step6_issue_specificity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "40084902-323d-4d76-aa10-96c3e909d62c",
   "metadata": {},
   "source": [
    "# Step 5 - Tissue Specificity\n",
    "-- Alex Warwick Vesztrocy, March 2024"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13eb117d-7642-4084-b67b-3044e2828112",
   "metadata": {},
   "source": [
    "This notebook contains the code to compute tissue specicity scores ($\\tau$). To do this the TPM data is first transformed using the `arcsinh` function -- $\\textrm{arcsinh}(x) := \\ln (x + \\sqrt{x^2 + 1})$ -- before taking the mean value of any replicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e29af330-0cc9-41f7-b75b-c7e7c39f0293",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "from scipy.stats import ks_2samp\n",
    "from tqdm.auto import tqdm\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "EXPR = './results/expr'\n",
    "\n",
    "TAU_RESULTS = './results/tau'\n",
    "!mkdir -p {TAU_RESULTS}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d93538a7-8235-40f8-82da-a365d18a2ea3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_tau(expr_df):\n",
    "    def tau_(x):\n",
    "        max_x = x.max()\n",
    "        if max_x > 0 and len(x) > 1:\n",
    "            # if gene is not expressed anywhere, we don't have enough data to include\n",
    "            # if only one tissue, do not include and set to -1 as a flag.\n",
    "            return np.sum(1 - (x/max_x))/(len(x)-1)\n",
    "        else:\n",
    "            return -1\n",
    "    \n",
    "    def transform_and_group(df):\n",
    "        def gen():\n",
    "            # group on the first aspect of multi-index for columns\n",
    "            for k in set(map(lambda x: x[0], df.columns)):\n",
    "                # transform and average\n",
    "                yield pd.DataFrame({k: df.loc[:,k].apply(np.arcsinh).mean(axis=1)})\n",
    "    \n",
    "        return pd.concat(gen(), axis=1)\n",
    "\n",
    "    tau_df = transform_and_group(expr_df).apply(tau_, axis=1)\n",
    "\n",
    "    # remove genes where tau=-1. these genes must have >2.0 for another tissue.\n",
    "    return tau_df[tau_df > -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "965f7daa-e312-4b55-bc9d-75a6b2628708",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_for_db(db_fn, out_fn):\n",
    "    in_db = pd.HDFStore(db_fn, mode='r')\n",
    "    if not os.path.isfile(out_fn):\n",
    "        for n in tqdm(in_db.root):\n",
    "            sp = n._v_name\n",
    "            print(sp)\n",
    "            expr_df = pd.read_hdf(in_db, sp)\n",
    "            compute_tau(expr_df).to_hdf(out_fn, sp, complevel=9, complib='zlib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "212da550-7d11-41c2-b751-946bbb743d86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Main categories only\n",
    "compute_for_db(db_fn=f'{EXPR}/animal_maincat_tpm_expr.h5',\n",
    "               out_fn=f'{TAU_RESULTS}/animal_maincat_tau.h5')\n",
    "# Categorised data\n",
    "compute_for_db(db_fn=f'{EXPR}/animal_categorised_tpm_expr.h5',\n",
    "               out_fn=f'{TAU_RESULTS}/animal_categorised_tau.h5')\n",
    "# All data\n",
    "compute_for_db(db_fn=f'{EXPR}/animal_tpm_expr.h5',\n",
    "               out_fn=f'{TAU_RESULTS}/animal_alldata_tau.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "05713233-d97f-45b3-914e-ec974bfad4f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AMBTC\n",
      "ARATH\n",
      "BRANA\n",
      "CAPAN\n",
      "CUCSA\n",
      "HELAN\n",
      "MAIZE\n",
      "MANES\n",
      "MARPO\n",
      "MEDTR\n",
      "ORYSJ\n",
      "PHYPA\n",
      "POPTR\n",
      "SELML\n",
      "SETIT\n",
      "SOLLC\n",
      "SOLTU\n",
      "SOYBN\n",
      "VITVI\n",
      "WHEAT\n"
     ]
    },
    {
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       "model_id": "2ec0b2d2f0974b4e85c5e0088ce29e86",
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       "version_minor": 0
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       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AMBTC\n",
      "ARATH\n",
      "BRANA\n",
      "CAPAN\n",
      "CUCSA\n",
      "HELAN\n",
      "MAIZE\n",
      "MANES\n",
      "MARPO\n",
      "MEDTR\n",
      "ORYSJ\n",
      "PHYPA\n",
      "POPTR\n",
      "SELML\n",
      "SETIT\n",
      "SOLLC\n",
      "SOLTU\n",
      "SOYBN\n",
      "VITVI\n",
      "WHEAT\n"
     ]
    }
   ],
   "source": [
    "# General tissue categorisation\n",
    "compute_for_db(db_fn=f'{EXPR}/plant_general_tpm_expr.h5',\n",
    "               out_fn=f'{TAU_RESULTS}/plant_general_tau.h5')\n",
    "# Specific tissue categorisation\n",
    "compute_for_db(db_fn=f'{EXPR}/plant_specific_tpm_expr.h5',\n",
    "               out_fn=f'{TAU_RESULTS}/plant_specific_tau.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a571016d-d9be-403d-84e2-da9eff44f250",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a398772-871d-4a5f-8ac8-85e946d3c6ff",
   "metadata": {},
   "source": [
    "## Plot distn per-species\n",
    "\n",
    "Want to have a look and see whether the distributions are similar per-species. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d192e741-0566-468d-b050-5d3df77498c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# species that have enough data and are in PANTHER\n",
    "dataset = {'animals': {'BOVIN','CHICK','DROME','FELCA','GORGO','HORSE','HUMAN','LEPOC','MACMU','MONDO','MOUSE','ORNAN','ORYLA','PANTR','PIG','RAT'},\n",
