{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QC and processing raw barcode counts for Library 1 with the Rho promoter\n",
    "This notebook takes raw barcode counts and processes it into activity scores for each replicate. Then, we do statistics to determine which sequences are significantly different from the Rho promoter alone. There are three biological replicates of RNA and one DNA sample from the input plasmid pool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "sys.path.insert(0, \"utils\")\n",
    "from utils import modeling, plot_utils, quality_control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_utils.set_manuscript_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>DNA</th>\n",
       "      <th>RNA1</th>\n",
       "      <th>RNA2</th>\n",
       "      <th>RNA3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>barcode</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>AACAACAAG</th>\n",
       "      <td>chr16-87432635-87432799_CPPQ_scrambled</td>\n",
       "      <td>3019</td>\n",
       "      <td>148</td>\n",
       "      <td>325</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAACCGC</th>\n",
       "      <td>chr4-119112319-119112483_CPPE_WT</td>\n",
       "      <td>4117</td>\n",
       "      <td>24493</td>\n",
       "      <td>25950</td>\n",
       "      <td>23406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAACGGG</th>\n",
       "      <td>chr7-128854234-128854398_UPCE_WT</td>\n",
       "      <td>86</td>\n",
       "      <td>76</td>\n",
       "      <td>39</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAACTAC</th>\n",
       "      <td>chr4-138107597-138107761_UPPE_WT</td>\n",
       "      <td>827</td>\n",
       "      <td>926</td>\n",
       "      <td>857</td>\n",
       "      <td>659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAACTGT</th>\n",
       "      <td>chr5-31298508-31298672_CPPE_WT</td>\n",
       "      <td>7170</td>\n",
       "      <td>492</td>\n",
       "      <td>392</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAAGCGG</th>\n",
       "      <td>chr16-37868024-37868188_UPPP_MUT-allCrxSites</td>\n",
       "      <td>1199</td>\n",
       "      <td>98</td>\n",
       "      <td>147</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAAGGAC</th>\n",
       "      <td>chr9-72409911-72410075_CBPE_MUT-shape</td>\n",
       "      <td>909</td>\n",
       "      <td>1920</td>\n",
       "      <td>1107</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAAGGCG</th>\n",
       "      <td>chr8-70212225-70212389_CPPP_MUT-allCrxSites</td>\n",
       "      <td>98</td>\n",
       "      <td>33</td>\n",
       "      <td>43</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAAGTAG</th>\n",
       "      <td>chr15-72652217-72652381_CPPE_WT</td>\n",
       "      <td>1851</td>\n",
       "      <td>2557</td>\n",
       "      <td>3203</td>\n",
       "      <td>2688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAAGTCT</th>\n",
       "      <td>chr2-104122476-104122640_CBPE_WT</td>\n",
       "      <td>305</td>\n",
       "      <td>386</td>\n",
       "      <td>357</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAATACC</th>\n",
       "      <td>chr2-5669535-5669699_CPPE_WT</td>\n",
       "      <td>518</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAATAGG</th>\n",
       "      <td>chr9-102717090-102717254_UPPP_MUT-allCrxSites</td>\n",
       "      <td>3577</td>\n",
       "      <td>1750</td>\n",
       "      <td>1563</td>\n",
       "      <td>2113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAATCAC</th>\n",
       "      <td>chr17-34197537-34197701_CPPQ_MUT-allCrxSites</td>\n",
       "      <td>1627</td>\n",
       "      <td>282</td>\n",
       "      <td>52</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAATGAG</th>\n",
       "      <td>chr4-44576957-44577121_UPPE_WT</td>\n",
       "      <td>7030</td>\n",
       "      <td>2706</td>\n",
       "      <td>3000</td>\n",
       "      <td>2267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAATGCT</th>\n",
