code/segment_analysis/TemporalCorrelation_mRNA_vRNA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3c2e1358-40f9-4ecf-9e18-ae6cd4251834",
   "metadata": {},
   "source": [
    "## Temporal correlation between single mRNA segments and total vRNA (Supplementary Figure 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f7ae5856-652a-4dd4-acb6-c9e46aad805f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "77e18544-e5dd-417a-876b-f5b0d60f0c95",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filterDF(df, repVal=None, moiVal=None, hpiVal=None, moleculeVal=None, segmentVal=None):\n",
    "    filtered_df = df\n",
    "    if repVal is not None:\n",
    "        filtered_df = filtered_df[filtered_df.rep == repVal]\n",
    "    \n",
    "    if moiVal is not None:\n",
    "        filtered_df = filtered_df[filtered_df.moi == moiVal]\n",
    "    \n",
    "    if hpiVal is not None:\n",
    "        filtered_df = filtered_df[filtered_df.hpi == hpiVal]\n",
    "    \n",
    "    if moleculeVal is not None:\n",
    "        filtered_df = filtered_df[filtered_df.molecule == moleculeVal]\n",
    "    \n",
    "    if segmentVal is not None:\n",
    "        filtered_df = filtered_df[filtered_df.segment == segmentVal]\n",
    "    \n",
    "    return filtered_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e5e186-07eb-484b-a5d8-6869708ad237",
   "metadata": {},
   "source": [
    "## Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "387516c8-1933-433d-ab7c-70e34e574d33",
   "metadata": {},
   "outputs": [],
   "source": [
    "spots=pd.read_csv(\"analysis/spot_detection/seq_2nt/all_spots.tsv.xz\",sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c90ce7d9-67f0-44e8-aa67-eb6e29399a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Filter groups with at least 5 rows\n",
    "filtered_spots = spots.groupby(['rep', 'moi', 'hpi', 'fov', 'cell']).filter(lambda x: len(x) >= 5)\n",
    "\n",
    "# Step 2: Calculate the number of unique cells per group\n",
    "unique_cells = filtered_spots.groupby(['rep', 'moi', 'hpi', 'fov'])['cell'].transform('nunique')\n",
    "\n",
    "# Step 3: Assign the calculated unique cells count to a new column\n",
    "filtered_spots['infected_cells'] = unique_cells\n",
    "\n",
    "# Step 4: Reset the index\n",
    "spots1 = filtered_spots.reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9228cd7f-512d-4b36-867d-a3421637b167",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the order of the segments for reordering\n",
    "segment_order = [\"PB2\",\"PB1\",\"PA\",\"HA\",\"NP\",\"NA\",\"M\",\"NS\"]\n",
    "\n",
    "# Perform the transformations\n",
    "spots2 = (spots1\n",
    "    .assign(target=np.where(spots1['target'].str.contains(','), 'multi', spots1['target']))\n",
    "    .assign(segment=spots1['target'].str.split(' ').str[0],\n",
    "            molecule=spots1['target'].str.split(' ').str[1])\n",
    "    .reset_index()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f5271858-ca29-4963-ab6f-ba38209b5ac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform the transformations\n",
    "spots3 = (spots2\n",
    "    .assign(segment=pd.Categorical(spots2['segment'], categories=segment_order, ordered=True))\n",
    "    .groupby(['rep', 'moi', 'hpi', 'fov', 'segment', 'molecule', 'ambiguous', 'infected_cells'],observed=False)\n",
    "    .agg(spots=('target', 'size'),\n",
    "         median_intensity=('intensity', 'median'),\n",
    "         mean_radius=('radius', 'median'))\n",
    "    .reset_index()\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9f7d35e-6ec6-4b90-8629-5886425ff05c",
   "metadata": {},
   "outputs": [],
   "source": [
    "spots_summary = (spots3\n",
    "    .assign(spots_per_infected_cell=lambda df: df['spots'] / df['infected_cells'])\n",
    "    .reset_index()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f65ea001",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter for moi > 0 (should no longer be necessary)\n",
    "filtered_data = spots_summary[spots_summary['moi'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "33c3e405-52a6-49fa-8f97-50d6d87500b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by 'moi', 'hpi', 'segment', 'molecule' and calculate mean of 'spots_per_infected_cell'\n",
