR182056            NaN             1   ERR182056   \n",
       "KPS_80         ERR124649            NaN             2   ERR124649   \n",
       "KPS_11         ERR216977            NaN             1   ERR216977   \n",
       "KPS_11         ERR473347            NaN             2   ERR473347   \n",
       "KPS_2          ERR212067            NaN             1   ERR212067   \n",
       "KPS_2          ERR773785            NaN             2   ERR773785   \n",
       "KPS_56         ERR245774            NaN             1   ERR245774   \n",
       "KPS_52         ERR212030            NaN             2   ERR212030   \n",
       "KPS_56         ERR181868            NaN             2   ERR181868   \n",
       "KPS_36         ERR176646            NaN             2   ERR176646   \n",
       "KPS_30         ERR176598            NaN             1   ERR176598   \n",
       "KPS_30         ERR126598            NaN             2   ERR126598   \n",
       "KPS_31         ERR176735            NaN             1   ERR176735   \n",
       "KPS_64         ERR176638            NaN             1   ERR176638   \n",
       "KPS_59         ERR164014            NaN             1   ERR164014   \n",
       "KPS_59         ERR473286            NaN             2   ERR473286   \n",
       "KPS_70         ERR176623            NaN             1   ERR176623   \n",
       "KPS_70         ERR176794            NaN             2   ERR176794   \n",
       "KPS_73         ERR212090            NaN             1   ERR212090   \n",
       "KPS_73         ERR323049            NaN             2   ERR323049   \n",
       "KPS_36         ERR176575            NaN             1   ERR176575   \n",
       "KPS_64         ERR181965            NaN             2   ERR181965   \n",
       "P000035        ERR072072            NaN             2   ERR072072   \n",
       "P000035        ERR046783            NaN             1   ERR046783   \n",
       "15            ERR1750901  SAMEA23761168             1  ERR1750901   \n",
       "4             ERR1750880  SAMEA23745418             2  ERR1750880   \n",
       "4             ERR1750879  SAMEA23744668             1  ERR1750879   \n",
       "15            ERR1750902  SAMEA23761918             2  ERR1750902   \n",
       "B             SRR3742666   SAMN05328085             1  SRR3742666   \n",
       "B             SRR3742668   SAMN05328087             2  SRR3742668   \n",
       "\n",
       "            isolate_type  \n",
       "patient_id                \n",
       "4078X       longitudinal  \n",
       "4078X       longitudinal  \n",
       "2047T       longitudinal  \n",
       "2047T       longitudinal  \n",
       "4085U       longitudinal  \n",
       "3018C       longitudinal  \n",
       "3103J       longitudinal  \n",
       "4085U       longitudinal  \n",
       "3100U       longitudinal  \n",
       "3100U       longitudinal  \n",
       "3103J       longitudinal  \n",
       "3018C       longitudinal  \n",
       "1678        longitudinal  \n",
       "1678        longitudinal  \n",
       "I0003710-6  longitudinal  \n",
       "I0004240-3  longitudinal  \n",
       "I0003710-6  longitudinal  \n",
       "I0004240-3  longitudinal  \n",
       "2718        longitudinal  \n",
       "1620        longitudinal  \n",
       "I0000221-7  longitudinal  \n",
       "3725        longitudinal  \n",
       "3725        longitudinal  \n",
       "I0001031-9  longitudinal  \n",
       "I0001031-9  longitudinal  \n",
       "1620        longitudinal  \n",
       "I0000221-7  longitudinal  \n",
       "I0004172-8  longitudinal  \n",
       "1682        longitudinal  \n",
       "2718        longitudinal  \n",
       "I0004304-7  longitudinal  \n",
       "I0004304-7  longitudinal  \n",
       "3439        longitudinal  \n",
       "1682        longitudinal  \n",
       "1657        longitudinal  \n",
       "1657        longitudinal  \n",
       "3439        longitudinal  \n",
       "3430        longitudinal  \n",
       "2492        longitudinal  \n",
       "2492        longitudinal  \n",
       "1941        longitudinal  \n",
       "1941        longitudinal  \n",
       "I0004172-8  longitudinal  \n",
       "3430        longitudinal  \n",
       "KPS_67      longitudinal  \n",
       "KPS_18      longitudinal  \n",
       "KPS_18      longitudinal  \n",
       "KPS_67      longitudinal  \n",
       "KPS_29      longitudinal  \n",
       "KPS_29      longitudinal  \n",
       "KPS_50      longitudinal  \n",
       "KPS_50      longitudinal  \n",
       "KPS_31      longitudinal  \n",
       "KPS_52      longitudinal  \n",
       "KPS_46      longitudinal  \n",
       "KPS_46      longitudinal  \n",
       "KPS_41      longitudinal  \n",
       "KPS_41      longitudinal  \n",
       "KPS_80      longitudinal  \n",
       "KPS_80      longitudinal  \n",
       "KPS_11      longitudinal  \n",
       "KPS_11      longitudinal  \n",
       "KPS_2       longitudinal  \n",
       "KPS_2       longitudinal  \n",
       "KPS_56      longitudinal  \n",
       "KPS_52      longitudinal  \n",
       "KPS_56      longitudinal  \n",
       "KPS_36      longitudinal  \n",
       "KPS_30      longitudinal  \n",
       "KPS_30      longitudinal  \n",
       "KPS_31      longitudinal  \n",
       "KPS_64      longitudinal  \n",
       "KPS_59      longitudinal  \n",
       "KPS_59      longitudinal  \n",
       "KPS_70      longitudinal  \n",
       "KPS_70      longitudinal  \n",
       "KPS_73      longitudinal  \n",
       "KPS_73      longitudinal  \n",
       "KPS_36      longitudinal  \n",
       "KPS_64      longitudinal  \n",
       "P000035     longitudinal  \n",
       "P000035     longitudinal  \n",
       "15          longitudinal  \n",
       "4           longitudinal  \n",
       "4           longitudinal  \n",
       "15          longitudinal  \n",
       "B           longitudinal  \n",
       "B           longitudinal  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
    "    display(sample_annotation.loc[isolate_pair_IDs_for_dropped_isolates , :].sort_values('population'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'BRYANT': 12,\n",
       "         'CETR': 32,\n",
       "         'GUERRA': 36,\n",
       "         'WALKER': 2,\n",
       "         'WITNEY': 4,\n",
       "         'XU': 2})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(list(sample_annotation.loc[isolate_pair_IDs_for_dropped_isolates , :].population))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of pairwise distances across isolate pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SNP_diff_threshold = 3\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['lines.linewidth']=3\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': 14})\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "#plot all pairwise comparisons\n",
    "n , bins , patches = plt.hist(num_fSNPs_per_isolate_pair , bins = 45 , color = 'black' , rwidth = 0.75 , edgecolor='black', linewidth = 0.0)\n",
    "\n",
    "plot_padding = 3\n",
    "\n",
    "total_number_of_patients = np.shape( num_fSNPs_per_isolate_pair )[0]\n",
    "\n",
    "plt.ylabel('Number of Longitudinal Pairs' , fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "plt.xlabel('Number of fixed SNPs Between Longitudinal Isolates', fontweight = 'bold', fontsize = 12 , color = 'k')\n",
    "\n",
    "plt.xlim(min(bins)-plot_padding , max(bins)+plot_padding)\n",
    "plt.ylim(min(n) , max(n)+3)\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(8.0, 5)\n",
    "fig.tight_layout()\n",
    "\n",
    "#file_name = '/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/figures/Filtering_by_fSNPs_analysis/pairwise_fSNP_distance_between_longitudinal_isolates.pdf'\n",
    "#plt.savefig(file_name, bbox_inches='tight', dpi = 300 , transparent = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x306 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SNP_diff_threshold = 25\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['lines.linewidth']=3\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})\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "#plot all pairwise comparison\n",
    "n , bins , patches = plt.hist( num_fSNPs_per_isolate_pair[num_fSNPs_per_isolate_pair <= SNP_diff_threshold] , bins = np.arange(0 , SNP_diff_threshold + 1) , color = 'black' , rwidth = 0.7 , edgecolor='black', linewidth=0.0 , align = 'left')\n",
    "\n",
    "plot_padding = 1.0\n",
    "\n",
    "total_number_of_patients = np.shape( num_fSNPs_per_isolate_pair )[0]\n",
    "\n",
    "plt.xlim(min(bins)-plot_padding , max(bins)-plot_padding)\n",
    "plt.ylim(min(n) , max(n)+1)\n",
    "\n",
    "\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "ax.tick_params(labelcolor = 'k')\n",
    "\n",
    "ax.set_xticks(bins[0:-1][0:len(bins[0:-1]):2]) #xtick positions\n",
    "ax.set_xticklabels([str(int(x)) for x in bins[0:][0:len(bins[0:]):2]]) #xtick labels\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(6.5, 4.25)\n",
    "fig.tight_layout()\n",
    "\n",
    "#file_name = '/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/figures/Filtering_by_fSNPs_analysis/pairwise_fSNP_distance_between_longitudinal_isolates_ZOOMED.pdf'\n",
    "#plt.savefig(file_name, bbox_inches='tight', dpi = 300 , transparent = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### *Filter* out Longitudinal Pairs that have > 7 SNPs between them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop mixed isolate pairs from Sample Annotation and export as CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "244\n"
     ]
    }
   ],
   "source": [