       "      <td>chr17-74221859-74222023_CPPE_WT</td>\n",
       "      <td>2481</td>\n",
       "      <td>1418</td>\n",
       "      <td>1722</td>\n",
       "      <td>1725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAATGTC</th>\n",
       "      <td>chr11-3422348-3422512_CPRE_MUT-allCrxSites</td>\n",
       "      <td>2912</td>\n",
       "      <td>4651</td>\n",
       "      <td>3679</td>\n",
       "      <td>5035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAATTCA</th>\n",
       "      <td>chr2-164427118-164427282_CPPE_MUT-allCrxSites</td>\n",
       "      <td>2296</td>\n",
       "      <td>2218</td>\n",
       "      <td>2237</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACACACAT</th>\n",
       "      <td>BASAL</td>\n",
       "      <td>6067</td>\n",
       "      <td>926</td>\n",
       "      <td>1183</td>\n",
       "      <td>697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACACAGCC</th>\n",
       "      <td>chr10-61695319-61695483_CPPP_MUT-allCrxSites</td>\n",
       "      <td>195</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACACAGGG</th>\n",
       "      <td>chr12-83444664-83444828_CPPE_MUT-allCrxSites</td>\n",
       "      <td>1680</td>\n",
       "      <td>1700</td>\n",
       "      <td>1539</td>\n",
       "      <td>1886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   label   DNA   RNA1   RNA2  \\\n",
       "barcode                                                                        \n",
       "AACAACAAG         chr16-87432635-87432799_CPPQ_scrambled  3019    148    325   \n",
       "AACAACCGC               chr4-119112319-119112483_CPPE_WT  4117  24493  25950   \n",
       "AACAACGGG               chr7-128854234-128854398_UPCE_WT    86     76     39   \n",
       "AACAACTAC               chr4-138107597-138107761_UPPE_WT   827    926    857   \n",
       "AACAACTGT                 chr5-31298508-31298672_CPPE_WT  7170    492    392   \n",
       "AACAAGCGG   chr16-37868024-37868188_UPPP_MUT-allCrxSites  1199     98    147   \n",
       "AACAAGGAC          chr9-72409911-72410075_CBPE_MUT-shape   909   1920   1107   \n",
       "AACAAGGCG    chr8-70212225-70212389_CPPP_MUT-allCrxSites    98     33     43   \n",
       "AACAAGTAG                chr15-72652217-72652381_CPPE_WT  1851   2557   3203   \n",
       "AACAAGTCT               chr2-104122476-104122640_CBPE_WT   305    386    357   \n",
       "AACAATACC                   chr2-5669535-5669699_CPPE_WT   518      1      1   \n",
       "AACAATAGG  chr9-102717090-102717254_UPPP_MUT-allCrxSites  3577   1750   1563   \n",
       "AACAATCAC   chr17-34197537-34197701_CPPQ_MUT-allCrxSites  1627    282     52   \n",
       "AACAATGAG                 chr4-44576957-44577121_UPPE_WT  7030   2706   3000   \n",
       "AACAATGCT                chr17-74221859-74222023_CPPE_WT  2481   1418   1722   \n",
       "AACAATGTC     chr11-3422348-3422512_CPRE_MUT-allCrxSites  2912   4651   3679   \n",
       "AACAATTCA  chr2-164427118-164427282_CPPE_MUT-allCrxSites  2296   2218   2237   \n",
       "AACACACAT                                          BASAL  6067    926   1183   \n",
       "AACACAGCC   chr10-61695319-61695483_CPPP_MUT-allCrxSites   195     22      0   \n",
       "AACACAGGG   chr12-83444664-83444828_CPPE_MUT-allCrxSites  1680   1700   1539   \n",
       "\n",
       "            RNA3  \n",
       "barcode           \n",
       "AACAACAAG     97  \n",
       "AACAACCGC  23406  \n",
       "AACAACGGG    233  \n",
       "AACAACTAC    659  \n",
       "AACAACTGT    149  \n",
       "AACAAGCGG    206  \n",
       "AACAAGGAC    995  \n",
       "AACAAGGCG     19  \n",
       "AACAAGTAG   2688  \n",
       "AACAAGTCT    204  \n",
       "AACAATACC      5  \n",
       "AACAATAGG   2113  \n",
       "AACAATCAC     30  \n",
       "AACAATGAG   2267  \n",
       "AACAATGCT   1725  \n",
       "AACAATGTC   5035  \n",
       "AACAATTCA   1850  \n",
       "AACACACAT    697  \n",
       "AACACAGCC     36  \n",
       "AACACAGGG   1886  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# File names\n",
