aggregate_variant_counts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aggregate variant counts for all samples\n",
    "Separate `Snakemake` rules count the observations of each variant in each sample from the Illumina barcode sequencing.\n",
    "This Python Jupyter notebook aggregates all of this counts, and then adds them to a codon variant table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up analysis\n",
    "### Import Python modules.\n",
    "Use [plotnine](https://plotnine.readthedocs.io/en/stable/) for ggplot2-like plotting.\n",
    "\n",
    "The analysis relies heavily on the Bloom lab's [dms_variants](https://jbloomlab.github.io/dms_variants) package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import itertools\n",
    "import math\n",
    "import os\n",
    "import warnings\n",
    "\n",
    "import Bio.SeqIO\n",
    "\n",
    "import dms_variants.codonvarianttable\n",
    "from dms_variants.constants import CBPALETTE\n",
    "import dms_variants.utils\n",
    "import dms_variants.plotnine_themes\n",
    "\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from plotnine import *\n",
    "\n",
    "import yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set [plotnine](https://plotnine.readthedocs.io/en/stable/) theme to the gray-grid one defined in `dms_variants`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "theme_set(dms_variants.plotnine_themes.theme_graygrid())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Versions of key software:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using dms_variants version 0.8.5\n"
     ]
    }
   ],
   "source": [
    "print(f\"Using dms_variants version {dms_variants.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ignore warnings that clutter output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the configuration file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('config.yaml') as f:\n",
    "    config = yaml.safe_load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make output directory if needed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(config['counts_dir'], exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize codon variant table\n",
    "Initialize the [CodonVariantTable](https://jbloomlab.github.io/dms_variants/dms_variants.codonvarianttable.html#dms_variants.codonvarianttable.CodonVariantTable) using the wildtype gene sequence and the CSV file with the table of variants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read sequence of 603 nt for SARS-CoV-2 from data/wildtype_sequence.fasta\n",
      "Initializing CodonVariantTable from gene sequence and results/prior_DMS_data/codon_variant_table.csv\n"
     ]
    }
   ],
   "source": [
    "wt_seqrecord = Bio.SeqIO.read(config['wildtype_sequence'], 'fasta')\n",
    "geneseq = str(wt_seqrecord.seq)\n",
    "primary_target = wt_seqrecord.name\n",
    "print(f\"Read sequence of {len(geneseq)} nt for {primary_target} from {config['wildtype_sequence']}\")\n",
    "      \n",
    "print(f\"Initializing CodonVariantTable from gene sequence and {config['codon_variant_table']}\")\n",
    "      \n",
    "variants = dms_variants.codonvarianttable.CodonVariantTable(\n",
    "                geneseq=geneseq,\n",
    "                barcode_variant_file=config['codon_variant_table'],\n",
    "                substitutions_are_codon=True,\n",
    "                substitutions_col='codon_substitutions',\n",
    "                primary_target=primary_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read barcode counts / fates\n",
    "Read data frame with list of all samples (barcode runs):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading list of barcode runs from data/barcode_runs.csv\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>library</th>\n",
       "      <th>sample</th>\n",
       "      <th>date</th>\n",
       "      <th>experiment</th>\n",
       "      <th>antibody</th>\n",
       "      <th>concentration</th>\n",
       "      <th>concentration_units</th>\n",
       "      <th>group</th>\n",
       "      <th>selection</th>\n",
       "      <th>frac_escape</th>\n",
       "      <th>cells_sorted</th>\n",
       "      <th>sample_lib</th>\n",
       "      <th>counts_file</th>\n",
       "      <th>fates_file</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
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       "      <td>expt_24-33</td>\n",
       "      <td>none</td>\n",
       "      <td>0</td>\n",
       "      <td>ng_per_mL</td>\n",
       "      <td>clinical_serum</td>\n",
       "      <td>reference</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160000000.0</td>\n",
       "      <td>expt_24-33-none-0-reference_lib1</td>\n",
       "      <td>results/counts/expt_24-33-none-0-reference_lib1_counts.csv</td>\n",