    "print np.shape(sample_annotation)[0] / 2 #number of longitudinal isolate pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get list of isolate pairs with a high genetic distance\n",
    "mixed_isolate_pairs = list( num_fSNPs_per_isolate_pair[num_fSNPs_per_isolate_pair > 7].index )\n",
    "\n",
    "#filter out from sample annotation\n",
    "clean_isolate_pairs = list( set(list(sample_annotation.index)) - set(mixed_isolate_pairs) )\n",
    "\n",
    "sample_annotation_clean = sample_annotation.loc[clean_isolate_pairs , :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n"
     ]
    }
   ],
   "source": [
    "print np.shape(sample_annotation_clean)[0] / 2 #number of longitudinal isolate pairs with fSNP distance <= 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export updated sample annotation file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_annotation_clean.to_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": 32,
   "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>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",
       "    <tr>\n",
       "      <th>patient_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P000183</th>\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>P000183</th>\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",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMJA00000000</td>\n",
       "      <td>Peru5115</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru5115</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMPC00000000</td>\n",
       "      <td>Peru4668</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru4668</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2491</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CETR</td>\n",
       "      <td>MMZT00000000</td>\n",
       "      <td>Peru4903</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru4903</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  fastq_files population  \\\n",
       "patient_id                                                                 \n",
       "P000183     /n/data1/hms/dbmi/farhat/fastq_db/walker/ERR03...     WALKER   \n",
       "P000183     /n/data1/hms/dbmi/farhat/fastq_db/walker/ERR03...     WALKER   \n",
       "1960        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "1960        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "2491        /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...       CETR   \n",
       "\n",
       "                  run_ID sample_ID  sample_order        tag  isolate_type  \n",
       "patient_id                                                                 \n",
       "P000183        ERR039337       NaN             1  ERR039337  longitudinal  \n",
       "P000183        ERR039338       NaN             2  ERR039338  longitudinal  \n",
       "1960        MMJA00000000  Peru5115             1   Peru5115  longitudinal  \n",
       "1960        MMPC00000000  Peru4668             2   Peru4668  longitudinal  \n",
       "2491        MMZT00000000  Peru4903             1   Peru4903  longitudinal  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_annotation_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Also Export Sample Annotation to CSV for modifying by inclusion of times between isolate collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rv76/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "sample_annotation_clean.drop(labels = ['fastq_files'] , axis = 1 , inplace = True)\n",
    "sample_annotation_clean_for_times = sample_annotation_clean.copy()\n",
    "sample_annotation_clean_for_times['patient_id'] = list(sample_annotation_clean_for_times.index)\n",
    "sample_annotation_clean_for_times.reset_index(inplace = True , drop = True)\n",
    "sample_annotation_clean_for_times.sort_values(by = ['population' , 'patient_id' , 'sample_order'] , inplace = True)\n",
    "sample_annotation_clean_for_times.to_csv('/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/CSV_files/sample_annotation_files/Longitudinal_sample_annotation_for_temporal_analysis.csv' , sep  = ',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of Lineages (across filtered longitudinal sample pairs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parse & Scrape lineage calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>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",
       "    <tr>\n",
       "      <th>patient_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P000183</th>\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>P000183</th>\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",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>CETR</td>\n",
       "      <td>MMJA00000000</td>\n",
       "      <td>Peru5115</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru5115</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>CETR</td>\n",
       "      <td>MMPC00000000</td>\n",
       "      <td>Peru4668</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru4668</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2491</th>\n",
       "      <td>CETR</td>\n",
       "      <td>MMZT00000000</td>\n",
       "      <td>Peru4903</td>\n",
       "      <td>1</td>\n",
       "      <td>Peru4903</td>\n",
       "      <td>longitudinal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           population        run_ID sample_ID  sample_order        tag  \\\n",
       "patient_id                                                               \n",
       "P000183        WALKER     ERR039337       NaN             1  ERR039337   \n",
       "P000183        WALKER     ERR039338       NaN             2  ERR039338   \n",
       "1960             CETR  MMJA00000000  Peru5115             1   Peru5115   \n",
       "1960             CETR  MMPC00000000  Peru4668             2   Peru4668   \n",
       "2491             CETR  MMZT00000000  Peru4903             1   Peru4903   \n",
       "\n",
       "            isolate_type  \n",
       "patient_id                \n",
       "P000183     longitudinal  \n",
       "P000183     longitudinal  \n",
       "1960        longitudinal  \n",
       "1960        longitudinal  \n",
       "2491        longitudinal  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_annotation_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rv76/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:19: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n"
     ]
    }
   ],
   "source": [
    "#dataframe that stores the lienage calls for each isolate\n",
    "lineage_call_DF = pd.DataFrame(index = range(0 , 2) , columns = ['population' , 'patient_id' , 'tag' , 'lineage_1' , 'lineage_2', 'lineage_3' , 'lineage_4' , 'decision'])\n",
    "\n",
    "\n",
    "for isolate_index in range(0, np.shape(sample_annotation_clean)[0]):\n",
    "\n",
    "    population = sample_annotation_clean.iloc[isolate_index , 0]\n",
    "    patient_id = str(sample_annotation_clean.iloc[isolate_index , :].name)\n",
    "    tag = sample_annotation_clean.iloc[isolate_index , 4] #folder title in JankyPipe output directory\n",
    "    \n",
    "    #lineage_call_file\n",
    "    lineage_call_txt = '/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/JankyPipe/output/' + tag + '/fast-lineage-caller/lineage_call.txt'\n",
    "    \n",
    "    #read in text file that stores the lineage call\n",
    "    f = open(lineage_call_txt,'r')\n",
    "    lineage_call_string = f.readline()\n",
    "\n",
    "    #store population, patient_id and sample_id in DF\n",
    "    lineage_call_DF.ix[isolate_index , 0] = population\n",
    "    lineage_call_DF.ix[isolate_index , 1] = patient_id\n",
    "    lineage_call_DF.ix[isolate_index , 2] = tag\n",
    "\n",
    "    #store full output / decision\n",
    "    lineage_call_DF.ix[isolate_index , 7] = lineage_call_string\n",
    "\n",
    "    for specific_lineage_call in lineage_call_string.split('lineage')[1:]:\n",
    "\n",
    "        specific_lineage_call = specific_lineage_call.replace(',', '')\n",
    "        specific_lineage_call = specific_lineage_call.replace('\\n', '')\n",
    "\n",
    "        #lineage_call_number can be determined by number of periods in lineage call\n",
    "        #if 0 -> lineage call 1, if 1 -> lineage call 2, etc.\n",
    "        lineage_call_number = specific_lineage_call.count('.')\n",
    "\n",
    "        #store lineage_call\n",
    "        lineage_call_DF.ix[isolate_index , 3 + lineage_call_number] = specific_lineage_call\n",
    "                \n",
    "#make the patient_ids the index\n",
    "lineage_call_DF.set_index(keys = 'patient_id' , drop = True , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>tag</th>\n",
       "      <th>lineage_1</th>\n",
       "      <th>lineage_2</th>\n",
       "      <th>lineage_3</th>\n",
       "      <th>lineage_4</th>\n",
       "      <th>decision</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P000183</th>\n",
       "      <td>WALKER</td>\n",
       "      <td>ERR039337</td>\n",
       "      <td>4</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.3.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>--decision: lineage4,lineage4.3,lineage4.3.1\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P000183</th>\n",
       "      <td>WALKER</td>\n",
       "      <td>ERR039338</td>\n",
       "      <td>4</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.3.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>--decision: lineage4,lineage4.3,lineage4.3.1\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>CETR</td>\n",
       "      <td>Peru5115</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>--decision: lineage4\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>CETR</td>\n",