    "filtered_data.loc[:, 'mean_n'] = (filtered_data\n",
    "    .groupby(['moi', 'hpi', 'segment', 'molecule'], observed=False)['spots_per_infected_cell']\n",
    "    .transform('mean'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "03ecc2c3-2a9c-41ea-a2a2-cbf2ac6e5a0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter out rows where 'segment' is 'missing' or 'multi'\n",
    "filtered_data = filtered_data[~filtered_data['segment'].isin(['missing', 'multi'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ee6e393-1ed9-4ece-86cd-4b772599c84d",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_data_mRNA_03=filterDF(filtered_data, repVal=None, moiVal=0.3, hpiVal=None, moleculeVal='mRNA', segmentVal=None)\n",
    "filtered_data_mRNA_1=filterDF(filtered_data, repVal=None, moiVal=1, hpiVal=None, moleculeVal='mRNA', segmentVal=None)\n",
    "filtered_data_vRNA_03=filterDF(filtered_data, repVal=None, moiVal=0.3, hpiVal=None, moleculeVal='vRNA', segmentVal=None)\n",
    "filtered_data_vRNA_1=filterDF(filtered_data, repVal=None, moiVal=1, hpiVal=None, moleculeVal='vRNA', segmentVal=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "79b9aa36-9fa6-42ea-8952-a7914d590f62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by the specified columns and summarize the data\n",
    "grouped_data = filtered_data.groupby(['rep', 'moi', 'hpi', 'fov', 'molecule'],observed=False).agg(\n",
    "    spots=pd.NamedAgg(column='spots', aggfunc='sum'),\n",
    "    spots_per_infected_cell=pd.NamedAgg(column='spots_per_infected_cell', aggfunc='sum')\n",
    ").reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4c47599f-18c2-4861-b3f1-a01588f13feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Further group by 'moi', 'hpi', and 'molecule' and calculate the mean\n",
    "grouped_data['mean_n'] = grouped_data.groupby(['moi', 'hpi', 'molecule'])['spots_per_infected_cell'].transform('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0724ec04-1a6a-45c1-b551-76ce8ed5ff5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_grouped_data_mRNA_03=filterDF(grouped_data, repVal=None, moiVal=0.3, hpiVal=None, moleculeVal='mRNA', segmentVal=None)\n",
    "filtered_grouped_data_mRNA_1=filterDF(grouped_data, repVal=None, moiVal=1, hpiVal=None, moleculeVal='mRNA', segmentVal=None)\n",
    "filtered_grouped_data_vRNA_03=filterDF(grouped_data, repVal=None, moiVal=0.3, hpiVal=None, moleculeVal='vRNA', segmentVal=None)\n",
    "filtered_grouped_data_vRNA_1=filterDF(grouped_data, repVal=None, moiVal=1, hpiVal=None, moleculeVal='vRNA', segmentVal=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4bd8f47-afd9-499e-8626-d042eb52e5aa",
   "metadata": {},
   "source": [
    "## Calculating the correlation coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d889c596-18a9-457c-9528-91b5a0c17852",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize an empty list to store dictionaries representing rows\n",
    "df=filtered_data_mRNA_03\n",
    "moleculeVal='vRNA'\n",
    "rows = []\n",
    "moiVal=0.3\n",
    "for segmentVal in df.segment.unique():\n",
    "    filtered=filterDF(filtered_data_mRNA_03, repVal=None, moiVal=moiVal, hpiVal=None, moleculeVal=None, segmentVal=segmentVal)\n",
    "    mean_Segm=filtered.groupby('hpi').first()\n",
    "    mean_vRNA=filtered_grouped_data_vRNA_03.groupby('hpi').first()\n",
    "    pearson_corr =stats.pearsonr(mean_Segm['mean_n'],mean_vRNA['mean_n'])\n",
    "    spearman_corr =stats.spearmanr(mean_Segm['mean_n'],mean_vRNA['mean_n'])\n",
    "    new_row = {'moi': moiVal, 'molecule': 'total '+moleculeVal, 'segment': segmentVal, 'PearsonCorrelation': pearson_corr[0],'Pearson_p-Value': pearson_corr[1],\n",
    "               'SpearmanCorrelation': spearman_corr[0],'Spearman_p-Value': spearman_corr[1]}\n",
    "    rows.append(new_row)\n",
    "moiVal=1\n",
    "df=filtered_data_mRNA_1\n",
    "for segmentVal in df.segment.unique():\n",
    "    filtered=filterDF(filtered_data_mRNA_1, repVal=None, moiVal=moiVal, hpiVal=None, moleculeVal=None, segmentVal=segmentVal)\n",
