3_compute_invivo_logos.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T16:19:38.521418Z",
     "start_time": "2021-04-05T16:19:37.033799Z"
    }
   },
   "outputs": [],
   "source": [
    "# Standard imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import logomaker\n",
    "import time\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Import marginalization function\n",
    "from utils import x_to_ct_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T16:19:48.914108Z",
     "start_time": "2021-04-05T16:19:38.523283Z"
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N10</th>\n",
       "      <th>treatment</th>\n",
       "      <th>replicate</th>\n",
       "      <th>DNA</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAAAAAAAAA</td>\n",
       "      <td>both</td>\n",
       "      <td>rep1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAAAAAAAAC</td>\n",
       "      <td>both</td>\n",
       "      <td>rep1</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAAAAAAAAG</td>\n",
       "      <td>both</td>\n",
       "      <td>rep1</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAAAAAAAAT</td>\n",
       "      <td>both</td>\n",
       "      <td>rep1</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAAAAAAACA</td>\n",
       "      <td>both</td>\n",
       "      <td>rep1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          N10 treatment replicate  DNA  4  5  6  7  8  9  10\n",
       "0  AAAAAAAAAA      both      rep1   18  0  0  0  0  0  0   0\n",
       "1  AAAAAAAAAC      both      rep1   41  0  0  0  0  0  0   0\n",
       "2  AAAAAAAAAG      both      rep1   38  0  0  0  0  0  0   0\n",
       "3  AAAAAAAAAT      both      rep1   30  0  0  0  0  0  0   0\n",
       "4  AAAAAAAACA      both      rep1   14  0  0  0  0  0  0   0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load in vivo data\n",
    "df = pd.read_csv('data/in_vivo_data.csv.gz', compression='gzip')\n",
    "\n",
    "# Rename columns and samples for added convenience\n",
    "df.rename(columns={'DNA count':'DNA', 'sample':'replicate'}, inplace=True)\n",
    "df['treatment'] = df['treatment'].map({'background':'none', \n",
    "                                       'PPP+P':'Rpp', \n",
    "                                       'OH+P':'PNK', \n",
    "                                       'All_end':'both'})\n",
    "df['replicate'] = df['replicate'].map({'KS112':'rep1', \n",
    "                                       'KS113':'rep2', \n",
    "                                       'KS114':'rep3'})\n",
    "df.columns  = [int(col[1:]) if col[0]=='p' else col for col in df.columns]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T16:19:58.409008Z",
     "start_time": "2021-04-05T16:19:48.915423Z"
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    "code_folding": []
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>replicate</th>\n",
       "      <th colspan=\"10\" halign=\"left\">rep1</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">rep3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <th colspan=\"7\" halign=\"left\">PNK</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Rpp</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"3\" halign=\"left\">both</th>\n",
       "      <th colspan=\"7\" halign=\"left\">none</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>...</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N10</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>AAAAAAAAAA</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAAAAAAAC</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAAAAAAAG</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAAAAAAAT</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAAAAAACA</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "replicate  rep1                              ... rep3                         \\\n",
       "treatment   PNK                   Rpp        ... both       none               \n",
       "             4  5  6  7  8  9  10  4  5  6   ...   8  9  10   4  5  6  7  8    \n",
       "N10                                          ...                               \n",
       "AAAAAAAAAA    0  0  0  0  0  0  0   0  0  0  ...    0  0  0    0  0  0  0  0   \n",
       "AAAAAAAAAC    0  0  2  0  0  0  0   0  0  0  ...    0  0  0    0  0  0  0  0   \n",
       "AAAAAAAAAG    0  1  0  0  0  0  0   0  0  0  ...    0  0  0    0  0  0  0  0   \n",
       "AAAAAAAAAT    0  0  0  0  0  0  0   0  0  0  ...    0  0  0    0  0  0  0  0   \n",
       "AAAAAAAACA    0  0  0  0  0  0  0   0  0  0  ...    0  0  0    0  0  0  0  0   \n",
       "\n",
       "replicate         \n",
       "treatment         \n",
       "           9  10  \n",
       "N10               \n",
       "AAAAAAAAAA  0  0  \n",
       "AAAAAAAAAC  0  0  \n",