       "      <td>results/counts/expt_24-33-none-0-reference_lib1_fates.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
       "      <td>200904</td>\n",
       "      <td>expt_24-33</td>\n",
       "      <td>none</td>\n",
       "      <td>0</td>\n",
       "      <td>ng_per_mL</td>\n",
       "      <td>clinical_serum</td>\n",
       "      <td>reference</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160000000.0</td>\n",
       "      <td>expt_24-33-none-0-reference_lib2</td>\n",
       "      <td>results/counts/expt_24-33-none-0-reference_lib2_counts.csv</td>\n",
       "      <td>results/counts/expt_24-33-none-0-reference_lib2_fates.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_27-CB6-400-escape</td>\n",
       "      <td>200904</td>\n",
       "      <td>expt_27</td>\n",
       "      <td>CB6</td>\n",
       "      <td>400</td>\n",
       "      <td>ng_per_mL</td>\n",
       "      <td>clinical_serum</td>\n",
       "      <td>escape</td>\n",
       "      <td>0.222</td>\n",
       "      <td>1907893.0</td>\n",
       "      <td>expt_27-CB6-400-escape_lib1</td>\n",
       "      <td>results/counts/expt_27-CB6-400-escape_lib1_counts.csv</td>\n",
       "      <td>results/counts/expt_27-CB6-400-escape_lib1_fates.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>expt_27-CB6-400-escape</td>\n",
       "      <td>200904</td>\n",
       "      <td>expt_27</td>\n",
       "      <td>CB6</td>\n",
       "      <td>400</td>\n",
       "      <td>ng_per_mL</td>\n",
       "      <td>clinical_serum</td>\n",
       "      <td>escape</td>\n",
       "      <td>0.225</td>\n",
       "      <td>927804.0</td>\n",
       "      <td>expt_27-CB6-400-escape_lib2</td>\n",
       "      <td>results/counts/expt_27-CB6-400-escape_lib2_counts.csv</td>\n",
       "      <td>results/counts/expt_27-CB6-400-escape_lib2_fates.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-REGN10933-400-escape</td>\n",
       "      <td>200904</td>\n",
       "      <td>expt_24</td>\n",
       "      <td>REGN10933</td>\n",
       "      <td>400</td>\n",
       "      <td>ng_per_mL</td>\n",
       "      <td>clinical_serum</td>\n",
       "      <td>escape</td>\n",
       "      <td>0.174</td>\n",
       "      <td>1252342.0</td>\n",
       "      <td>expt_24-REGN10933-400-escape_lib1</td>\n",
       "      <td>results/counts/expt_24-REGN10933-400-escape_lib1_counts.csv</td>\n",
       "      <td>results/counts/expt_24-REGN10933-400-escape_lib1_fates.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>expt_24-REGN10933-400-escape</td>\n",
       "      <td>200904</td>\n",
       "      <td>expt_24</td>\n",
       "      <td>REGN10933</td>\n",
       "      <td>400</td>\n",
       "      <td>ng_per_mL</td>\n",
       "      <td>clinical_serum</td>\n",
       "      <td>escape</td>\n",
       "      <td>0.154</td>\n",
       "      <td>984237.0</td>\n",
       "      <td>expt_24-REGN10933-400-escape_lib2</td>\n",
       "      <td>results/counts/expt_24-REGN10933-400-escape_lib2_counts.csv</td>\n",
       "      <td>results/counts/expt_24-REGN10933-400-escape_lib2_fates.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_26-REGN10933+REGN10987-400-escape</td>\n",
       "      <td>200904</td>\n",
       "      <td>expt_26</td>\n",
       "      <td>REGN10933+REGN10987</td>\n",
       "      <td>400</td>\n",
       "      <td>ng_per_mL</td>\n",
       "      <td>clinical_serum</td>\n",
       "      <td>escape</td>\n",
       "      <td>0.101</td>\n",
       "      <td>635898.0</td>\n",
       "      <td>expt_26-REGN10933+REGN10987-400-escape_lib1</td>\n",
       "      <td>results/counts/expt_26-REGN10933+REGN10987-400-escape_lib1_counts.csv</td>\n",
       "      <td>results/counts/expt_26-REGN10933+REGN10987-400-escape_lib1_fates.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>expt_26-REGN10933+REGN10987-400-escape</td>\n",
       "      <td>200904</td>\n",
       "      <td>expt_26</td>\n",
       "      <td>REGN10933+REGN10987</td>\n",
       "      <td>400</td>\n",
       "      <td>ng_per_mL</td>\n",
       "      <td>clinical_serum</td>\n",
       "      <td>escape</td>\n",
       "      <td>0.083</td>\n",
       "      <td>585188.0</td>\n",
       "      <td>expt_26-REGN10933+REGN10987-400-escape_lib2</td>\n",
       "      <td>results/counts/expt_26-REGN10933+REGN10987-400-escape_lib2_counts.csv</td>\n",
       "      <td>results/counts/expt_26-REGN10933+REGN10987-400-escape_lib2_fates.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_25-REGN10987-400-escape</td>\n",
       "      <td>200904</td>\n",
       "      <td>expt_25</td>\n",
       "      <td>REGN10987</td>\n",
       "      <td>400</td>\n",
       "      <td>ng_per_mL</td>\n",
       "      <td>clinical_serum</td>\n",
       "      <td>escape</td>\n",
       "      <td>0.148</td>\n",
       "      <td>1155004.0</td>\n",
       "      <td>expt_25-REGN10987-400-escape_lib1</td>\n",