    "    mean_Segm=filtered.groupby('hpi').first()\n",
    "    mean_vRNA=filtered_grouped_data_vRNA_1.groupby('hpi').first()\n",
    "    pearson_corr =stats.pearsonr(mean_Segm['mean_n'],mean_vRNA['mean_n'])\n",
    "    spearman_corr =stats.spearmanr(mean_Segm['mean_n'],mean_vRNA['mean_n'])\n",
    "    new_row = {'moi': moiVal, 'molecule': 'total '+moleculeVal, 'segment': segmentVal, 'PearsonCorrelation': pearson_corr[0],'Pearson_p-Value': pearson_corr[1],\n",
    "               'SpearmanCorrelation': spearman_corr[0],'Spearman_p-Value': spearman_corr[1]}\n",
    "    rows.append(new_row)\n",
    "# Convert the list of dictionaries to a DataFrame\n",
    "df_res = pd.DataFrame(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "40e5ded0-ab85-4db9-9809-11783968e6d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_res.to_csv('analysis/temporal_correlation/correlation_total_vRNA.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f463f7e2-bb9e-45c1-b06d-8cff01a4f77d",
   "metadata": {},
   "source": [
    "## Plotting the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cab6b1f1-f72a-460c-a1bd-1876ed124b57",
   "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>moi</th>\n",
       "      <th>molecule</th>\n",
       "      <th>segment</th>\n",
       "      <th>PearsonCorrelation</th>\n",
       "      <th>Pearson_p-Value</th>\n",
       "      <th>SpearmanCorrelation</th>\n",
       "      <th>Spearman_p-Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.3</td>\n",
       "      <td>total vRNA</td>\n",
       "      <td>PB2</td>\n",
       "      <td>0.591697</td>\n",
       "      <td>0.093274</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.331740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.3</td>\n",
       "      <td>total vRNA</td>\n",
       "      <td>PB1</td>\n",
       "      <td>0.794355</td>\n",
       "      <td>0.010550</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.099186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>total vRNA</td>\n",
       "      <td>PA</td>\n",
       "      <td>0.505629</td>\n",
       "      <td>0.164937</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.487922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3</td>\n",
       "      <td>total vRNA</td>\n",
       "      <td>HA</td>\n",
       "      <td>0.553960</td>\n",
       "      <td>0.121724</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.286105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3</td>\n",
       "      <td>total vRNA</td>\n",
       "      <td>NP</td>\n",
       "      <td>0.609360</td>\n",
       "      <td>0.081511</td>\n",
       "      <td>0.616667</td>\n",
       "      <td>0.076929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.3</td>\n",
       "      <td>total vRNA</td>\n",
       "      <td>NA</td>\n",
       "      <td>0.786723</td>\n",
       "      <td>0.011890</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.099186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.3</td>\n",
       "      <td>total vRNA</td>\n",
       "      <td>M</td>\n",
       "      <td>0.889488</td>\n",
       "      <td>0.001324</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.170471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.3</td>\n",
       "      <td>total vRNA</td>\n",
       "      <td>NS</td>\n",
       "      <td>0.281964</td>\n",
       "      <td>0.462295</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.932157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>total vRNA</td>\n",
       "      <td>PB2</td>\n",
       "      <td>0.266122</td>\n",
       "      <td>0.488845</td>\n",
       "      <td>0.183333</td>\n",
       "      <td>0.636820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>total vRNA</td>\n",
       "      <td>PB1</td>\n",
       "      <td>0.399863</td>\n",
       "      <td>0.286286</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.380713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>total vRNA</td>\n",
       "      <td>PA</td>\n",
       "      <td>0.463561</td>\n",