    "           'plants': {'AMBTC','ARATH','BRANA','CAPAN','CUCSA','HELAN','MAIZE','MANES','MARPO','MEDTR','ORYSJ','PHYPA','POPTR','SELML','SETIT','SOLLC','SOLTU','SOYBN','VITVI','WHEAT'}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e8b7e417-2e9a-4a91-8974-0cdffbf0180f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_distns(species, dbs):\n",
    "    def gen():\n",
    "        for sp in species:\n",
    "            z = []\n",
    "            for (db_key, db) in dbs.items():\n",
    "                z.append(pd.DataFrame({'tau': pd.read_hdf(db, sp)}))\n",
    "                z[-1]['type'] = db_key\n",
    "            df = pd.concat(z)\n",
    "            df['species'] = sp\n",
    "            yield df\n",
    "            \n",
    "    return pd.concat(gen())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "269374bd-bb5d-468a-929e-aef576a17445",
   "metadata": {},
   "outputs": [],
   "source": [
    "animal_df = read_distns(dataset['animals'],\n",
    "                        {'main category only': pd.HDFStore(f'{TAU_RESULTS}/animal_maincat_tau.h5', mode='r'),\n",
    "                         'categorised': pd.HDFStore(f'{TAU_RESULTS}/animal_categorised_tau.h5', mode='r')})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8b65ecbf-650b-4d10-868c-e257b1735399",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\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>tau</th>\n",
       "      <th>type</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSSSCG00000000002</th>\n",
       "      <td>0.733438</td>\n",
       "      <td>main category only</td>\n",
       "      <td>PIG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSSSCG00000000003</th>\n",
       "      <td>0.399908</td>\n",
       "      <td>main category only</td>\n",
       "      <td>PIG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSSSCG00000000005</th>\n",
       "      <td>0.527448</td>\n",
       "      <td>main category only</td>\n",
       "      <td>PIG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSSSCG00000000006</th>\n",
       "      <td>0.625656</td>\n",
       "      <td>main category only</td>\n",
       "      <td>PIG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSSSCG00000000007</th>\n",
       "      <td>0.309803</td>\n",
       "      <td>main category only</td>\n",
       "      <td>PIG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSFCAG00000053855</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>categorised</td>\n",
       "      <td>FELCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSFCAG00000053857</th>\n",
       "      <td>0.951151</td>\n",
       "      <td>categorised</td>\n",
       "      <td>FELCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSFCAG00000053858</th>\n",
       "      <td>0.887292</td>\n",
       "      <td>categorised</td>\n",
       "      <td>FELCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSFCAG00000053860</th>\n",
       "      <td>0.462693</td>\n",
       "      <td>categorised</td>\n",
       "      <td>FELCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSFCAG00000053862</th>\n",
       "      <td>0.977063</td>\n",
       "      <td>categorised</td>\n",
       "      <td>FELCA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>860070 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         tau                type species\n",
       "gene_id                                                 \n",
       "ENSSSCG00000000002  0.733438  main category only     PIG\n",
       "ENSSSCG00000000003  0.399908  main category only     PIG\n",
       "ENSSSCG00000000005  0.527448  main category only     PIG\n",
       "ENSSSCG00000000006  0.625656  main category only     PIG\n",
       "ENSSSCG00000000007  0.309803  main category only     PIG\n",
       "...                      ...                 ...     ...\n",
       "ENSFCAG00000053855  1.000000         categorised   FELCA\n",
       "ENSFCAG00000053857  0.951151         categorised   FELCA\n",
       "ENSFCAG00000053858  0.887292         categorised   FELCA\n",
       "ENSFCAG00000053860  0.462693         categorised   FELCA\n",
       "ENSFCAG00000053862  0.977063         categorised   FELCA\n",
       "\n",
       "[860070 rows x 3 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plant_df = read_distns(dataset['plants'],\n",
    "                       {'general': pd.HDFStore(f'{TAU_RESULTS}/plant_general_tau.h5', mode='r'),\n",
    "                        'specific': pd.HDFStore(f'{TAU_RESULTS}/plant_specific_tau.h5', mode='r')})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "958c7efa-81ec-44c2-a333-7f1b96c6d844",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Tissue Specificity ($\\\\tau$)')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(data=animal_df.sort_values('species'), x='species', y='tau', hue='type', showfliers=False)\n",
    "ax.tick_params(axis='x', labelrotation=45)\n",
    "ax.set_title('Animal Tissue Specificity Distribution')\n",
    "ax.set_xlabel('Animal Species')\n",
    "ax.set_ylabel('Tissue Specificity ($\\\\tau$)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "fea67347-35ee-4d53-93dd-5847bce8d7d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(data=plant_df.sort_values('species'), x='species', y='tau', hue='type', showfliers=False)\n",
    "ax.tick_params(axis='x', labelrotation=45)\n",
    "ax.set_title('Plant Tissue Specificity Distribution')\n",
    "ax.set_xlabel('Plant Species')\n",
    "ax.set_ylabel('Tissue Specificity ($\\\\tau$)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ca1383c-a893-4834-893c-e1a14a0d7372",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}