{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "tamil-advancement",
   "metadata": {},
   "source": [
    "# 1. Data preprocessing and curation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0d1d069",
   "metadata": {},
   "source": [
    "This notebook focuses on the preparation of LPS datasets, including both public and private sources, and demonstrates how to merge them for training the PerturbGen model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8b14104",
   "metadata": {},
   "source": [
    "## 1.1. Public LPS data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bba2a08",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gdown # for downloading files from Google Drive\n",
    "import requests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da1d0485",
   "metadata": {},
   "source": [
    "Downloading the public LPS dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "broke-mexico",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://ftp.ebi.ac.uk/biostudies/fire/E-MTAB-/026/E-MTAB-10026/Files/covid_portal_210320_with_raw.h5ad\n",
      "To: /home/jovyan/farm_mount/Cytomeister/Evaluation_datasets/LPS/Public_Emily/Raw_h5ad.zip\n",
      "100%|██████████| 7.19G/7.19G [01:31<00:00, 78.9MB/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'./Raw_h5ad.zip'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://ftp.ebi.ac.uk/biostudies/fire/E-MTAB-/026/E-MTAB-10026/Files/covid_portal_210320_with_raw.h5ad'\n",
    "output= './Raw_h5ad'\n",
    "gdown.download(url,output) # download the file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17127c7f",
   "metadata": {},
   "source": [
    "Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98744b7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "functional-vertex",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the downloaded h5ad file\n",
    "adata = sc.read('./Raw.h5ad')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e8a88f7",
   "metadata": {},
   "source": [
    "Here, we investigate the .h5ad file to identify LPS samples and healthy control cells in the downloaded LPS data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "understood-tutorial",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 647366 × 24929\n",
       "    obs: 'sample_id', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'full_clustering', 'initial_clustering', 'Resample', 'Collection_Day', 'Sex', 'Age_interval', 'Swab_result', 'Status', 'Smoker', 'Status_on_day_collection', 'Status_on_day_collection_summary', 'Days_from_onset', 'Site', 'time_after_LPS', 'Worst_Clinical_Status', 'Outcome', 'patient_id'\n",
       "    var: 'feature_types'\n",
       "    uns: 'hvg', 'leiden', 'neighbors', 'pca', 'umap'\n",
       "    obsm: 'X_pca', 'X_pca_harmony', 'X_umap'\n",
       "    layers: 'raw'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "objective-offset",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time_after_LPS\n",
       "nan    639482\n",
       "90m      3999\n",
       "10h      3885\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs['time_after_LPS'].value_counts() # check time points available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "verified-italian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Status\n",
       "Covid        527286\n",
       "Healthy       97039\n",
       "Non_covid     15157\n",
       "LPS            7884\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs['Status'].value_counts() # check conditions available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "generic-livestock",
   "metadata": {},
   "outputs": [],
   "source": [
    "lps_sample_ids = adata.obs.loc[adata.obs['Status'] == 'LPS', 'patient_id'].unique() # get patient ids for LPS samples\n",
    "\n",
