impc-phenotype-hits-vs-tested-genes.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with IMPC Statistical Results Data\n",
    "\n",
    "Statistical results are the main output of the IMPC Data Analysis Pipeline, we use statistical results to generate gene-phenotype associations or hits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib.request\n",
    "import gzip\n",
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Download the Statistical Results report from the [latest Data Release](https://ftp.ebi.ac.uk/pub/databases/impc/all-data-releases/latest/results/statistical-results-ALL.csv.gz) (may take a while is aroung 2.7GB when uncompressed)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats_results_url = \"https://ftp.ebi.ac.uk/pub/databases/impc/other/statistical-results-ALL.csv.gz\"\n",
    "# This URL is temporary plesae use the one below after the IMPC has fixed the issue with the stats results file\n",
    "# stats_results_url = \"https://ftp.ebi.ac.uk/pub/databases/impc/all-data-releases/latest/results/statistical-results-ALL.csv.gz\"\n",
    "stats_results_file_name = \"data/statistical-results-ALL.csv.gz\"\n",
    "urllib.request.urlretrieve(stats_results_url, stats_results_file_name)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Open the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/5lgvty8d0js7s4d5qw017r340000gp/T/ipykernel_46165/2223263253.py:2: DtypeWarning: Columns (25,60) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  stats_results = pd.read_csv(stats_results_file)\n"
     ]
    }
   ],
   "source": [
    "with gzip.open(stats_results_file_name) as stats_results_file:\n",
    "    stats_results = pd.read_csv(stats_results_file)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Check the file structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>phenotyping_center</th>\n",
       "      <th>intercept_estimate</th>\n",
       "      <th>procedure_id</th>\n",
       "      <th>mutant_biological_model_id</th>\n",
       "      <th>rotated_residuals_test</th>\n",
       "      <th>weight_effect_p_value</th>\n",
       "      <th>male_mutant_count</th>\n",
       "      <th>pipeline_stable_key</th>\n",
       "      <th>female_ko_effect_p_value</th>\n",
       "      <th>pipeline_stable_id</th>\n",
       "      <th>...</th>\n",
       "      <th>pipeline_id</th>\n",
       "      <th>group_1_genotype</th>\n",
       "      <th>male_ko_parameter_estimate</th>\n",
       "      <th>genotype_effect_parameter_estimate</th>\n",
       "      <th>categories</th>\n",
       "      <th>parameter_id</th>\n",
       "      <th>batch_significant</th>\n",
       "      <th>genotype_effect_stderr_estimate</th>\n",
       "      <th>resource_fullname</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HMGU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HMGU_001</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IMPC</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICS</td>\n",
       "      <td>420.169756</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.667601</td>\n",
       "      <td>ICS_001</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>control</td>\n",
       "      <td>5.753204</td>\n",
       "      <td>-0.106308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>9.789624</td>\n",
       "      <td>IMPC</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BCM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BCM_001</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IMPC</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ICS_001</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IMPC</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UC Davis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UCD_001</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IMPC</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  phenotyping_center  intercept_estimate  procedure_id   \n",
       "0               HMGU                 NaN           NaN  \\\n",
       "1                ICS          420.169756           NaN   \n",
       "2                BCM                 NaN           NaN   \n",
       "3                ICS                 NaN           NaN   \n",
       "4           UC Davis                 NaN           NaN   \n",
       "\n",
       "   mutant_biological_model_id  rotated_residuals_test  weight_effect_p_value   \n",
       "0                         NaN                     NaN                    NaN  \\\n",
       "1                         NaN                     NaN                    NaN   \n",
       "2                         NaN                     NaN                    NaN   \n",
       "3                         NaN                     NaN                    NaN   \n",
       "4                         NaN                     NaN                    NaN   \n",
       "\n",
       "   male_mutant_count  pipeline_stable_key  female_ko_effect_p_value   \n",