       "      <td>Peru4668</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>--decision: lineage4\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2491</th>\n",
       "      <td>CETR</td>\n",
       "      <td>Peru4903</td>\n",
       "      <td>4</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.3.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>--decision: lineage4,lineage4.3,lineage4.3.3\\n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           population        tag lineage_1 lineage_2 lineage_3 lineage_4  \\\n",
       "patient_id                                                                 \n",
       "P000183        WALKER  ERR039337         4       4.3     4.3.1       NaN   \n",
       "P000183        WALKER  ERR039338         4       4.3     4.3.1       NaN   \n",
       "1960             CETR   Peru5115         4       NaN       NaN       NaN   \n",
       "1960             CETR   Peru4668         4       NaN       NaN       NaN   \n",
       "2491             CETR   Peru4903         4       4.3     4.3.3       NaN   \n",
       "\n",
       "                                                  decision  \n",
       "patient_id                                                  \n",
       "P000183     --decision: lineage4,lineage4.3,lineage4.3.1\\n  \n",
       "P000183     --decision: lineage4,lineage4.3,lineage4.3.1\\n  \n",
       "1960                                --decision: lineage4\\n  \n",
       "1960                                --decision: lineage4\\n  \n",
       "2491        --decision: lineage4,lineage4.3,lineage4.3.3\\n  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lineage_call_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "lineage_call_per_patient = lineage_call_DF.copy()\n",
    "lineage_call_per_patient.loc[: , 'patient_id'] = list( lineage_call_per_patient.index )\n",
    "lineage_call_per_patient.drop_duplicates(subset=['population' , 'patient_id' , 'decision'], keep='first' , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rv76/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:6: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "patient_lineage_DF = lineage_call_per_patient.loc[: , ['population']]\n",
    "lineage_assignments = []\n",
    "                                                       \n",
    "#find most precise lineage for each patient\n",
    "for row_index in range(0 , np.shape(lineage_call_per_patient)[0]):\n",
    "    patient_information = lineage_call_per_patient.ix[row_index , :]\n",
    "    patient_id = patient_information.name\n",
    "    \n",
    "    lineage_call = patient_information.lineage_1 #set as default\n",
    "    for lineage in [patient_information.lineage_2 , patient_information.lineage_3 , patient_information.lineage_4]:\n",
    "                                                       \n",
    "        #check to see if nan value\n",
    "        if pd.isnull(lineage) == False:\n",
    "            lineage_call = lineage\n",
    "        elif pd.isnull(lineage) == True:\n",
    "            break\n",
    "        \n",
    "    lineage_assignments.append(lineage_call)\n",
    "                                                       \n",
    "patient_lineage_DF.loc[: , 'lineage_call'] = lineage_assignments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>lineage_call</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P000183</th>\n",
       "      <td>WALKER</td>\n",
       "      <td>4.3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>CETR</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2491</th>\n",
       "      <td>CETR</td>\n",
       "      <td>4.3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2968</th>\n",
       "      <td>CETR</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020E</th>\n",
       "      <td>BRYANT</td>\n",
       "      <td>2.2.1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           population lineage_call\n",
       "patient_id                        \n",
       "P000183        WALKER        4.3.1\n",
       "1960             CETR            4\n",
       "2491             CETR        4.3.3\n",
       "2968             CETR          4.8\n",
       "2020E          BRYANT      2.2.1.1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_lineage_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "patient_lineage_DF.loc[: , 'overall_lineage_call'] = [lineage_i[0] for lineage_i in list(patient_lineage_DF.lineage_call)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_lineage_calls = set( list( patient_lineage_DF.overall_lineage_call ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1', '3', '2', '5', '4']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list( unique_lineage_calls )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pie_slice_proportions = []\n",
    "labels = []\n",
    "colors = []\n",
    "\n",
    "for lineage_call in unique_lineage_calls:\n",
    "    \n",
    "    proportion = float(list( patient_lineage_DF.overall_lineage_call ).count(lineage_call)) / float(len(list( patient_lineage_DF.overall_lineage_call )))\n",
    "    pie_slice_proportions.append(proportion)\n",
    "    labels.append(lineage_call)\n",
    "    \n",
    "    if lineage_call[0] == '1':\n",
    "        colors.append('xkcd:pink')\n",
    "    if lineage_call[0] == '2':\n",
    "        colors.append('xkcd:bright blue')\n",
    "    if lineage_call[0] == '3':\n",
    "        colors.append('xkcd:purple')\n",
    "    if lineage_call[0] == '4':\n",
    "        colors.append('xkcd:red')\n",
    "    if lineage_call[0] == '5':\n",
    "        colors.append('xkcd:light brown')\n",
    "        \n",
    "# Make square figures and axes\n",
    "the_grid = GridSpec(1, 1)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "wedges, texts = ax.pie(pie_slice_proportions, shadow=False , colors = colors , wedgeprops = {'linewidth': 12 } , labeldistance = 1.1 , startangle = 23)\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(4, 4)\n",
    "fig.tight_layout()\n",
    "\n",
    "for w in wedges:\n",
    "    w.set_linewidth(1.5)\n",
    "    w.set_edgecolor('black')\n",
    "    \n",
    "#for text in texts:\n",
    "#    text.set_rotation(0)\n",
    "\n",
    "file_name = '/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/figures/Sample_Summary/Global_Lineage_Distribution_across_subjects.pdf'\n",
    "plt.savefig(file_name, bbox_inches='tight', dpi = 300 , transparent = True)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patient_popualtion_tags = list( patient_lineage_DF.population )\n",
    "population_IDs = ['CETR' , 'CASALI' , 'WALKER' , 'TRAUNER', 'WITNEY' , 'XU' , 'GUERRA' , 'BRYANT']\n",
    "\n",
    "pie_slice_proportions = []\n",
    "labels = []\n",
    "colors = []\n",
    "sample_sizes = []\n",
    "\n",
    "for population_id in population_IDs:\n",
    "    \n",
    "    proportion = float(list( patient_popualtion_tags ).count(population_id)) / float(len(patient_popualtion_tags))\n",
    "    pie_slice_proportions.append(proportion)\n",
    "    sample_size = str( list( patient_popualtion_tags ).count(population_id) )\n",
    "    \n",
    "    labels.append(population_id + '\\n' + 'N = ' + sample_size)\n",
    "    \n",
    "    \n",
    "    if population_id == 'CETR':\n",
    "        colors.append('xkcd:lipstick red')\n",
    "    if population_id == 'CASALI':\n",
    "        colors.append('xkcd:neon blue')\n",
    "    if population_id == 'WALKER':\n",
    "        colors.append('xkcd:light magenta')\n",
    "    if population_id == 'TRAUNER':\n",
    "        colors.append('xkcd:saffron')\n",
    "    if population_id == 'WITNEY':\n",
    "        colors.append('xkcd:grass green')\n",
    "    if population_id == 'XU':\n",
    "        colors.append('xkcd:bright red')\n",
    "    if population_id == 'GUERRA':\n",
    "        colors.append('xkcd:royal purple')\n",
    "    if population_id == 'BRYANT':\n",
    "        colors.append('xkcd:steel')\n",
    "        \n",
    "# Make square figures and axes\n",
    "\n",
    "the_grid = GridSpec(1, 1)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "wedges, texts = ax.pie(pie_slice_proportions, shadow=False , colors = colors , wedgeprops = {'linewidth': 8 } , labeldistance = 0.50 , startangle = 113)\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(4, 4)\n",
    "fig.tight_layout()\n",
    "\n",
    "for w in wedges:\n",
    "    w.set_linewidth(1.5)\n",
    "    w.set_edgecolor('black')\n",
    "    \n",
    "for text in texts:\n",
    "    text.set_fontsize(10)\n",
    "    \n",
    "file_name = '/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/figures/Sample_Summary/sample_study_distribution_across_subjects.pdf'\n",
    "plt.savefig(file_name, bbox_inches='tight', dpi = 300 , transparent = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##########################################################################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replicate Sample Pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##########################################################################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Import Sample Annotation file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "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_with_Kraken_and_F2.csv' , sep  = ',').set_index('patient_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "SNP_variant_dir = '/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/pickled_files/variant_calling/replicate_SNPs/all_SNPs_between_replicate_pairs/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>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",