    "healthy_sample_ids = adata.obs.loc[adata.obs['Status'] == 'Healthy', 'patient_id'].unique() # get patient ids for Healthy samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "advanced-canal",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "adata = adata[adata.obs['Status'].isin(['LPS','Healthy'])].copy() # keep only LPS and Healthy samples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e5a3cde",
   "metadata": {},
   "source": [
    "Next, we apply a set of modifications and add details to make downstream analyses more convenient e.g. converting time obs into categorical obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sensitive-northern",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs['time_after_LPS'] = adata.obs['time_after_LPS'].astype(str) # convert to string\n",
    "adata.obs['time_after_LPS'] = adata.obs['time_after_LPS'].replace('nan', 'normal') # replace nan with normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "23d4552a-7c48-4603-af08-c0ee6e05903a",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs['time_after_LPS'] = adata.obs['time_after_LPS'].apply(lambda x: x if x == 'normal' else f'{x}_LPS')\n",
    "\n",
    "adata.obs['time_after_LPS'] = pd.Categorical(\n",
    "    adata.obs['time_after_LPS'],\n",
    "    categories=['normal', '90m_LPS', '10h_LPS'],\n",
    "    ordered=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "soviet-simulation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time_after_LPS\n",
       "normal     97039\n",
       "90m_LPS     3999\n",
       "10h_LPS     3885\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs['time_after_LPS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "plain-nurse",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs['donor_id'] = adata.obs['patient_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "07dc3ac2-3623-432c-b61a-4413a0ae3638",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs['study'] = 'Public_Emily2021'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "labeled-disaster",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_interval\n",
       "(30, 39]    30538\n",
       "(50, 59]    26344\n",
       "(20, 29]    20426\n",
       "(40, 49]    15926\n",
       "(60, 69]     9970\n",
       "(70, 79]     1719\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs['Age_interval'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "simplified-sender",
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = {\n",
    "    '(20, 29]': 'Adult',      \n",
    "    '(30, 39]': 'Adult',\n",
    "    '(40, 49]': 'Adult',\n",
    "    '(50, 59]': 'Adult',\n",
    "    '(60, 69]': 'Adult',\n",
    "    '(70, 79]': 'Old'\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "earned-gallery",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs['life_stage'] = adata.obs['Age_interval'].map(mapping)\n",
    "\n",
    "\n",
    "adata.obs['life_stage'] = pd.Categorical(\n",
    "    adata.obs['life_stage'],\n",
    "    categories=['Embryo', 'Fetal', 'Childhood', 'Young Adult', 'Adult', 'Old'],\n",
    "    ordered=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "56e64b5a-88e0-450c-85c7-dd868b2ff74f",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs['development_stage'] = adata.obs['life_stage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e6d56d3d-afcb-4867-abc3-b2a417be64f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs['tissue'] = 'blood'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "67f058ae-8547-42bb-b97c-546d62b5731d",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.X = adata.layers['raw'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e31c5979-704f-49cb-bf2d-2d949986c1db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float32(275279.0)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.X.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "65175042-d236-4102-a2f6-da2015beb8bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "covid_index\n",