    "barcode_count_dir = os.path.join(os.getcwd(), \"Data\", \"Rhodopsin\")\n",
    "barcode_count_files = [\"library1Plasmid.counts\", \"library1Rna1.counts\",\n",
    "                      \"library1Rna2.counts\", \"library1Rna3.counts\"]\n",
    "barcode_count_files = [os.path.join(barcode_count_dir, i) for i in barcode_count_files]\n",
    "\n",
    "# Stuff for downstream functions\n",
    "sample_labels = np.array([\"DNA\", \"RNA1\", \"RNA2\", \"RNA3\"])\n",
    "sample_rna_mask = np.array([False, True, True, True])\n",
    "rna_labels = sample_labels[sample_rna_mask]\n",
    "dna_labels = sample_labels[np.logical_not(sample_rna_mask)]\n",
    "n_samples = len(sample_labels)\n",
    "n_rna_samples = len(rna_labels)\n",
    "n_dna_samples = len(dna_labels)\n",
    "\n",
    "n_barcodes_per_sequence = 3\n",
    "results_dir = barcode_count_dir\n",
    "output_prefix = os.path.join(results_dir, \"library1\")\n",
    "\n",
    "all_sample_counts_df = quality_control.read_bc_count_files(barcode_count_files, sample_labels)\n",
    "all_sample_counts_df.to_csv(f\"{output_prefix}RawBarcodeCounts.txt\", sep=\"\\t\", na_rep=\"NaN\")\n",
    "all_sample_counts_df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Threshold barcode counts, assess reproducibility\n",
    "Set barcodes that are below the DNA cutoff to an NaN (because they are missing from the input plasmid pool) and those that are below any of the RNA cutoffs to zero in all replicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barcodes missing in DNA:\n",
      "Sample DNA: 1090 barcodes\n",
      "1090 barcodes are missing from more than 0 DNA samples.\n",
      "Barcodes off in RNA:\n",
      "Sample RNA1: 1744 barcodes\n",
      "Sample RNA2: 1913 barcodes\n",
      "Sample RNA3: 1491 barcodes\n",
      "2215 barcodes are off in more than 0 RNA samples.\n",
      "There are a total of  157.151 million barcode counts.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cutoffs = [10, 5, 5, 5]\n",
    "threshold_sample_counts_df = quality_control.filter_low_counts(all_sample_counts_df, sample_labels, cutoffs,\n",
    "                                                               dna_labels=dna_labels, bc_per_seq=n_barcodes_per_sequence)\n",
    "fig = quality_control.reproducibility_plots(threshold_sample_counts_df, rna_labels, \"CPM\", big_dimensions=True)\n",
    "plot_utils.save_fig(fig, f\"{output_prefix}Reproducibility\", timestamp=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize RNA barcode counts by plasmid barcode counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_sample_counts_df = quality_control.normalize_rna_by_dna(threshold_sample_counts_df, rna_labels, dna_labels)\n",
    "# Drop DNA\n",
    "barcode_sample_counts_df = normalized_sample_counts_df.drop(columns=dna_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute expression across replicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "activity_replicate_df = quality_control.average_barcodes(barcode_sample_counts_df)\n",
    "activity_replicate_df.to_csv(f\"{output_prefix}ReplicateExpression.txt\", sep=\"\\t\", na_rep=\"NaN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize to basal, average across replicates, do statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ryan/Documents/DBBS/CohenLab/Manuscripts/CRX-Information-Content/utils/quality_control.py:408: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  cov = std / mean\n"
     ]
    }
   ],
   "source": [
    "sequence_expression_df = quality_control.basal_normalize(activity_replicate_df, \"BASAL\")\n",
    "# Compare vs. basal\n",
    "sequence_expression_df[\"expression_pvalue\"] = quality_control.log_ttest_vs_basal(activity_replicate_df, \"BASAL\")\n",
    "sequence_expression_df[\"expression_qvalue\"] = modeling.fdr(sequence_expression_df[\"expression_pvalue\"])\n",
    "\n",
    "# Save to file\n",
    "sequence_expression_df.to_csv(f\"{output_prefix}TotalExpressionSummary.txt\", sep=\"\\t\", na_rep=\"NaN\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}