       "    <tr>\n",
       "      <th>patient_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>replicate_pair_61</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>MMOI00000000</td>\n",
       "      <td>Peru4688</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru4688</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_61</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/pools/99-R86...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99-R862</td>\n",
       "      <td>0</td>\n",
       "      <td>99-R862</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_19</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>MMKP00000000</td>\n",
       "      <td>Peru5074</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru5074</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_19</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/pools/01-R08...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-R0878</td>\n",
       "      <td>0</td>\n",
       "      <td>01-R0878</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_21</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>MMZA00000000</td>\n",
       "      <td>Peru4922</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru4922</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         fastq_files  \\\n",
       "patient_id                                                             \n",
       "replicate_pair_61  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "replicate_pair_61  /n/data1/hms/dbmi/farhat/fastq_db/pools/99-R86...   \n",
       "replicate_pair_19  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "replicate_pair_19  /n/data1/hms/dbmi/farhat/fastq_db/pools/01-R08...   \n",
       "replicate_pair_21  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "\n",
       "                  population        run_ID sample_ID  sample_order       tag  \\\n",
       "patient_id                                                                     \n",
       "replicate_pair_61     CP_REP  MMOI00000000  Peru4688             0  Peru4688   \n",
       "replicate_pair_61     CP_REP           NaN   99-R862             0   99-R862   \n",
       "replicate_pair_19     CP_REP  MMKP00000000  Peru5074             0  Peru5074   \n",
       "replicate_pair_19     CP_REP           NaN  01-R0878             0  01-R0878   \n",
       "replicate_pair_21     CP_REP  MMZA00000000  Peru4922             0  Peru4922   \n",
       "\n",
       "                  isolate_type  \n",
       "patient_id                      \n",
       "replicate_pair_61    replicate  \n",
       "replicate_pair_61    replicate  \n",
       "replicate_pair_19    replicate  \n",
       "replicate_pair_19    replicate  \n",
       "replicate_pair_21    replicate  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_annotation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70\n"
     ]
    }
   ],
   "source": [
    "num_isolate_pair_IDs = np.shape(sample_annotation)[0] / 2\n",
    "print num_isolate_pair_IDs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "isolate_pair_ID_list = list(set(sample_annotation.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Call function to collect SNPs passing Difference in Alternate Allele Frequency Threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "10\n",
      "15\n",
      "20\n",
      "25\n",
      "30\n",
      "35\n",
      "40\n",
      "45\n",
      "50\n",
      "55\n",
      "60\n",
      "65\n",
      "70\n"
     ]
    }
   ],
   "source": [
    "Base_Call_variants_btwn_isolates_big_change_in_alt_AF = []\n",
    "\n",
    "isolate_pair_index = 0\n",
    "\n",
    "#iterate through isolate pairs, collect all SNP variants arising between each pair of isolates\n",
    "for isolate_pair_ID in isolate_pair_ID_list:\n",
    "    \n",
    "    #retrieve filtered paired Base Calls with a change in Alternate Allele Frequency > threshold\n",
    "    Base_Call_variants_btwn_isolates_big_change_in_alt_AF_pair_i = get_filtered_SNPs_between_isolates(isolate_pair_ID , alt_AF_diff_threshold)\n",
    "    \n",
    "    #store relevant Base Call info in list of DataFrames (1 for each isolate pair)\n",
    "    Base_Call_variants_btwn_isolates_big_change_in_alt_AF.append(Base_Call_variants_btwn_isolates_big_change_in_alt_AF_pair_i)\n",
    "    isolate_pair_index += 1\n",
    "    \n",
    "    if isolate_pair_index % 5 == 0:\n",
    "        print isolate_pair_index\n",
    "        \n",
    "#concatenate DataFrames for each subject into 1 DataFrame\n",
    "Base_Call_variants_btwn_isolates_big_change_in_alt_AF = pd.concat(Base_Call_variants_btwn_isolates_big_change_in_alt_AF , axis = 0)\n",
    "\n",
    "Base_Call_variants_btwn_isolates_big_change_in_alt_AF.reset_index(inplace = True , drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Filter*: Drop paired Base Calls if both Base Calls in a pair support *different* Alternate Alleles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#list that stores the indices of paired Base Calls with DIFFERENT Alternate Alleles\n",
    "Base_Calls_to_Drop = []\n",
    "\n",
    "#iterate through each PAIR of corresponding Base Calls from paired isolates\n",
    "for isolate_A_Base_Call_i , isolate_B_Base_Call_i in zip(range(0 , np.shape(Base_Call_variants_btwn_isolates_big_change_in_alt_AF)[0] , 2) , range(1 , np.shape(Base_Call_variants_btwn_isolates_big_change_in_alt_AF)[0] , 2) ):\n",
    "    \n",
    "    #pull info that both Base Calls should have in COMMON\n",
    "    isolate_A_Base_Call_info = list( Base_Call_variants_btwn_isolates_big_change_in_alt_AF.loc[isolate_A_Base_Call_i , ['ref_base','ref_position','gene_id','genomic_coord','population','patient_id']] ) \n",
    "    isolate_B_Base_Call_info = list( Base_Call_variants_btwn_isolates_big_change_in_alt_AF.loc[isolate_B_Base_Call_i , ['ref_base','ref_position','gene_id','genomic_coord','population','patient_id']] ) \n",
    "    \n",
    "    #make sure Base Calls Match with respect to Reference Base, Reference Position, gene ID, Genomic Coordinate, Gene Category, Symbol, Population & Patient ID\n",
    "    if isolate_A_Base_Call_info == isolate_B_Base_Call_info:\n",
    "        \n",
    "        #pull alternate Allele for each of the paired isolates\n",
    "        isolate_A_Alt_Base = Base_Call_variants_btwn_isolates_big_change_in_alt_AF.loc[isolate_A_Base_Call_i , 'alt_base']\n",
    "        isolate_B_Alt_Base = Base_Call_variants_btwn_isolates_big_change_in_alt_AF.loc[isolate_B_Base_Call_i , 'alt_base']\n",
    "        \n",
    "        #Check to see if there is a 'Z' in the pair of Alternate Alleles, if so one of the Base Calls supported the Reference Base (so there was no Alternate Allele)\n",
    "        if (isolate_A_Alt_Base == 'Z') or (isolate_B_Alt_Base == 'Z'):\n",
    "            pass\n",
    "        \n",
    "        #if neither Alternate Allele is a 'Z', then check to see that the Alternate Allele Bases Match\n",
    "        elif isolate_A_Alt_Base == isolate_B_Alt_Base:\n",
    "            pass\n",
    "        \n",
    "        #if the Alternate Alleles DON'T match and both Base Calls supported Alternate Alleles (not the Reference), then we can't compare the Allele Frequencies of these Alternate Alleles (since they're different)\n",
    "        else:\n",
    "            Base_Calls_to_Drop = Base_Calls_to_Drop + [isolate_A_Base_Call_i , isolate_B_Base_Call_i]\n",
    "        \n",
    "    #print indices of Base Calls and see what went wrong if the paired Base Calls have different information that the Calls should have in Common (Ref Position, Ref Base, Gene ID, Patient ID, etc.)\n",
    "    else:\n",
    "        print (isolate_A_Base_Call_i , isolate_B_Base_Call_i)\n",
    "        \n",
    "#Drop Paired Base Calls that supported different Alternate Alleles\n",
    "Base_Call_variants_btwn_isolates_big_change_in_alt_AF.drop(Base_Calls_to_Drop , axis = 0 , inplace = True)\n",
    "\n",
    "#reset index\n",
    "Base_Call_variants_btwn_isolates_big_change_in_alt_AF.reset_index(inplace = True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>ref_base</th>\n",
       "      <th>alt_base</th>\n",
       "      <th>ref_position</th>\n",
       "      <th>quality</th>\n",
       "      <th>SNP_type</th>\n",
       "      <th>PASS_filter</th>\n",
       "      <th>INFO</th>\n",
       "      <th>alt_AF</th>\n",
       "      <th>depth</th>\n",
       "      <th>tag</th>\n",
       "      <th>population</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_coord</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>SNP_ftype</th>\n",
       "      <th>AA_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>Z</td>\n",
       "      <td>7691</td>\n",
       "      <td>1203</td>\n",
       "      <td>Ref_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 100, 0, 0], u'AC': [0], u'AF': [0....</td>\n",
       "      <td>0.00</td>\n",
       "      <td>34</td>\n",
       "      <td>01-R0878</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_19</td>\n",
       "      <td>Rv0006</td>\n",
       "      <td>390</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>7691</td>\n",
       "      <td>6639</td>\n",
       "      <td>Alt_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 0, 100, 0], u'AC': [2], u'AF': [1....</td>\n",
       "      <td>1.00</td>\n",
       "      <td>168</td>\n",
       "      <td>Peru5074</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_19</td>\n",
       "      <td>Rv0006</td>\n",
       "      <td>390</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>T130T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>G</td>\n",