       "AAACCTGAGACCACGA-newcastle65    Initial\n",
       "AAACCTGAGATGTCGG-newcastle65    Initial\n",
       "AAACCTGAGGCGATAC-newcastle65    Initial\n",
       "AAACCTGAGTACACCT-newcastle65    Initial\n",
       "AAACCTGAGTGAATTG-newcastle65    Initial\n",
       "                                 ...   \n",
       "BGCV15_TTTCCTCTCTGATACG-1       Initial\n",
       "BGCV15_TTTCCTCTCTTTAGGG-1       Initial\n",
       "BGCV15_TTTGCGCTCACCGTAA-1       Initial\n",
       "BGCV15_TTTGGTTTCAAGATCC-1       Initial\n",
       "BGCV15_TTTGTCACAAGCCATT-1       Initial\n",
       "Name: Resample, Length: 104923, dtype: category\n",
       "Categories (1, object): ['Initial']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs['Resample']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "090676e0-9ddb-49e5-9fbe-09a3ffdf3d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subset to Gene Expression features\n",
    "adata = adata[:, adata.var['feature_types'] == 'Gene Expression']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e27651a7-dcac-4e2f-b048-2c82a50f662e",
   "metadata": {},
   "outputs": [],
   "source": [
    "ref = pd.read_csv(\"../../../../Ensembl_symbol_Human_(GRCh38.p14)/mart_export.txt\")  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ae9dd219-a3e6-4883-a436-7d46a6b05840",
   "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>feature_types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MIR1302-2HG</th>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL627309.1</th>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL627309.3</th>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL627309.2</th>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL669831.2</th>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC007325.2</th>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL354822.1</th>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC233755.2</th>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC233755.1</th>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC240274.1</th>\n",
       "      <td>Gene Expression</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24737 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature_types\n",
       "MIR1302-2HG  Gene Expression\n",
       "AL627309.1   Gene Expression\n",
       "AL627309.3   Gene Expression\n",
       "AL627309.2   Gene Expression\n",
       "AL669831.2   Gene Expression\n",
       "...                      ...\n",
       "AC007325.2   Gene Expression\n",
       "AL354822.1   Gene Expression\n",
       "AC233755.2   Gene Expression\n",
       "AC233755.1   Gene Expression\n",
       "AC240274.1   Gene Expression\n",
       "\n",
       "[24737 rows x 1 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "186dccde-1644-4a90-87cf-2c534fe371b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_962786/4272093940.py:16: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n",
      "  adata.var[\"gene_symbol\"] = adata.var_names.str.strip().str.upper()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adata now has 19193 genes with Ensembl IDs as var_names.\n"
     ]
    }
   ],
   "source": [
    "# 1. Convert to string and clean\n",
    "ref.index = ref.index.astype(str).str.strip().str.upper()\n",
    "ref[\"HGNC symbol\"] = ref[\"HGNC symbol\"].astype(str).str.strip().str.upper()\n",
    "\n",
    "# Drop NaNs in gene_symbol (after converting to str 'nan', you can use replace)\n",
    "ref = ref.replace({\"HGNC symbol\": {\"NAN\": None}}).dropna(subset=[\"HGNC symbol\"])\n",
    "\n",
    "# 2. Map gene_symbol -> ensembl_id by first occurrence\n",
    "ref_rev = (\n",
    "    ref.reset_index()\n",
    "       .drop_duplicates(subset=[\"HGNC symbol\"])\n",
    "       .set_index(\"HGNC symbol\")[\"Gene stable ID\"]\n",
    ")\n",
    "\n",