       "AAAAAAAAAG  0  0  \n",
       "AAAAAAAAAT  0  0  \n",
       "AAAAAAAACA  0  0  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define end positions\n",
    "poss = list(range(4,11))\n",
    "L = 10\n",
    "\n",
    "# Pivot dataframe \n",
    "ct_df = df.pivot(index='N10', \n",
    "                 columns=['treatment','replicate'], \n",
    "                 values=poss)\n",
    "\n",
    "# Reorder columns\n",
    "ct_df = ct_df.reorder_levels([2,1,0], axis=1)\n",
    "sorted_cols = ct_df.columns.sortlevel()[0]\n",
    "ct_df = ct_df[sorted_cols]\n",
    "\n",
    "# Preview ct_df\n",
    "ct_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T16:19:58.417683Z",
     "start_time": "2021-04-05T16:19:58.410081Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "replicate  sample\n",
       "rep1       none      1.000000\n",
       "rep2       none      1.000000\n",
       "rep3       none      1.000000\n",
       "rep1       Rpp       0.910393\n",
       "rep2       Rpp       0.922886\n",
       "rep3       Rpp       0.987480\n",
       "rep1       PNK       0.755147\n",
       "rep2       PNK       0.751011\n",
       "rep3       PNK       0.753237\n",
       "rep1       both      0.911045\n",
       "rep2       both      0.896461\n",
       "rep3       both      0.922206\n",
       "Name: efficiency, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load pre-computed efficiencies\n",
    "eps = pd.read_csv('csv_results/efficiencies.csv', index_col=[0,1])['efficiency']\n",
    "eps.index = eps.index.reorder_levels([1,0])\n",
    "eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T16:19:58.534350Z",
     "start_time": "2021-04-05T16:19:58.418730Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create list of dinucs and dinuc display names\n",
    "dinucs = ['NN'] + [b1+b2 for b1 in 'ACGT' for b2 in 'ACGT']\n",
    "#dinucs = dinucs[:2]  # For testing\n",
    "primers = [(d[0]+'p'+d[1]).replace('T','U') for d in dinucs]\n",
    "\n",
    "# Create dataframe to hold counts by position\n",
    "replicates = ['rep1','rep2','rep3']\n",
    "oh_stat_df = pd.DataFrame(columns=['primer', 'replicate']+poss[:-1])\n",
    "for primer in primers:\n",
    "    for rep in replicates:\n",
    "        oh_stat_df = oh_stat_df.append({'primer':primer, 'replicate':rep}, ignore_index=True)\n",
    "oh_stat_df = oh_stat_df.fillna(0)\n",
    "oh_stat_df = oh_stat_df.groupby(['primer','replicate']).first()\n",
    "#oh_stat_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T16:21:53.862468Z",
     "start_time": "2021-04-05T16:19:58.535477Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rep1: NpN ApA ApC ApG ApU CpA CpC CpG CpU GpA GpC GpG GpU UpA UpC UpG UpU \n",
      "rep2: NpN ApA ApC ApG ApU CpA CpC CpG CpU GpA GpC GpG GpU UpA UpC UpG UpU \n",
      "rep3: NpN ApA ApC ApG ApU CpA CpC CpG CpU GpA GpC GpG GpU UpA UpC UpG UpU \n",
      "Done! Execution time:115.32 sec.\n"
     ]
    }
   ],
   "source": [
    "# Let list of all N10 promoters\n",
    "all_seqs = ct_df.index.values\n",
    "\n",
    "# Draw logos within figure\n",
    "t = time.time()\n",
    "\n",
    "# Iterate over replicates\n",
    "for k, rep in enumerate(replicates):\n",
    "    \n",
    "    # Feedback\n",
    "    print(f'{rep}: ', end='')\n",
    "    \n",
    "    # Create dataframe with efficiency-corrected OH counts for each promoter\n",
    "    OH_df = 0.5*(ct_df[(rep,'both')]/eps[(rep,'both')] \n",
    "                 + ct_df[(rep,'PNK')]/eps[(rep,'PNK')]\n",
    "                 - ct_df[(rep,'Rpp')]/eps[(rep,'Rpp')]\n",
    "                 - ct_df[(rep,'none')]/eps[(rep,'none')])\n",
    "    OH_df.tail()\n",
    "\n",
    "    # Create dataframe with efficiency-corrected PPP counts for each promoter\n",
    "    PPP_df = 0.5*(ct_df[(rep,'both')]/eps[(rep,'both')] \n",
    "                 - ct_df[(rep,'PNK')]/eps[(rep,'PNK')]\n",
    "                 + ct_df[(rep,'Rpp')]/eps[(rep,'Rpp')]\n",
    "                 - ct_df[(rep,'none')]/eps[(rep,'none')])\n",
    "    PPP_df.tail()\n",
    "    \n",
    "    # Compute total corrected counts \n",
    "    total_ct = OH_df[poss[:-1]].sum(axis=1) + PPP_df[poss[1:]].sum(axis=1)\n",
    "        \n",
    "    # Iterate over dinucs\n",
    "    for i, dinuc in enumerate(dinucs):\n",
    "        \n",
    "        # Feedback\n",
    "        primer = primers[i]\n",
    "        print(f'{primer} ', end='')\n",
    "\n",
    "        # Iterate over positions\n",
    "        for j, pos in enumerate(poss[:-1]):\n",
    "\n",
    "            # Create filename\n",
    "            file_name = f'csv_logos/invivo_p{pos}_{primer}_{rep}_logo.csv'\n",
    "            \n",
    "            # Get OH corrected counts\n",
    "            OH_ct = OH_df[pos]\n",
    "\n",