       "      <td>Z</td>\n",
       "      <td>8040</td>\n",
       "      <td>1703</td>\n",
       "      <td>Ref_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 0, 100, 0], u'AC': [0], u'AF': [0....</td>\n",
       "      <td>0.00</td>\n",
       "      <td>46</td>\n",
       "      <td>01-R0878</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_19</td>\n",
       "      <td>Rv0006</td>\n",
       "      <td>739</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>8040</td>\n",
       "      <td>6781</td>\n",
       "      <td>Alt_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [99, 0, 1, 0], u'AC': [2], u'AF': [0.9...</td>\n",
       "      <td>0.99</td>\n",
       "      <td>178</td>\n",
       "      <td>Peru5074</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_19</td>\n",
       "      <td>Rv0006</td>\n",
       "      <td>739</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>G247S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>11370</td>\n",
       "      <td>2031</td>\n",
       "      <td>Alt_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 0, 0, 100], u'AC': [2], u'AF': [1....</td>\n",
       "      <td>1.00</td>\n",
       "      <td>59</td>\n",
       "      <td>01-R0878</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_19</td>\n",
       "      <td>Rvnt02_Rv0008c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>Z</td>\n",
       "      <td>11370</td>\n",
       "      <td>6705</td>\n",
       "      <td>Ref_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 100, 0, 0], u'AC': [0], u'AF': [0....</td>\n",
       "      <td>0.00</td>\n",
       "      <td>170</td>\n",
       "      <td>Peru5074</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_19</td>\n",
       "      <td>Rvnt02_Rv0008c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>14251</td>\n",
       "      <td>6315</td>\n",
       "      <td>Alt_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [100, 0, 0, 0], u'AC': [2], u'AF': [1....</td>\n",
       "      <td>1.00</td>\n",
       "      <td>161</td>\n",
       "      <td>Peru5074</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_19</td>\n",
       "      <td>Rv0012</td>\n",
       "      <td>163</td>\n",
       "      <td>Antigen</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>D55N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>G</td>\n",
       "      <td>Z</td>\n",
       "      <td>14251</td>\n",
       "      <td>1611</td>\n",
       "      <td>Ref_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 0, 100, 0], u'AC': [0], u'AF': [0....</td>\n",
       "      <td>0.00</td>\n",
       "      <td>46</td>\n",
       "      <td>01-R0878</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_19</td>\n",
       "      <td>Rv0012</td>\n",
       "      <td>163</td>\n",
       "      <td>Antigen</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>G</td>\n",
       "      <td>Z</td>\n",
       "      <td>17608</td>\n",
       "      <td>2279</td>\n",
       "      <td>Ref_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 0, 100, 0], u'AC': [0], u'AF': [0....</td>\n",
       "      <td>0.00</td>\n",
       "      <td>65</td>\n",
       "      <td>01-R0878</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_19</td>\n",
       "      <td>Rv0015c</td>\n",
       "      <td>1155</td>\n",
       "      <td>Antigen</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>17608</td>\n",
       "      <td>5244</td>\n",
       "      <td>Alt_PASS</td>\n",
       "      <td>[]</td>\n",
       "      <td>{u'QP': [0, 99, 1, 0], u'AC': [2], u'AF': [0.9...</td>\n",
       "      <td>0.99</td>\n",
       "      <td>149</td>\n",
       "      <td>Peru5074</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_19</td>\n",
       "      <td>Rv0015c</td>\n",
       "      <td>1155</td>\n",
       "      <td>Antigen</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>S385R</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ref_base alt_base  ref_position  quality  SNP_type PASS_filter  \\\n",
       "0        C        Z          7691     1203  Ref_PASS          []   \n",
       "1        C        G          7691     6639  Alt_PASS          []   \n",
       "2        G        Z          8040     1703  Ref_PASS          []   \n",
       "3        G        A          8040     6781  Alt_PASS          []   \n",
       "4        C        T         11370     2031  Alt_PASS          []   \n",
       "5        C        Z         11370     6705  Ref_PASS          []   \n",
       "6        G        A         14251     6315  Alt_PASS          []   \n",
       "7        G        Z         14251     1611  Ref_PASS          []   \n",
       "8        G        Z         17608     2279  Ref_PASS          []   \n",
       "9        G        C         17608     5244  Alt_PASS          []   \n",
       "\n",
       "                                                INFO  alt_AF  depth       tag  \\\n",
       "0  {u'QP': [0, 100, 0, 0], u'AC': [0], u'AF': [0....    0.00     34  01-R0878   \n",
       "1  {u'QP': [0, 0, 100, 0], u'AC': [2], u'AF': [1....    1.00    168  Peru5074   \n",
       "2  {u'QP': [0, 0, 100, 0], u'AC': [0], u'AF': [0....    0.00     46  01-R0878   \n",
       "3  {u'QP': [99, 0, 1, 0], u'AC': [2], u'AF': [0.9...    0.99    178  Peru5074   \n",
       "4  {u'QP': [0, 0, 0, 100], u'AC': [2], u'AF': [1....    1.00     59  01-R0878   \n",
       "5  {u'QP': [0, 100, 0, 0], u'AC': [0], u'AF': [0....    0.00    170  Peru5074   \n",
       "6  {u'QP': [100, 0, 0, 0], u'AC': [2], u'AF': [1....    1.00    161  Peru5074   \n",
       "7  {u'QP': [0, 0, 100, 0], u'AC': [0], u'AF': [0....    0.00     46  01-R0878   \n",
       "8  {u'QP': [0, 0, 100, 0], u'AC': [0], u'AF': [0....    0.00     65  01-R0878   \n",
       "9  {u'QP': [0, 99, 1, 0], u'AC': [2], u'AF': [0.9...    0.99    149  Peru5074   \n",
       "\n",
       "  population         patient_id         gene_id gene_coord  \\\n",
       "0     CP_REP  replicate_pair_19          Rv0006        390   \n",
       "1     CP_REP  replicate_pair_19          Rv0006        390   \n",
       "2     CP_REP  replicate_pair_19          Rv0006        739   \n",
       "3     CP_REP  replicate_pair_19          Rv0006        739   \n",
       "4     CP_REP  replicate_pair_19  Rvnt02_Rv0008c       None   \n",
       "5     CP_REP  replicate_pair_19  Rvnt02_Rv0008c       None   \n",
       "6     CP_REP  replicate_pair_19          Rv0012        163   \n",
       "7     CP_REP  replicate_pair_19          Rv0012        163   \n",
       "8     CP_REP  replicate_pair_19         Rv0015c       1155   \n",
       "9     CP_REP  replicate_pair_19         Rv0015c       1155   \n",
       "\n",
       "           gene_category gene_symbol SNP_ftype AA_change  \n",
       "0  Antibiotic Resistance        None      None      None  \n",
       "1  Antibiotic Resistance        None         S     T130T  \n",
       "2  Antibiotic Resistance        None      None      None  \n",
       "3  Antibiotic Resistance        None         N     G247S  \n",
       "4                   None        None         I      None  \n",
       "5                   None        None         I      None  \n",
       "6                Antigen        None         N      D55N  \n",
       "7                Antigen        None      None      None  \n",
       "8                Antigen        None      None      None  \n",
       "9                Antigen        None         N     S385R  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Base_Call_variants_btwn_isolates_big_change_in_alt_AF.head(n = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7590, 18)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(Base_Call_variants_btwn_isolates_big_change_in_alt_AF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Re-Shape Filtered DataFrame (Paired Base Calls across all isolate pairs) to store one entry per SNP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "SNP_variants_between_paired_isolates = pd.DataFrame()\n",
    "\n",
    "#common information to both Base Calls (can just look at isolate A)\n",
    "population_dict = {}\n",
    "patient_id_dict = {}\n",
    "ref_position_dict = {}\n",
    "ref_allele_dict = {}\n",
    "gene_id_dict  = {}\n",
    "genomic_coord_dict = {}\n",
    "gene_category_dict = {}\n",
    "gene_symbol_dict = {}\n",
    "\n",
    "#look at info for both Base Calls\n",
    "alt_allele_dict = {}\n",
    "alt_AF_diff_dict = {}\n",
    "SNP_type_dict = {}\n",
    "AA_change_dict = {}\n",
    "\n",
    "SNP_index = 0\n",
    "#iterate through indices for isolate A (store common information for isolate pair A & B came from and Base Call), calculate different in Alternate Allele Frequencies, store Syn or NSyn info\n",
    "for even_index in range(0 , np.shape(Base_Call_variants_btwn_isolates_big_change_in_alt_AF)[0] , 2):\n",
    "    \n",
    "    #Base Call info for isolate A\n",
    "    Base_Call_info_isolate_A = Base_Call_variants_btwn_isolates_big_change_in_alt_AF.loc[even_index , :]\n",
    "    #Base Call info for isolate B\n",
    "    Base_Call_info_isolate_B = Base_Call_variants_btwn_isolates_big_change_in_alt_AF.loc[even_index+1 , :]\n",
    "    \n",
    "    population_dict[SNP_index] = Base_Call_info_isolate_A.population\n",
    "    patient_id_dict[SNP_index] = Base_Call_info_isolate_A.patient_id\n",
    "    ref_position_dict[SNP_index] = Base_Call_info_isolate_A.ref_position\n",
    "    ref_allele_dict[SNP_index] = Base_Call_info_isolate_A.ref_base\n",
    "    gene_id_dict[SNP_index] = Base_Call_info_isolate_A.gene_id\n",