    "# 3. Map gene_symbol from adata.var_names to ensembl_id\n",
    "adata.var[\"gene_symbol\"] = adata.var_names.str.strip().str.upper()\n",
    "adata.var[\"ensembl_id\"] = adata.var[\"gene_symbol\"].map(ref_rev)\n",
    "\n",
    "# 4. Filter genes without mapping\n",
    "adata = adata[:, adata.var[\"ensembl_id\"].notna()].copy()\n",
    "\n",
    "# 5. Set var_names to ensembl_id\n",
    "adata.var_names = adata.var[\"ensembl_id\"]\n",
    "\n",
    "print(f\"adata now has {adata.n_vars} genes with Ensembl IDs as var_names.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3087f503-d8e8-471b-a24a-4f84d43aa36a",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.var.index.name = 'gene_id'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e253575d-dff7-4b1f-9c9e-cc6004c6eca4",
   "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>feature_types</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>ensembl_id</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>ENSG00000243485</th>\n",
       "      <td>Gene Expression</td>\n",
       "      <td>MIR1302-2HG</td>\n",
       "      <td>ENSG00000243485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000177757</th>\n",
       "      <td>Gene Expression</td>\n",
       "      <td>FAM87B</td>\n",
       "      <td>ENSG00000177757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000225880</th>\n",
       "      <td>Gene Expression</td>\n",
       "      <td>LINC00115</td>\n",
       "      <td>ENSG00000225880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000230368</th>\n",
       "      <td>Gene Expression</td>\n",
       "      <td>FAM41C</td>\n",
       "      <td>ENSG00000230368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000187634</th>\n",
       "      <td>Gene Expression</td>\n",
       "      <td>SAMD11</td>\n",
       "      <td>ENSG00000187634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198886</th>\n",
       "      <td>Gene Expression</td>\n",
       "      <td>MT-ND4</td>\n",
       "      <td>ENSG00000198886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198786</th>\n",
       "      <td>Gene Expression</td>\n",
       "      <td>MT-ND5</td>\n",
       "      <td>ENSG00000198786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198695</th>\n",
       "      <td>Gene Expression</td>\n",
       "      <td>MT-ND6</td>\n",
       "      <td>ENSG00000198695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000198727</th>\n",
       "      <td>Gene Expression</td>\n",
       "      <td>MT-CYB</td>\n",
       "      <td>ENSG00000198727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000274847</th>\n",
       "      <td>Gene Expression</td>\n",
       "      <td>MAFIP</td>\n",
       "      <td>ENSG00000274847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19193 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   feature_types  gene_symbol       ensembl_id\n",
       "gene_id                                                       \n",
       "ENSG00000243485  Gene Expression  MIR1302-2HG  ENSG00000243485\n",
       "ENSG00000177757  Gene Expression       FAM87B  ENSG00000177757\n",
       "ENSG00000225880  Gene Expression    LINC00115  ENSG00000225880\n",
       "ENSG00000230368  Gene Expression       FAM41C  ENSG00000230368\n",
       "ENSG00000187634  Gene Expression       SAMD11  ENSG00000187634\n",
       "...                          ...          ...              ...\n",
       "ENSG00000198886  Gene Expression       MT-ND4  ENSG00000198886\n",
       "ENSG00000198786  Gene Expression       MT-ND5  ENSG00000198786\n",
       "ENSG00000198695  Gene Expression       MT-ND6  ENSG00000198695\n",
       "ENSG00000198727  Gene Expression       MT-CYB  ENSG00000198727\n",
       "ENSG00000274847  Gene Expression        MAFIP  ENSG00000274847\n",
       "\n",