       "      <td>results/counts/expt_25-REGN10987-400-escape_lib1_counts.csv</td>\n",
       "      <td>results/counts/expt_25-REGN10987-400-escape_lib1_fates.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>expt_25-REGN10987-400-escape</td>\n",
       "      <td>200904</td>\n",
       "      <td>expt_25</td>\n",
       "      <td>REGN10987</td>\n",
       "      <td>400</td>\n",
       "      <td>ng_per_mL</td>\n",
       "      <td>clinical_serum</td>\n",
       "      <td>escape</td>\n",
       "      <td>0.150</td>\n",
       "      <td>1091502.0</td>\n",
       "      <td>expt_25-REGN10987-400-escape_lib2</td>\n",
       "      <td>results/counts/expt_25-REGN10987-400-escape_lib2_counts.csv</td>\n",
       "      <td>results/counts/expt_25-REGN10987-400-escape_lib2_fates.csv</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Reading list of barcode runs from {config['barcode_runs']}\")\n",
    "\n",
    "barcode_runs = (pd.read_csv(config['barcode_runs'])\n",
    "                .assign(sample_lib=lambda x: x['sample'] + '_' + x['library'],\n",
    "                        counts_file=lambda x: config['counts_dir'] + '/' + x['sample_lib'] + '_counts.csv',\n",
    "                        fates_file=lambda x: config['counts_dir'] + '/' + x['sample_lib'] + '_fates.csv',\n",
    "                        )\n",
    "                .drop(columns='R1')  # don't need this column, and very large\n",
    "                )\n",
    "\n",
    "assert all(map(os.path.isfile, barcode_runs['counts_file'])), 'missing some counts files'\n",
    "assert all(map(os.path.isfile, barcode_runs['fates_file'])), 'missing some fates files'\n",
    "\n",
    "display(HTML(barcode_runs.to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confirm sample / library combinations unique:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert len(barcode_runs) == len(barcode_runs.groupby(['sample', 'library']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure the the libraries for which we have barcode runs are all in our variant table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "unknown_libs = set(barcode_runs['library']) - set(variants.libraries)\n",
    "if unknown_libs:\n",
    "    raise ValueError(f\"Libraries with barcode runs not in variant table: {unknown_libs}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now concatenate the barcode counts and fates for each sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First few lines of counts data frame:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>barcode</th>\n",
       "      <th>count</th>\n",
       "      <th>library</th>\n",
       "      <th>sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>TTCCAAAATATTGTCA</td>\n",
       "      <td>263040</td>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TAGTAACAATGCGGTA</td>\n",
       "      <td>174279</td>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TTAATTAGTATCAGGT</td>\n",
       "      <td>60387</td>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TTAGATGAAGCCAGTA</td>\n",
       "      <td>51224</td>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TAATATGCAAGAGCCA</td>\n",
       "      <td>48555</td>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First few lines of fates data frame:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>fate</th>\n",
       "      <th>count</th>\n",
       "      <th>library</th>\n",
       "      <th>sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>valid barcode</td>\n",
       "      <td>53957723</td>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>invalid barcode</td>\n",
       "      <td>5653429</td>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>low quality barcode</td>\n",
       "      <td>5031936</td>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>failed chastity filter</td>\n",
       "      <td>3009381</td>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unparseable barcode</td>\n",
       "      <td>1204055</td>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = pd.concat([pd.read_csv(f) for f in barcode_runs['counts_file']],\n",
    "                   sort=False,\n",
    "                   ignore_index=True)\n",
    "\n",
    "print('First few lines of counts data frame:')\n",
    "display(HTML(counts.head().to_html(index=False)))\n",
    "\n",
    "fates = pd.concat([pd.read_csv(f) for f in barcode_runs['fates_file']],\n",
    "                  sort=False,\n",
    "                  ignore_index=True)\n",
    "\n",
    "print('First few lines of fates data frame:')\n",
    "display(HTML(fates.head().to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine fates of parsed barcodes\n",
    "First, we'll analyze the \"fates\" of the parsed barcodes.\n",
    "These fates represent what happened to each Illumina read we parsed:\n",