    "    genomic_coord_dict[SNP_index] = Base_Call_info_isolate_A.gene_coord\n",
    "    gene_category_dict[SNP_index] = Base_Call_info_isolate_A.gene_category\n",
    "    gene_symbol_dict[SNP_index] = Base_Call_info_isolate_A.gene_symbol\n",
    "    \n",
    "    #get alternate allele\n",
    "    alt_allele_calls = [Base_Call_info_isolate_A.alt_base , Base_Call_info_isolate_B.alt_base]\n",
    "    try:\n",
    "        alt_allele_calls.remove('Z')\n",
    "    except ValueError:\n",
    "        pass\n",
    "    alt_allele_dict[SNP_index] = alt_allele_calls[0]\n",
    "\n",
    "    #get difference in Alternate Allele Frequencies\n",
    "    alt_AF_diff_dict[SNP_index] = abs(Base_Call_info_isolate_A.alt_AF - Base_Call_info_isolate_B.alt_AF)\n",
    "    \n",
    "    #get type of SNP \n",
    "    if 'S' in [Base_Call_info_isolate_A.SNP_ftype , Base_Call_info_isolate_B.SNP_ftype]:\n",
    "        SNP_type_dict[SNP_index] = 'S'\n",
    "        \n",
    "    elif 'N' in [Base_Call_info_isolate_A.SNP_ftype , Base_Call_info_isolate_B.SNP_ftype]:\n",
    "        SNP_type_dict[SNP_index] = 'N'\n",
    "        \n",
    "    elif 'I' in [Base_Call_info_isolate_A.SNP_ftype , Base_Call_info_isolate_B.SNP_ftype]:\n",
    "        SNP_type_dict[SNP_index] = 'I'\n",
    "        \n",
    "    #get AA change\n",
    "    AA_change_calls = [Base_Call_info_isolate_A.AA_change , Base_Call_info_isolate_B.AA_change]\n",
    "    try:\n",
    "        AA_change_calls.remove('None')\n",
    "    except ValueError:\n",
    "        pass\n",
    "    AA_change_dict[SNP_index] = AA_change_calls[0]\n",
    "        \n",
    "    SNP_index += 1\n",
    "    \n",
    "#convert dictionaries into series\n",
    "population = pd.Series(population_dict)\n",
    "patient_id = pd.Series(patient_id_dict)\n",
    "ref_position = pd.Series(ref_position_dict)\n",
    "ref_allele = pd.Series(ref_allele_dict)\n",
    "alt_allele = pd.Series(alt_allele_dict)\n",
    "gene_id = pd.Series(gene_id_dict)\n",
    "genomic_coord = pd.Series(genomic_coord_dict)\n",
    "gene_category = pd.Series(gene_category_dict)\n",
    "gene_symbol = pd.Series(gene_symbol_dict)\n",
    "alt_AF_diff = pd.Series(alt_AF_diff_dict)\n",
    "SNP_type = pd.Series(SNP_type_dict)\n",
    "AA_change = pd.Series(AA_change_dict)\n",
    "        \n",
    "#create DataFrame\n",
    "SNP_variants_between_paired_isolates['population'] = population\n",
    "SNP_variants_between_paired_isolates['patient_id'] = patient_id\n",
    "SNP_variants_between_paired_isolates['ref_position'] = ref_position \n",
    "SNP_variants_between_paired_isolates['ref_allele'] = ref_allele\n",
    "SNP_variants_between_paired_isolates['alt_allele'] = alt_allele\n",
    "SNP_variants_between_paired_isolates['gene_id'] = gene_id\n",
    "SNP_variants_between_paired_isolates['genomic_coord'] = genomic_coord\n",
    "SNP_variants_between_paired_isolates['gene_category'] = gene_category\n",
    "SNP_variants_between_paired_isolates['gene_symbol'] = gene_symbol\n",
    "SNP_variants_between_paired_isolates['alt_AF_diff'] = alt_AF_diff\n",
    "SNP_variants_between_paired_isolates['SNP_type'] = SNP_type\n",
    "SNP_variants_between_paired_isolates['AA_change'] = AA_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>0</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_19</td>\n",
       "      <td>7691</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv0006</td>\n",
       "      <td>390</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>None</td>\n",
       "      <td>1.00</td>\n",
       "      <td>S</td>\n",
       "      <td>T130T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_19</td>\n",
       "      <td>8040</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv0006</td>\n",
       "      <td>739</td>\n",
       "      <td>Antibiotic Resistance</td>\n",
       "      <td>None</td>\n",
       "      <td>0.99</td>\n",
       "      <td>N</td>\n",
       "      <td>G247S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_19</td>\n",
       "      <td>11370</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>Rvnt02_Rv0008c</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.00</td>\n",
       "      <td>I</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_19</td>\n",
       "      <td>14251</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv0012</td>\n",
       "      <td>163</td>\n",
       "      <td>Antigen</td>\n",
       "      <td>None</td>\n",
       "      <td>1.00</td>\n",
       "      <td>N</td>\n",
       "      <td>D55N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CP_REP</td>\n",
       "      <td>replicate_pair_19</td>\n",
       "      <td>17608</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\n",
       "      <td>Rv0015c</td>\n",
       "      <td>1155</td>\n",
       "      <td>Antigen</td>\n",
       "      <td>None</td>\n",
       "      <td>0.99</td>\n",
       "      <td>N</td>\n",
       "      <td>S385R</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_19          7691          C          G   \n",
       "1     CP_REP  replicate_pair_19          8040          G          A   \n",
       "2     CP_REP  replicate_pair_19         11370          C          T   \n",
       "3     CP_REP  replicate_pair_19         14251          G          A   \n",
       "4     CP_REP  replicate_pair_19         17608          G          C   \n",
       "\n",
       "          gene_id genomic_coord          gene_category gene_symbol  \\\n",
       "0          Rv0006           390  Antibiotic Resistance        None   \n",
       "1          Rv0006           739  Antibiotic Resistance        None   \n",
       "2  Rvnt02_Rv0008c          None                   None        None   \n",
       "3          Rv0012           163                Antigen        None   \n",
       "4         Rv0015c          1155                Antigen        None   \n",
       "\n",
       "   alt_AF_diff SNP_type AA_change  \n",
       "0         1.00        S     T130T  \n",
       "1         0.99        N     G247S  \n",
       "2         1.00        I      None  \n",
       "3         1.00        N      D55N  \n",
       "4         0.99        N     S385R  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SNP_variants_between_paired_isolates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3795, 12)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(SNP_variants_between_paired_isolates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "replicate_pair_54    1094\n",
       "replicate_pair_8      775\n",
       "replicate_pair_19     734\n",
       "replicate_pair_41     424\n",
       "replicate_pair_57     286\n",
       "replicate_pair_58     170\n",
       "I0003922-7            156\n",
       "I0003710-6            155\n",
       "replicate_pair_68       1\n",
       "replicate_pair_9        0\n",
       "replicate_pair_24       0\n",
       "replicate_pair_21       0\n",
       "replicate_pair_22       0\n",
       "replicate_pair_23       0\n",
       "replicate_pair_27       0\n",
       "replicate_pair_25       0\n",
       "replicate_pair_26       0\n",
       "replicate_pair_28       0\n",
       "replicate_pair_29       0\n",
       "replicate_pair_3        0\n",
       "replicate_pair_30       0\n",
       "replicate_pair_2        0\n",
       "replicate_pair_16       0\n",
       "replicate_pair_18       0\n",
       "replicate_pair_33       0\n",
       "replicate_pair_15       0\n",
       "replicate_pair_14       0\n",
       "replicate_pair_13       0\n",
       "replicate_pair_12       0\n",
       "replicate_pair_11       0\n",
       "replicate_pair_10       0\n",
       "replicate_pair_1        0\n",
       "replicate_pair_0        0\n",
       "P12_3                   0\n",
       "P12_2                   0\n",
       "P12_1                   0\n",
       "I0004220-5              0\n",
       "replicate_pair_32       0\n",
       "replicate_pair_37       0\n",
       "replicate_pair_34       0\n",
       "replicate_pair_59       0\n",
       "replicate_pair_74       0\n",
       "replicate_pair_73       0\n",
       "replicate_pair_72       0\n",
       "replicate_pair_71       0\n",
       "replicate_pair_70       0\n",
       "replicate_pair_7        0\n",
       "replicate_pair_69       0\n",
       "replicate_pair_67       0\n",
       "replicate_pair_66       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isolate_pairs = list(set( sample_annotation.index ))\n",
    "\n",
    "num_fSNPs_per_isolate_pair = {}\n",
    "for isolate_pair in isolate_pairs:\n",
    "    \n",
    "    num_fSNPs_per_isolate_pair[isolate_pair] = np.shape(SNP_variants_between_paired_isolates[SNP_variants_between_paired_isolates.patient_id == isolate_pair])[0]\n",
    "    \n",
    "#convert dict to series\n",
    "num_fSNPs_per_isolate_pair = pd.Series(num_fSNPs_per_isolate_pair)\n",
    "\n",
    "#sort values\n",
    "num_fSNPs_per_isolate_pair.sort_values(ascending = False , inplace = True)\n",
    "\n",
    "num_fSNPs_per_isolate_pair.head(n=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copy series for down-stream analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_fSNPs_per_replicate_isolate_pair = num_fSNPs_per_isolate_pair.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Subset* to the isolate pairs that we're going to drop (at threshold of > 7 SNPs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "isolate_pair_IDs_for_dropped_isolates = list( set( list( num_fSNPs_per_isolate_pair[num_fSNPs_per_isolate_pair > 7].index ) ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['replicate_pair_19', 'replicate_pair_8', 'I0003922-7', 'replicate_pair_54', 'replicate_pair_57', 'replicate_pair_41', 'I0003710-6', 'replicate_pair_58']\n"