       "[19193 rows x 3 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bb10eba-10e2-4a6d-bfe1-9b3191b6fcf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "covid_index\n",
       "AAACCTGAGACCACGA-newcastle65    Adult\n",
       "AAACCTGAGATGTCGG-newcastle65    Adult\n",
       "AAACCTGAGGCGATAC-newcastle65    Adult\n",
       "AAACCTGAGTACACCT-newcastle65    Adult\n",
       "AAACCTGAGTGAATTG-newcastle65    Adult\n",
       "                                ...  \n",
       "BGCV15_TTTCCTCTCTGATACG-1       Adult\n",
       "BGCV15_TTTCCTCTCTTTAGGG-1       Adult\n",
       "BGCV15_TTTGCGCTCACCGTAA-1       Adult\n",
       "BGCV15_TTTGGTTTCAAGATCC-1       Adult\n",
       "BGCV15_TTTGTCACAAGCCATT-1       Adult\n",
       "Name: development_stage, Length: 104923, dtype: category\n",
       "Categories (2, object): ['Adult' < 'Old']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs['development_stage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "dbce8e7f-d9f9-48de-99bb-15752db19962",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "full_clustering\n",
       "CD4.Naive                13829\n",
       "NK_16hi                  13473\n",
       "CD8.Naive                 8105\n",
       "CD4.CM                    7505\n",
       "B_naive                   6908\n",
       "CD83_CD14_mono            6772\n",
       "CD8.TE                    6501\n",
       "CD4.IL22                  6379\n",
       "CD8.EM                    5734\n",
       "gdT                       4948\n",
       "CD14_mono                 3774\n",
       "MAIT                      3465\n",
       "CD16_mono                 3007\n",
       "Platelets                 2363\n",
       "NK_56hi                   2190\n",
       "B_switched_memory         1395\n",
       "DC2                       1052\n",
       "DC3                        975\n",
       "B_immature                 966\n",
       "NKT                        864\n",
       "CD4.Tfh                    800\n",
       "pDC                        678\n",
       "B_non-switched_memory      647\n",
       "B_exhausted                334\n",
       "RBC                        301\n",
       "NK_prolif                  221\n",
       "ILC1_3                     213\n",
       "C1_CD16_mono               198\n",
       "CD4.EM                     188\n",
       "Plasma_cell_IgA            173\n",
       "Plasma_cell_IgG            168\n",
       "DC1                        104\n",
       "CD4.Th1                    101\n",
       "Plasmablast                 95\n",
       "Plasma_cell_IgM             84\n",
       "Treg                        75\n",
       "HSC_erythroid               74\n",
       "CD8.Prolif                  70\n",
       "HSC_CD38pos                 61\n",
       "ILC2                        36\n",
       "CD4.Prolif                  32\n",
       "CD4.Th2                     16\n",
       "ASDC                        16\n",
       "HSC_CD38neg                 13\n",
       "DC_prolif                    6\n",
       "HSC_prolif                   6\n",
       "Mono_prolif                  4\n",
       "HSC_myeloid                  3\n",
       "CD4.Th17                     1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs['full_clustering'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "adffa3ed-21d9-47db-be48-6306ae5de899",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs['cell_type'] = adata.obs['full_clustering'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ebc4ac68-0f87-4ce8-9d8b-5ab0de2673f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_type_counts = adata.obs['cell_type'].value_counts()\n",
    "\n",
    "valid_cell_types = cell_type_counts[cell_type_counts >= 50].index\n",
    "\n",
    "adata = adata[adata.obs['cell_type'].isin(valid_cell_types)].copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "eedb48d6-f686-41b9-85d3-c5e706824499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "covid_index\n",