    " - Did the barcode read fail the Illumina chastity filter?\n",
    " - Was the barcode *unparseable* (i.e., the read didn't appear to be a valid barcode based on flanking regions)?\n",
    " - Was the barcode sequence too *low quality* based on the Illumina quality scores?\n",
    " - Was the barcode parseable but *invalid* (i.e., not in our list of variant-associated barcodes in the codon variant table)?\n",
    " - Was the barcode *valid*, and so will be added to variant counts.\n",
    " \n",
    "First, we just write a CSV file with all the barcode fates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing barcode fates to results/counts/barcode_fates.csv\n"
     ]
    }
   ],
   "source": [
    "fatesfile = os.path.join(config['counts_dir'], 'barcode_fates.csv')\n",
    "print(f\"Writing barcode fates to {fatesfile}\")\n",
    "fates.to_csv(fatesfile, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we tabulate the barcode fates in wide format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fate</th>\n",
       "      <th>failed chastity filter</th>\n",
       "      <th>invalid barcode</th>\n",
       "      <th>low quality barcode</th>\n",
       "      <th>unparseable barcode</th>\n",
       "      <th>valid barcode</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample</th>\n",
       "      <th>library</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 rowspan=\"2\" valign=\"top\">expt_24-33-none-0-reference</th>\n",
       "      <th>lib1</th>\n",
       "      <td>3.0e+06</td>\n",
       "      <td>5.7e+06</td>\n",
       "      <td>5.0e+06</td>\n",
       "      <td>1.2e+06</td>\n",
       "      <td>5.4e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>3.2e+06</td>\n",
       "      <td>5.5e+06</td>\n",
       "      <td>5.3e+06</td>\n",
       "      <td>1.2e+06</td>\n",
       "      <td>5.8e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">expt_24-REGN10933-400-escape</th>\n",
       "      <th>lib1</th>\n",
       "      <td>0.0e+00</td>\n",
       "      <td>3.3e+05</td>\n",
       "      <td>3.4e+05</td>\n",
       "      <td>7.4e+04</td>\n",
       "      <td>4.5e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>0.0e+00</td>\n",
       "      <td>3.8e+05</td>\n",
       "      <td>2.7e+05</td>\n",
       "      <td>9.8e+04</td>\n",
       "      <td>3.4e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">expt_25-REGN10987-400-escape</th>\n",
       "      <th>lib1</th>\n",
       "      <td>0.0e+00</td>\n",
       "      <td>1.2e+06</td>\n",
       "      <td>3.7e+05</td>\n",
       "      <td>9.9e+04</td>\n",
       "      <td>4.0e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>0.0e+00</td>\n",
       "      <td>4.9e+05</td>\n",
       "      <td>4.1e+05</td>\n",
       "      <td>1.5e+05</td>\n",
       "      <td>5.2e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">expt_26-REGN10933+REGN10987-400-escape</th>\n",
       "      <th>lib1</th>\n",
       "      <td>0.0e+00</td>\n",
       "      <td>3.7e+05</td>\n",
       "      <td>2.4e+05</td>\n",
       "      <td>8.7e+04</td>\n",
       "      <td>3.1e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>0.0e+00</td>\n",
       "      <td>2.7e+05</td>\n",
       "      <td>2.0e+05</td>\n",
       "      <td>7.1e+04</td>\n",
       "      <td>2.4e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">expt_27-CB6-400-escape</th>\n",
       "      <th>lib1</th>\n",
       "      <td>0.0e+00</td>\n",
       "      <td>2.0e+06</td>\n",
       "      <td>6.2e+05</td>\n",
       "      <td>1.5e+05</td>\n",
       "      <td>6.7e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>0.0e+00</td>\n",
       "      <td>3.6e+05</td>\n",
       "      <td>2.8e+05</td>\n",
       "      <td>9.7e+04</td>\n",
       "      <td>3.6e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(fates\n",
    "             .pivot_table(columns='fate',\n",
    "                          values='count',\n",
    "                          index=['sample', 'library'])\n",
    "             .applymap('{:.1e}'.format)  # scientific notation\n",
    "             .to_html()\n",
    "             ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we plot the barcode-read fates for each library / sample, showing the bars for valid barcodes in orange and the others in gray.\n",
    "We see that the largest fraction of barcode reads correspond to valid barcodes, and most of the others are invalid barcodes (probably because the map to variants that aren't present in our variant table since we didn't associate all variants with barcodes). The exception to this is lib2 Titeseq_03_bin3; the PCR for this sample in the original sequencing run failed, so we followed it up with a single MiSeq lane. We did not filter out the PhiX reads from this data before parsing, so these PhiX reads will deflate the fraction of valid barcode reads as expected, but does not indicate any problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x600 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncol = 4\n",