     ]
    }
   ],
   "source": [
    "print isolate_pair_IDs_for_dropped_isolates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(isolate_pair_IDs_for_dropped_isolates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>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",
       "    <tr>\n",
       "      <th>patient_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>replicate_pair_19</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>MMKP00000000</td>\n",
       "      <td>Peru5074</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru5074</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_19</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/pools/01-R08...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-R0878</td>\n",
       "      <td>0</td>\n",
       "      <td>01-R0878</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_8</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>MMOZ00000000</td>\n",
       "      <td>Peru4671</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru4671</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_8</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/pools/01-R01...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-R0185</td>\n",
       "      <td>0</td>\n",
       "      <td>01-R0185</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_54</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>MMSH00000000</td>\n",
       "      <td>Peru4574</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru4574</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_54</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/pools/04-R02...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-R0266</td>\n",
       "      <td>0</td>\n",
       "      <td>04-R0266</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_57</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>MMUQ00000000</td>\n",
       "      <td>Peru5037</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru5037</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_57</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/pools/98-R79...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98-R790</td>\n",
       "      <td>0</td>\n",
       "      <td>98-R790</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_41</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>MRFD00000000</td>\n",
       "      <td>Peru5442</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru5442</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_41</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/pools/02-R08...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02-R0812</td>\n",
       "      <td>0</td>\n",
       "      <td>02-R0812</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_58</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>MMUY00000000</td>\n",
       "      <td>Peru5029</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru5029</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_58</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/pools/99-103...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99-10364</td>\n",
       "      <td>0</td>\n",
       "      <td>99-10364</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I0003922-7</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>LC_REP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peru3016</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru3016</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I0003922-7</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>LC_REP</td>\n",
       "      <td>MMBB00000000</td>\n",
       "      <td>Peru4110</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru4110</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I0003710-6</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>LC_REP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peru2905</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru2905</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I0003710-6</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>LC_REP</td>\n",
       "      <td>MMBH00000000</td>\n",
       "      <td>Peru4104</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru4104</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         fastq_files  \\\n",
       "patient_id                                                             \n",
       "replicate_pair_19  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "replicate_pair_19  /n/data1/hms/dbmi/farhat/fastq_db/pools/01-R08...   \n",
       "replicate_pair_8   /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "replicate_pair_8   /n/data1/hms/dbmi/farhat/fastq_db/pools/01-R01...   \n",
       "replicate_pair_54  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "replicate_pair_54  /n/data1/hms/dbmi/farhat/fastq_db/pools/04-R02...   \n",
       "replicate_pair_57  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "replicate_pair_57  /n/data1/hms/dbmi/farhat/fastq_db/pools/98-R79...   \n",
       "replicate_pair_41  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "replicate_pair_41  /n/data1/hms/dbmi/farhat/fastq_db/pools/02-R08...   \n",
       "replicate_pair_58  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "replicate_pair_58  /n/data1/hms/dbmi/farhat/fastq_db/pools/99-103...   \n",
       "I0003922-7         /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "I0003922-7         /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "I0003710-6         /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "I0003710-6         /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "\n",
       "                  population        run_ID sample_ID  sample_order       tag  \\\n",
       "patient_id                                                                     \n",
       "replicate_pair_19     CP_REP  MMKP00000000  Peru5074             0  Peru5074   \n",
       "replicate_pair_19     CP_REP           NaN  01-R0878             0  01-R0878   \n",
       "replicate_pair_8      CP_REP  MMOZ00000000  Peru4671             0  Peru4671   \n",
       "replicate_pair_8      CP_REP           NaN  01-R0185             0  01-R0185   \n",
       "replicate_pair_54     CP_REP  MMSH00000000  Peru4574             0  Peru4574   \n",
       "replicate_pair_54     CP_REP           NaN  04-R0266             0  04-R0266   \n",
       "replicate_pair_57     CP_REP  MMUQ00000000  Peru5037             0  Peru5037   \n",
       "replicate_pair_57     CP_REP           NaN   98-R790             0   98-R790   \n",
       "replicate_pair_41     CP_REP  MRFD00000000  Peru5442             0  Peru5442   \n",
       "replicate_pair_41     CP_REP           NaN  02-R0812             0  02-R0812   \n",
       "replicate_pair_58     CP_REP  MMUY00000000  Peru5029             0  Peru5029   \n",
       "replicate_pair_58     CP_REP           NaN  99-10364             0  99-10364   \n",
       "I0003922-7            LC_REP           NaN  Peru3016             0  Peru3016   \n",
       "I0003922-7            LC_REP  MMBB00000000  Peru4110             0  Peru4110   \n",
       "I0003710-6            LC_REP           NaN  Peru2905             0  Peru2905   \n",
       "I0003710-6            LC_REP  MMBH00000000  Peru4104             0  Peru4104   \n",
       "\n",
       "                  isolate_type  \n",
       "patient_id                      \n",
       "replicate_pair_19    replicate  \n",
       "replicate_pair_19    replicate  \n",
       "replicate_pair_8     replicate  \n",
       "replicate_pair_8     replicate  \n",
       "replicate_pair_54    replicate  \n",
       "replicate_pair_54    replicate  \n",
       "replicate_pair_57    replicate  \n",
       "replicate_pair_57    replicate  \n",
       "replicate_pair_41    replicate  \n",
       "replicate_pair_41    replicate  \n",
       "replicate_pair_58    replicate  \n",
       "replicate_pair_58    replicate  \n",
       "I0003922-7           replicate  \n",
       "I0003922-7           replicate  \n",
       "I0003710-6           replicate  \n",
       "I0003710-6           replicate  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
    "    display(sample_annotation.loc[isolate_pair_IDs_for_dropped_isolates , :].sort_values('population'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'CP_REP': 12, 'LC_REP': 4})"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(list(sample_annotation.loc[isolate_pair_IDs_for_dropped_isolates , :].population))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of pairwise distances across isolate pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SNP_diff_threshold = 3\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['lines.linewidth']=3\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': 14})\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "#plot all pairwise comparisons\n",
    "n , bins , patches = plt.hist(num_fSNPs_per_isolate_pair , bins = 45 , color = 'black' , rwidth = 0.75 , edgecolor='black', linewidth = 0.0)\n",
    "\n",
    "plot_padding = 3\n",
    "\n",
    "total_number_of_patients = np.shape( num_fSNPs_per_isolate_pair )[0]\n",
    "\n",
    "plt.ylabel('Number of Replicate Pairs' , fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "plt.xlabel('Number of fixed SNPs Between Replicate Isolates', fontweight = 'bold', fontsize = 12 , color = 'k')\n",
    "\n",
    "plt.xlim(min(bins)-plot_padding , max(bins)+plot_padding)\n",
    "plt.ylim(min(n) , max(n)+3)\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(8.0, 5)\n",