       "AAACCTGAGACCACGA-newcastle65    normal\n",
       "AAACCTGAGATGTCGG-newcastle65    normal\n",
       "AAACCTGAGGCGATAC-newcastle65    normal\n",
       "AAACCTGAGTACACCT-newcastle65    normal\n",
       "AAACCTGAGTGAATTG-newcastle65    normal\n",
       "                                 ...  \n",
       "BGCV15_TTTCCTCTCTGATACG-1       normal\n",
       "BGCV15_TTTCCTCTCTTTAGGG-1       normal\n",
       "BGCV15_TTTGCGCTCACCGTAA-1       normal\n",
       "BGCV15_TTTGGTTTCAAGATCC-1       normal\n",
       "BGCV15_TTTGTCACAAGCCATT-1       normal\n",
       "Name: time_after_LPS, Length: 104790, dtype: category\n",
       "Categories (3, object): ['normal' < '90m_LPS' < '10h_LPS']"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs['time_after_LPS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f72e7b0e-5261-40ee-afe8-7f1029f00433",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If data is sparse, it's best to use .X.sum(axis=1).A1\n",
    "if hasattr(adata.X, 'sum'):\n",
    "    adata.obs['n_counts'] = np.ravel(adata.X.sum(axis=1))\n",
    "else:\n",
    "    adata.obs['n_counts'] = adata.X.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b5cee31f-2162-42b9-b588-7f76fb614705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "covid_index\n",
       "AAACCTGAGACCACGA-newcastle65    4242.0\n",
       "AAACCTGAGATGTCGG-newcastle65    4708.0\n",
       "AAACCTGAGGCGATAC-newcastle65    2858.0\n",
       "AAACCTGAGTACACCT-newcastle65    5227.0\n",
       "AAACCTGAGTGAATTG-newcastle65    4012.0\n",
       "                                 ...  \n",
       "BGCV15_TTTCCTCTCTGATACG-1       5766.0\n",
       "BGCV15_TTTCCTCTCTTTAGGG-1       7415.0\n",
       "BGCV15_TTTGCGCTCACCGTAA-1       5168.0\n",
       "BGCV15_TTTGGTTTCAAGATCC-1       6504.0\n",
       "BGCV15_TTTGTCACAAGCCATT-1       5246.0\n",
       "Name: n_counts, Length: 104790, dtype: float32"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs['n_counts']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4513d819",
   "metadata": {},
   "source": [
    "Eventually, we save the public data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ba449fff-c958-48b7-8c20-865ab8dc1da2",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.write_h5ad('./Public_LPS_PP.h5ad')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10197fab",
   "metadata": {},
   "source": [
    "## 1.2. Private LPS data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dd8b670",
   "metadata": {},
   "source": [
    "This data will be published after the paper publication."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1cb765b",
   "metadata": {},
   "outputs": [],
   "source": [
    "tcell=sc.read_h5ad('/path/to/data1/tcell_raw_matrix_annotated.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bc9ee09f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 69370 × 35976\n",
       "    obs: 'patient_id', 'time_after_LPS', 'Sex', 'age', 'batch', 'IR_VJ_1_locus_tcr', 'IR_VJ_2_locus_tcr', 'IR_VDJ_1_locus_tcr', 'IR_VDJ_2_locus_tcr', 'IR_VJ_1_cdr3_tcr', 'IR_VJ_2_cdr3_tcr', 'IR_VDJ_1_cdr3_tcr', 'IR_VDJ_2_cdr3_tcr', 'IR_VJ_1_cdr3_nt_tcr', 'IR_VJ_2_cdr3_nt_tcr', 'IR_VDJ_1_cdr3_nt_tcr', 'IR_VDJ_2_cdr3_nt_tcr', 'IR_VJ_1_expr_tcr', 'IR_VJ_2_expr_tcr', 'IR_VDJ_1_expr_tcr', 'IR_VDJ_2_expr_tcr', 'IR_VJ_1_expr_raw_tcr', 'IR_VJ_2_expr_raw_tcr', 'IR_VDJ_1_expr_raw_tcr', 'IR_VDJ_2_expr_raw_tcr', 'IR_VJ_1_v_gene_tcr', 'IR_VJ_2_v_gene_tcr', 'IR_VDJ_1_v_gene_tcr', 'IR_VDJ_2_v_gene_tcr', 'IR_VJ_1_d_gene_tcr', 'IR_VJ_2_d_gene_tcr', 'IR_VDJ_1_d_gene_tcr', 'IR_VDJ_2_d_gene_tcr', 'IR_VJ_1_j_gene_tcr', 'IR_VJ_2_j_gene_tcr', 'IR_VDJ_1_j_gene_tcr', 'IR_VDJ_2_j_gene_tcr', 'IR_VJ_1_c_gene_tcr', 'IR_VJ_2_c_gene_tcr', 'IR_VDJ_1_c_gene_tcr', 'IR_VDJ_2_c_gene_tcr', 'IR_VJ_1_junction_ins_tcr', 