       "      <td>0.208826</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>0.111633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>total vRNA</td>\n",
       "      <td>HA</td>\n",
       "      <td>0.430557</td>\n",
       "      <td>0.247328</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.139227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>total vRNA</td>\n",
       "      <td>NP</td>\n",
       "      <td>0.365350</td>\n",
       "      <td>0.333611</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.668231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>total vRNA</td>\n",
       "      <td>NA</td>\n",
       "      <td>0.621086</td>\n",
       "      <td>0.074237</td>\n",
       "      <td>0.516667</td>\n",
       "      <td>0.154390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>total vRNA</td>\n",
       "      <td>M</td>\n",
       "      <td>0.954687</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.000507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>total vRNA</td>\n",
       "      <td>NS</td>\n",
       "      <td>0.153889</td>\n",
       "      <td>0.692622</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.432845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    moi    molecule segment  PearsonCorrelation  Pearson_p-Value  \\\n",
       "0   0.3  total vRNA     PB2            0.591697         0.093274   \n",
       "1   0.3  total vRNA     PB1            0.794355         0.010550   \n",
       "2   0.3  total vRNA      PA            0.505629         0.164937   \n",
       "3   0.3  total vRNA      HA            0.553960         0.121724   \n",
       "4   0.3  total vRNA      NP            0.609360         0.081511   \n",
       "5   0.3  total vRNA      NA            0.786723         0.011890   \n",
       "6   0.3  total vRNA       M            0.889488         0.001324   \n",
       "7   0.3  total vRNA      NS            0.281964         0.462295   \n",
       "8   1.0  total vRNA     PB2            0.266122         0.488845   \n",
       "9   1.0  total vRNA     PB1            0.399863         0.286286   \n",
       "10  1.0  total vRNA      PA            0.463561         0.208826   \n",
       "11  1.0  total vRNA      HA            0.430557         0.247328   \n",
       "12  1.0  total vRNA      NP            0.365350         0.333611   \n",
       "13  1.0  total vRNA      NA            0.621086         0.074237   \n",
       "14  1.0  total vRNA       M            0.954687         0.000062   \n",
       "15  1.0  total vRNA      NS            0.153889         0.692622   \n",
       "\n",
       "    SpearmanCorrelation  Spearman_p-Value  \n",
       "0              0.366667          0.331740  \n",
       "1              0.583333          0.099186  \n",
       "2              0.266667          0.487922  \n",
       "3              0.400000          0.286105  \n",
       "4              0.616667          0.076929  \n",
       "5              0.583333          0.099186  \n",
       "6              0.500000          0.170471  \n",
       "7              0.033333          0.932157  \n",
       "8              0.183333          0.636820  \n",
       "9              0.333333          0.380713  \n",
       "10             0.566667          0.111633  \n",
       "11             0.533333          0.139227  \n",
       "12             0.166667          0.668231  \n",
       "13             0.516667          0.154390  \n",
       "14             0.916667          0.000507  \n",
       "15             0.300000          0.432845  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4468d1cd-bec5-4c95-87ac-ea1f5a24404e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.arange(8)\n",
    "plt.bar(x,df_res[df_res['moi']==0.3].PearsonCorrelation, label='0.3 MOI',color='rosybrown')\n",
    "plt.bar(x+9,df_res[df_res['moi']==1].PearsonCorrelation,label='1.0 MOI',color='cadetblue')\n",
    "plt.xticks(list(x)+list(x+9), df_res.segment)\n",
    "plt.ylabel('Pearson\\'s correlation coefficient\\nbetween mRNA segment and total vRNA')\n",
    "plt.legend()\n",
    "plt.savefig('figures/supp-fig8-correlation_total_vRNA.svg')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}