       "0                NaN                   14                       NaN  \\\n",
       "1                8.0                   10                  0.667601   \n",
       "2                NaN                   16                       NaN   \n",
       "3                NaN                   10                       NaN   \n",
       "4                NaN                   13                       NaN   \n",
       "\n",
       "  pipeline_stable_id  ...  pipeline_id group_1_genotype   \n",
       "0           HMGU_001  ...          NaN              NaN  \\\n",
       "1            ICS_001  ...          NaN          control   \n",
       "2            BCM_001  ...          NaN              NaN   \n",
       "3            ICS_001  ...          NaN              NaN   \n",
       "4            UCD_001  ...          NaN              NaN   \n",
       "\n",
       "  male_ko_parameter_estimate genotype_effect_parameter_estimate categories   \n",
       "0                        NaN                                NaN        NaN  \\\n",
       "1                   5.753204                          -0.106308        NaN   \n",
       "2                        NaN                                NaN        NaN   \n",
       "3                        NaN                                NaN        NaN   \n",
       "4                        NaN                                NaN        NaN   \n",
       "\n",
       "   parameter_id batch_significant genotype_effect_stderr_estimate   \n",
       "0           NaN               NaN                             NaN  \\\n",
       "1           NaN             False                        9.789624   \n",
       "2           NaN               NaN                             NaN   \n",
       "3           NaN               NaN                             NaN   \n",
       "4           NaN               NaN                             NaN   \n",
       "\n",
       "   resource_fullname  significant  \n",
       "0               IMPC        False  \n",
       "1               IMPC        False  \n",
       "2               IMPC        False  \n",
       "3               IMPC        False  \n",
       "4               IMPC        False  \n",
       "\n",
       "[5 rows x 88 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_results.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Filter out legacy / external projects data (optional). The [mousephenotype.org](mousephenotype.org) website has data for some legacy phenotyping projects (like MGP and EuroPhenome) and some especial collaboration projects (like the 3i Comsortium). Also filter out statistical results without top_level_mp_term, so statistical results are annotated with a different ontology, for some really specific procedures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "impc_stats_results = stats_results[(stats_results.resource_fullname == \"IMPC\") & (~stats_results.top_level_mp_term_name.isna())]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Split the nested list of top_level_mp_term_names and explode the dataframe, so we have one value for top_level_mp_term_name in every row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/5lgvty8d0js7s4d5qw017r340000gp/T/ipykernel_46165/3174311107.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  impc_stats_results.top_level_mp_term_name = impc_stats_results.top_level_mp_term_name.str.split(\",\")\n"
     ]
    }
   ],
   "source": [
    "impc_stats_results.top_level_mp_term_name = impc_stats_results.top_level_mp_term_name.str.split(\",\")\n",
    "impc_stats_results_top_level_exploded = impc_stats_results.explode(\"top_level_mp_term_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "impc_gene_phenotype_assoc = impc_stats_results_top_level_exploded[[\"marker_symbol\", \"top_level_mp_term_name\", \"significant\"]].drop_duplicates()\n",
    "impc_gene_phenotype_top_level_tested_vs_hits = impc_gene_phenotype_assoc.groupby([\"marker_symbol\", \"top_level_mp_term_name\"], as_index=False).any(\"significant\").groupby([\"top_level_mp_term_name\", \"significant\"], as_index=False).nunique(\"marker_symbol\").sort_values(\"marker_#symbol\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'IMPC gene-phenotype associations tested vs hits'}, xlabel='top_level_mp_term_name'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "impc_gene_phenotype_top_level_tested_vs_hits.pivot_table(index=\"top_level_mp_term_name\", columns=\"significant\", values=\"marker_symbol\", aggfunc=\"sum\").plot.bar(title=\"IMPC gene-phenotype associations tested vs hits\", figsize=(10, 10), stacked=True)\n",
    "#impc_gene_phenotype_top_level_tested_vs_hits.plot.barh(x=\"top_level_mp_term_name\", y=\"marker_symbol\", color=\"significant\", title=\"IMPC gene-phenotype associations tested vs hits\", figsize=(10, 10))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.10.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}