'IR_VJ_2_junction_ins_tcr', 'IR_VDJ_1_junction_ins_tcr', 'IR_VDJ_2_junction_ins_tcr', 'has_ir_tcr', 'multi_chain_tcr', 'IR_VJ_1_locus_bcr', 'IR_VJ_2_locus_bcr', 'IR_VDJ_1_locus_bcr', 'IR_VDJ_2_locus_bcr', 'IR_VJ_1_cdr3_bcr', 'IR_VJ_2_cdr3_bcr', 'IR_VDJ_1_cdr3_bcr', 'IR_VDJ_2_cdr3_bcr', 'IR_VJ_1_cdr3_nt_bcr', 'IR_VJ_2_cdr3_nt_bcr', 'IR_VDJ_1_cdr3_nt_bcr', 'IR_VDJ_2_cdr3_nt_bcr', 'IR_VJ_1_expr_bcr', 'IR_VJ_2_expr_bcr', 'IR_VDJ_1_expr_bcr', 'IR_VDJ_2_expr_bcr', 'IR_VJ_1_expr_raw_bcr', 'IR_VJ_2_expr_raw_bcr', 'IR_VDJ_1_expr_raw_bcr', 'IR_VDJ_2_expr_raw_bcr', 'IR_VJ_1_v_gene_bcr', 'IR_VJ_2_v_gene_bcr', 'IR_VDJ_1_v_gene_bcr', 'IR_VDJ_2_v_gene_bcr', 'IR_VJ_1_d_gene_bcr', 'IR_VJ_2_d_gene_bcr', 'IR_VDJ_1_d_gene_bcr', 'IR_VDJ_2_d_gene_bcr', 'IR_VJ_1_j_gene_bcr', 'IR_VJ_2_j_gene_bcr', 'IR_VDJ_1_j_gene_bcr', 'IR_VDJ_2_j_gene_bcr', 'IR_VJ_1_c_gene_bcr', 'IR_VJ_2_c_gene_bcr', 'IR_VDJ_1_c_gene_bcr', 'IR_VDJ_2_c_gene_bcr', 'IR_VJ_1_junction_ins_bcr', 'IR_VJ_2_junction_ins_bcr', 'IR_VDJ_1_junction_ins_bcr', 'IR_VDJ_2_junction_ins_bcr', 'has_ir_bcr', 'multi_chain_bcr', 'scr_th', 'doublet_scores', 'cal_th', 'scr_predicted_doublets', 'cal_dobulets', 'data_batch', 'individual_id', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'cell_typist_full_predicted_labels', 'cell_typist_full_majority_voting', 'cell_typist_initial_predicted_labels', 'cell_typist_initial_majority_voting', 'final_anno', 'timeline', 'sample_id', 'all_T'\n",
       "    var: 'gene_ids', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'GEX'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tcell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5900ff77",
   "metadata": {},
   "outputs": [],
   "source": [
    "bcell=sc.read_h5ad('/path/to/data2/bcell_raw_matrix_annotated_allanno.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "10535ad2-6cf3-4293-911e-521d7cb11c99",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 11964 × 36601\n",
       "    obs: 'patient_id', 'time_after_LPS', 'Sex', 'age', 'batch', 'IR_VJ_1_locus_tcr', 'IR_VJ_2_locus_tcr', 'IR_VDJ_1_locus_tcr', 'IR_VDJ_2_locus_tcr', 'IR_VJ_1_cdr3_tcr', 'IR_VJ_2_cdr3_tcr', 'IR_VDJ_1_cdr3_tcr', 'IR_VDJ_2_cdr3_tcr', 'IR_VJ_1_cdr3_nt_tcr', 'IR_VJ_2_cdr3_nt_tcr', 'IR_VDJ_1_cdr3_nt_tcr', 'IR_VDJ_2_cdr3_nt_tcr', 'IR_VJ_1_expr_tcr', 'IR_VJ_2_expr_tcr', 'IR_VDJ_1_expr_tcr', 'IR_VDJ_2_expr_tcr', 'IR_VJ_1_expr_raw_tcr', 'IR_VJ_2_expr_raw_tcr', 'IR_VDJ_1_expr_raw_tcr', 'IR_VDJ_2_expr_raw_tcr', 'IR_VJ_1_v_gene_tcr', 'IR_VJ_2_v_gene_tcr', 'IR_VDJ_1_v_gene_tcr', 'IR_VDJ_2_v_gene_tcr', 'IR_VJ_1_d_gene_tcr', 'IR_VJ_2_d_gene_tcr', 'IR_VDJ_1_d_gene_tcr', 'IR_VDJ_2_d_gene_tcr', 'IR_VJ_1_j_gene_tcr', 'IR_VJ_2_j_gene_tcr', 'IR_VDJ_1_j_gene_tcr', 'IR_VDJ_2_j_gene_tcr', 'IR_VJ_1_c_gene_tcr', 'IR_VJ_2_c_gene_tcr', 'IR_VDJ_1_c_gene_tcr', 'IR_VDJ_2_c_gene_tcr', 'IR_VJ_1_junction_ins_tcr', 'IR_VJ_2_junction_ins_tcr', 'IR_VDJ_1_junction_ins_tcr', 'IR_VDJ_2_junction_ins_tcr', 'has_ir_tcr', 'multi_chain_tcr', 'IR_VJ_1_locus_bcr', 'IR_VJ_2_locus_bcr', 'IR_VDJ_1_locus_bcr', 'IR_VDJ_2_locus_bcr', 'IR_VJ_1_cdr3_bcr', 'IR_VJ_2_cdr3_bcr', 'IR_VDJ_1_cdr3_bcr', 'IR_VDJ_2_cdr3_bcr', 'IR_VJ_1_cdr3_nt_bcr', 'IR_VJ_2_cdr3_nt_bcr', 'IR_VDJ_1_cdr3_nt_bcr', 'IR_VDJ_2_cdr3_nt_bcr', 'IR_VJ_1_expr_bcr', 'IR_VJ_2_expr_bcr', 'IR_VDJ_1_expr_bcr', 'IR_VDJ_2_expr_bcr', 'IR_VJ_1_expr_raw_bcr', 'IR_VJ_2_expr_raw_bcr', 'IR_VDJ_1_expr_raw_bcr', 'IR_VDJ_2_expr_raw_bcr', 'IR_VJ_1_v_gene_bcr', 'IR_VJ_2_v_gene_bcr', 'IR_VDJ_1_v_gene_bcr', 'IR_VDJ_2_v_gene_bcr', 'IR_VJ_1_d_gene_bcr', 'IR_VJ_2_d_gene_bcr', 'IR_VDJ_1_d_gene_bcr', 'IR_VDJ_2_d_gene_bcr', 'IR_VJ_1_j_gene_bcr', 'IR_VJ_2_j_gene_bcr', 'IR_VDJ_1_j_gene_bcr', 'IR_VDJ_2_j_gene_bcr', 'IR_VJ_1_c_gene_bcr', 'IR_VJ_2_c_gene_bcr', 'IR_VDJ_1_c_gene_bcr', 'IR_VDJ_2_c_gene_bcr', 'IR_VJ_1_junction_ins_bcr', 'IR_VJ_2_junction_ins_bcr', 'IR_VDJ_1_junction_ins_bcr', 'IR_VDJ_2_junction_ins_bcr', 'has_ir_bcr', 'multi_chain_bcr', 'scr_th', 'doublet_scores', 'cal_th', 'scr_predicted_doublets', 'cal_dobulets', 'data_batch', 'individual_id', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'cell_typist_full_predicted_labels', 'cell_typist_full_majority_voting', 'cell_typist_initial_predicted_labels', 'cell_typist_initial_majority_voting', 'final_anno', 'final_anno1', 'timeline', 'sample_id', 'all_B'\n",