    "nfacets = len(fates.groupby(['sample', 'library']))\n",
    "\n",
    "barcode_fate_plot = (\n",
    "    ggplot(\n",
    "        fates\n",
    "        .assign(sample=lambda x: pd.Categorical(x['sample'],\n",
    "                                                x['sample'].unique(),\n",
    "                                                ordered=True),\n",
    "                fate=lambda x: pd.Categorical(x['fate'],\n",
    "                                              x['fate'].unique(),\n",
    "                                              ordered=True),\n",
    "                is_valid=lambda x: x['fate'] == 'valid barcode'\n",
    "                ), \n",
    "        aes('fate', 'count', fill='is_valid')) +\n",
    "    geom_bar(stat='identity') +\n",
    "    facet_wrap('~ sample + library', ncol=ncol) +\n",
    "    scale_fill_manual(CBPALETTE, guide=False) +\n",
    "    theme(figure_size=(3.25 * ncol, 2 * math.ceil(nfacets / ncol)),\n",
    "          axis_text_x=element_text(angle=90),\n",
    "          panel_grid_major_x=element_blank()\n",
    "          ) +\n",
    "    scale_y_continuous(labels=dms_variants.utils.latex_sci_not,\n",
    "                       name='number of reads')\n",
    "    )\n",
    "\n",
    "_ = barcode_fate_plot.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add barcode counts to variant table\n",
    "Now we use the [CodonVariantTable.add_sample_counts_df](https://jbloomlab.github.io/dms_variants/dms_variants.codonvarianttable.html#dms_variants.codonvarianttable.CodonVariantTable.add_sample_counts_df) method to add the barcode counts to the variant table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "variants.add_sample_counts_df(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variant table now has a `variant_count_df` attribute that gives a data frame of all the variant counts.\n",
    "Here are the first few lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>library</th>\n",
       "      <th>sample</th>\n",
       "      <th>barcode</th>\n",
       "      <th>count</th>\n",
       "      <th>variant_call_support</th>\n",
       "      <th>codon_substitutions</th>\n",
       "      <th>aa_substitutions</th>\n",
       "      <th>n_codon_substitutions</th>\n",
       "      <th>n_aa_substitutions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
       "      <td>TCCAAGGAAATTGTGA</td>\n",
       "      <td>312</td>\n",
       "      <td>3</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
       "      <td>CGATGAATTCAGCTCT</td>\n",
       "      <td>307</td>\n",
       "      <td>12</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
       "      <td>TATCACAACGTAGACC</td>\n",
       "      <td>201</td>\n",
       "      <td>6</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
       "      <td>TACCTATTCCCATAAA</td>\n",
       "      <td>147</td>\n",
       "      <td>2</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib1</td>\n",
       "      <td>expt_24-33-none-0-reference</td>\n",
       "      <td>TTACAAATAACACCAT</td>\n",
       "      <td>143</td>\n",
       "      <td>2</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(variants.variant_count_df.head().to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the variant counts data frame to a CSV file.\n",
    "It can then be used to re-initialize a [CodonVariantTable](https://jbloomlab.github.io/dms_variants/dms_variants.codonvarianttable.html#dms_variants.codonvarianttable.CodonVariantTable) via its [from_variant_count_df](https://jbloomlab.github.io/dms_variants/dms_variants.codonvarianttable.html#dms_variants.codonvarianttable.CodonVariantTable.from_variant_count_df) method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing variant counts to results/counts/variant_counts.csv\n"
     ]
    }
   ],
   "source": [
    "print(f\"Writing variant counts to {config['variant_counts']}\")\n",
    "variants.variant_count_df.to_csv(config['variant_counts'], index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [CodonVariantTable](https://jbloomlab.github.io/dms_variants/dms_variants.codonvarianttable.html#dms_variants.codonvarianttable.CodonVariantTable) has lots of nice functions that can be used to analyze the counts it contains.\n",
    "However, we do that in the next notebook so we don't have to re-run this entire (rather computationally intensive) notebook every time we want to analyze a new aspect of the counts."
   ]
  }
 ],
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  "language_info": {
   "name": "python"
  }
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 "nbformat_minor": 4
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