    "            # Get indices for promoters that match the desired dinucleotide\n",
    "            if dinuc == 'NN':\n",
    "                seqs = all_seqs\n",
    "            else:\n",
    "                seqs = [seq for seq in all_seqs if seq[(pos-1):(pos+1)]==dinuc]         \n",
    "\n",
    "            # Add OH stats\n",
    "            oh_stat_df.loc[(primer, rep),pos] = OH_df.loc[seqs,pos].sum()\n",
    "                \n",
    "            # Marginalize and regularize OH_ct to get fg_counts_df\n",
    "            fg_counts_df = x_to_ct_df(x=seqs, ct=OH_ct[seqs])\n",
    "            fg_counts_df.index = range(1,L+1)\n",
    "            fg_counts_df = fg_counts_df.where(fg_counts_df>0,0)+1\n",
    "            zero_count_poss = fg_counts_df.index[(fg_counts_df==1).any(axis=1)]\n",
    "\n",
    "            # Marginalize total_ct to get bg_counts_df\n",
    "            bg_counts_df = x_to_ct_df(x=seqs, ct=total_ct[seqs])\n",
    "            bg_counts_df.index = range(1,L+1)\n",
    "            bg_counts_df = bg_counts_df.where(bg_counts_df>0,0)+2*(len(poss)-1)\n",
    "\n",
    "            # Compute enrichment ratio and center values\n",
    "            logo_df = np.log2(fg_counts_df/bg_counts_df)\n",
    "            logo_df.loc[zero_count_poss,:] = 0.0\n",
    "            logo_df = logomaker.transform_matrix(logo_df, center_values=True)\n",
    "\n",
    "            # Modify logo index\n",
    "            logo_df.index = range(1,L+1)\n",
    "\n",
    "            # Set logo values to zero at A-site and P-site (determined by pos)\n",
    "            logo_df.loc[(pos,pos+1),:] = 0\n",
    "            \n",
    "            # Save logo\n",
    "            logo_df.to_csv(file_name)\n",
    "            \n",
    "    # Newline feedback\n",
    "    print('')\n",
    "    \n",
    "# Show execution time\n",
    "print(f'Done! Execution time:{time.time()-t:.2f} sec.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-05T16:21:53.875117Z",
     "start_time": "2021-04-05T16:21:53.864597Z"
    }
   },
   "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>primer</th>\n",
       "      <th>replicate</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ApA</td>\n",
       "      <td>rep1</td>\n",
       "      <td>0.153971</td>\n",
       "      <td>270.469849</td>\n",
       "      <td>3206.209324</td>\n",
       "      <td>2702.402852</td>\n",
       "      <td>1096.586942</td>\n",
       "      <td>555.130115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ApA</td>\n",
       "      <td>rep2</td>\n",
       "      <td>54.453680</td>\n",
       "      <td>245.168443</td>\n",
       "      <td>4051.189959</td>\n",
       "      <td>4812.025962</td>\n",
       "      <td>3521.058243</td>\n",
       "      <td>645.429137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ApA</td>\n",
       "      <td>rep3</td>\n",
       "      <td>40.758572</td>\n",
       "      <td>204.025771</td>\n",
       "      <td>3231.493927</td>\n",
       "      <td>2682.401060</td>\n",
       "      <td>1355.395298</td>\n",
       "      <td>27.347419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ApC</td>\n",
       "      <td>rep1</td>\n",
       "      <td>17.881950</td>\n",
       "      <td>158.577889</td>\n",
       "      <td>3049.709273</td>\n",
       "      <td>5011.971234</td>\n",
       "      <td>129.377103</td>\n",
       "      <td>-124.505645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ApC</td>\n",
       "      <td>rep2</td>\n",
       "      <td>37.747765</td>\n",
       "      <td>299.890478</td>\n",
       "      <td>3748.775250</td>\n",
       "      <td>7070.634930</td>\n",
       "      <td>2676.158825</td>\n",
       "      <td>196.929395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  primer replicate          4           5            6            7  \\\n",
       "0    ApA      rep1   0.153971  270.469849  3206.209324  2702.402852   \n",
       "1    ApA      rep2  54.453680  245.168443  4051.189959  4812.025962   \n",
       "2    ApA      rep3  40.758572  204.025771  3231.493927  2682.401060   \n",
       "3    ApC      rep1  17.881950  158.577889  3049.709273  5011.971234   \n",
       "4    ApC      rep2  37.747765  299.890478  3748.775250  7070.634930   \n",
       "\n",
       "             8           9  \n",
       "0  1096.586942  555.130115  \n",
       "1  3521.058243  645.429137  \n",
       "2  1355.395298   27.347419  \n",
       "3   129.377103 -124.505645  \n",
       "4  2676.158825  196.929395  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save OH counts table\n",
    "out_df = oh_stat_df.copy()\n",
    "out_df.reset_index(inplace=True)\n",
    "out_df.to_csv('csv_results/oh_counts_table.csv')\n",
    "out_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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