    "fig.tight_layout()\n",
    "\n",
    "#file_name = '/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/figures/Filtering_by_fSNPs_analysis/pairwise_fSNP_distance_between_replicate_isolates.pdf'\n",
    "#plt.savefig(file_name, bbox_inches='tight', dpi = 300 , transparent = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x306 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SNP_diff_threshold = 25\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['lines.linewidth']=3\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})\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "#plot all pairwise comparison\n",
    "n , bins , patches = plt.hist( num_fSNPs_per_isolate_pair[num_fSNPs_per_isolate_pair <= SNP_diff_threshold] , bins = np.arange(0 , SNP_diff_threshold + 1) , color = 'black' , rwidth = 0.7 , edgecolor='black', linewidth=0.0 , align = 'left')\n",
    "\n",
    "plot_padding = 1.0\n",
    "\n",
    "total_number_of_patients = np.shape( num_fSNPs_per_isolate_pair )[0]\n",
    "\n",
    "plt.xlim(min(bins)-plot_padding , max(bins)-plot_padding)\n",
    "plt.ylim(min(n) , max(n)+1)\n",
    "\n",
    "\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "ax.tick_params(labelcolor = 'k')\n",
    "\n",
    "ax.set_xticks(bins[0:-1][0:len(bins[0:-1]):2]) #xtick positions\n",
    "ax.set_xticklabels([str(int(x)) for x in bins[0:][0:len(bins[0:]):2]]) #xtick labels\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(6.5, 4.25)\n",
    "fig.tight_layout()\n",
    "\n",
    "#file_name = '/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/figures/Filtering_by_fSNPs_analysis/pairwise_fSNP_distance_between_replicate_isolates_ZOOMED.pdf'\n",
    "#plt.savefig(file_name, bbox_inches='tight', dpi = 300 , transparent = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Filter* out Replicate Pairs that have > 7 SNPs between them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop mixed isolate pairs from Sample Annotation and export as CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70\n"
     ]
    }
   ],
   "source": [
    "print np.shape(sample_annotation)[0] / 2 #number of replicate isolate pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get list of isolate pairs with a high genetic distance\n",
    "mixed_isolate_pairs = list( num_fSNPs_per_isolate_pair[num_fSNPs_per_isolate_pair > 7].index )\n",
    "\n",
    "#filter out from sample annotation\n",
    "clean_isolate_pairs = list( set(list(sample_annotation.index)) - set(mixed_isolate_pairs) )\n",
    "\n",
    "sample_annotation_clean = sample_annotation.loc[clean_isolate_pairs , :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62\n"
     ]
    }
   ],
   "source": [
    "print np.shape(sample_annotation_clean)[0] / 2 #number of replicate isolate pairs with fSNP distance <= 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export updated sample annotation file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_annotation_clean.to_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 = ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>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",
       "    <tr>\n",
       "      <th>patient_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>replicate_pair_18</th>\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>replicate_pair_18</th>\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",
       "    <tr>\n",
       "      <th>replicate_pair_26</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>MMGW00000000</td>\n",
       "      <td>Peru4719</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru4719</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_26</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/fastq_db/pools/01-R09...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-R0909</td>\n",
       "      <td>0</td>\n",
       "      <td>01-R0909</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replicate_pair_11</th>\n",
       "      <td>/n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...</td>\n",
       "      <td>CP_REP</td>\n",
       "      <td>MMOR00000000</td>\n",
       "      <td>Peru4679</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru4679</td>\n",
       "      <td>replicate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         fastq_files  \\\n",
       "patient_id                                                             \n",
       "replicate_pair_18  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "replicate_pair_18  /n/data1/hms/dbmi/farhat/fastq_db/pools/01-R07...   \n",
       "replicate_pair_26  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "replicate_pair_26  /n/data1/hms/dbmi/farhat/fastq_db/pools/01-R09...   \n",
       "replicate_pair_11  /n/data1/hms/dbmi/farhat/cetr_strains/good_wgs...   \n",
       "\n",
       "                  population        run_ID sample_ID  sample_order       tag  \\\n",
       "patient_id                                                                     \n",
       "replicate_pair_18     CP_REP  MMYK00000000  Peru4938             0  Peru4938   \n",
       "replicate_pair_18     CP_REP           NaN  01-R0774             0  01-R0774   \n",
       "replicate_pair_26     CP_REP  MMGW00000000  Peru4719             0  Peru4719   \n",
       "replicate_pair_26     CP_REP           NaN  01-R0909             0  01-R0909   \n",
       "replicate_pair_11     CP_REP  MMOR00000000  Peru4679             0  Peru4679   \n",
       "\n",
       "                  isolate_type  \n",
       "patient_id                      \n",
       "replicate_pair_18    replicate  \n",
       "replicate_pair_18    replicate  \n",
       "replicate_pair_26    replicate  \n",
       "replicate_pair_26    replicate  \n",
       "replicate_pair_11    replicate  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_annotation_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##########################################################################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Longitudinal & Replicate Isolate Pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##########################################################################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the distribution of fSNPs between all *Replicate* and *Longitudinal* Isolate Pairs on the same plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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})\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "#get the fSNP distances for all replicate isolates\n",
    "fSNP_replicate_values = num_fSNPs_per_replicate_isolate_pair.values\n",
    "\n",
    "#get the fSNPs distances for all longitudinal isolates\n",
    "fSNP_longitudinal_values = num_fSNPs_per_longitudinal_isolate_pair.values\n",
    "\n",
    "#plot all pairwise comparisons\n",
    "n , bins , patches = plt.hist([fSNP_replicate_values , fSNP_longitudinal_values] , bins = 45 , color = ['0.80' , '0.20'] , rwidth = 1.0 , edgecolor='black', linewidth=1.25 , stacked = True , label = ['Replicate','Longitudinal'])\n",
    "\n",
    "plot_padding = 20\n",
    "\n",
    "plt.ylabel('Number of Isolate Pairs' , fontweight = 'bold' , fontsize = 12, color = 'k')\n",
    "plt.xlabel('Number of Fixed SNPs Between Isolates Pairs', fontweight = 'bold', fontsize = 12 , color = 'k')\n",
    "\n",
    "plt.xlim(np.min(bins)-plot_padding , np.max(bins)+plot_padding)\n",
    "plt.ylim(np.min(n) , np.max(n)+3)\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "ax.legend(loc='upper right')\n",
    "ax.tick_params(labelcolor = 'k')\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(7.5, 5.0)\n",
    "fig.tight_layout()\n",
    "\n",
    "file_name = '/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/figures/Filtering_by_fSNPs_analysis/pairwise_fSNP_distance_between_isolate_pairs.pdf'\n",
    "plt.savefig(file_name, bbox_inches='tight', dpi = 300 , transparent = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 468x306 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "#get the fSNP distances for all replicate isolates\n",
    "fSNP_replicate_values = num_fSNPs_per_replicate_isolate_pair.values\n",
    "\n",
    "#get the fSNPs distances for all longitudinal isolates\n",
    "fSNP_longitudinal_values = num_fSNPs_per_longitudinal_isolate_pair.values\n",
    "\n",
    "#plot all pairwise comparisons\n",
    "n , bins , patches = plt.hist([fSNP_replicate_values[fSNP_replicate_values <= 25] , fSNP_longitudinal_values[fSNP_longitudinal_values <= 25]] , bins = np.arange(0 , 25 + 1) , color = ['0.80' , '0.20'] , rwidth = 1.0 , edgecolor='black', linewidth=1.25 , stacked = True , label = ['Replicate','Longitudinal'], align = 'left')\n",
    "\n",
    "plot_padding = 1.0\n",
    "plt.plot([7.6 , 7.6] , [0 , np.max(n)] , color = '0.65' , linewidth = 1.25 , linestyle = '-')\n",
    "\n",
    "\n",
    "plt.xlim(np.min(bins)-plot_padding , np.max(bins)+plot_padding)\n",
    "plt.ylim(np.min(n) , np.max(n)+3)\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "ax.set_xticks(bins[0:-1][0:len(bins[0:-1]):2]) #xtick positions\n",
    "ax.set_xticklabels([str(int(x)) for x in bins[0:][0:len(bins[0:]):2]]) #xtick labels\n",
    "ax.tick_params(labelcolor = 'k')\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(6.5, 4.25)\n",
    "fig.tight_layout()\n",
    "\n",
    "file_name = '/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/figures/Filtering_by_fSNPs_analysis/pairwise_fSNP_distance_between_isolate_pairs_ZOOMED.pdf'\n",
    "plt.savefig(file_name, bbox_inches='tight', dpi = 300 , transparent = True)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}