       "    var: 'gene_ids', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bcell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5311238f",
   "metadata": {},
   "outputs": [],
   "source": [
    "myeloid=sc.read_h5ad('/path/to/data3/mcell_raw_matrix_annotated.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd5993c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_968620/3716584077.py:1: FutureWarning: Use anndata.concat instead of AnnData.concatenate, AnnData.concatenate is deprecated and will be removed in the future. See the tutorial for concat at: https://anndata.readthedocs.io/en/latest/concatenation.html\n",
      "  combined_data=tcell.concatenate(bcell,myeloid)\n"
     ]
    }
   ],
   "source": [
    "combined_data=tcell.concatenate(bcell,myeloid) # combine the three datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ce2c52a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 122263 × 35976\n",
       "    obs: 'patient_id', 'time_after_LPS', 'Sex', 'age', 'batch', 'IR_VJ_1_locus_tcr', 'IR_VJ_2_locus_tcr', 'IR_VDJ_1_locus_tcr', 'IR_VDJ_2_locus_tcr', 'IR_VJ_1_cdr3_tcr', 'IR_VJ_2_cdr3_tcr', 'IR_VDJ_1_cdr3_tcr', 'IR_VDJ_2_cdr3_tcr', 'IR_VJ_1_cdr3_nt_tcr', 'IR_VJ_2_cdr3_nt_tcr', 'IR_VDJ_1_cdr3_nt_tcr', 'IR_VDJ_2_cdr3_nt_tcr', 'IR_VJ_1_expr_tcr', 'IR_VJ_2_expr_tcr', 'IR_VDJ_1_expr_tcr', 'IR_VDJ_2_expr_tcr', 'IR_VJ_1_expr_raw_tcr', 'IR_VJ_2_expr_raw_tcr', 'IR_VDJ_1_expr_raw_tcr', 'IR_VDJ_2_expr_raw_tcr', 'IR_VJ_1_v_gene_tcr', 'IR_VJ_2_v_gene_tcr', 'IR_VDJ_1_v_gene_tcr', 'IR_VDJ_2_v_gene_tcr', 'IR_VJ_1_d_gene_tcr', 'IR_VJ_2_d_gene_tcr', 'IR_VDJ_1_d_gene_tcr', 'IR_VDJ_2_d_gene_tcr', 'IR_VJ_1_j_gene_tcr', 'IR_VJ_2_j_gene_tcr', 'IR_VDJ_1_j_gene_tcr', 'IR_VDJ_2_j_gene_tcr', 'IR_VJ_1_c_gene_tcr', 'IR_VJ_2_c_gene_tcr', 'IR_VDJ_1_c_gene_tcr', 'IR_VDJ_2_c_gene_tcr', 'IR_VJ_1_junction_ins_tcr', 'IR_VJ_2_junction_ins_tcr', 'IR_VDJ_1_junction_ins_tcr', 'IR_VDJ_2_junction_ins_tcr', 'has_ir_tcr', 'multi_chain_tcr', 'IR_VJ_1_locus_bcr', 'IR_VJ_2_locus_bcr', 'IR_VDJ_1_locus_bcr', 'IR_VDJ_2_locus_bcr', 'IR_VJ_1_cdr3_bcr', 'IR_VJ_2_cdr3_bcr', 'IR_VDJ_1_cdr3_bcr', 'IR_VDJ_2_cdr3_bcr', 'IR_VJ_1_cdr3_nt_bcr', 'IR_VJ_2_cdr3_nt_bcr', 'IR_VDJ_1_cdr3_nt_bcr', 'IR_VDJ_2_cdr3_nt_bcr', 'IR_VJ_1_expr_bcr', 'IR_VJ_2_expr_bcr', 'IR_VDJ_1_expr_bcr', 'IR_VDJ_2_expr_bcr', 'IR_VJ_1_expr_raw_bcr', 'IR_VJ_2_expr_raw_bcr', 'IR_VDJ_1_expr_raw_bcr', 'IR_VDJ_2_expr_raw_bcr', 'IR_VJ_1_v_gene_bcr', 'IR_VJ_2_v_gene_bcr', 'IR_VDJ_1_v_gene_bcr', 'IR_VDJ_2_v_gene_bcr', 'IR_VJ_1_d_gene_bcr', 'IR_VJ_2_d_gene_bcr', 'IR_VDJ_1_d_gene_bcr', 'IR_VDJ_2_d_gene_bcr', 'IR_VJ_1_j_gene_bcr', 'IR_VJ_2_j_gene_bcr', 'IR_VDJ_1_j_gene_bcr', 'IR_VDJ_2_j_gene_bcr', 'IR_VJ_1_c_gene_bcr', 'IR_VJ_2_c_gene_bcr', 'IR_VDJ_1_c_gene_bcr', 'IR_VDJ_2_c_gene_bcr', 'IR_VJ_1_junction_ins_bcr', 'IR_VJ_2_junction_ins_bcr', 'IR_VDJ_1_junction_ins_bcr', 'IR_VDJ_2_junction_ins_bcr', 'has_ir_bcr', 'multi_chain_bcr', 'scr_th', 'doublet_scores', 'cal_th', 'scr_predicted_doublets', 'cal_dobulets', 'data_batch', 'individual_id', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'cell_typist_full_predicted_labels', 'cell_typist_full_majority_voting', 'cell_typist_initial_predicted_labels', 'cell_typist_initial_majority_voting', 'final_anno', 'timeline', 'sample_id', 'all_T', 'final_anno1', 'all_B'\n",
       "    var: 'gene_ids', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'GEX-0', 'GEX-2'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data # check combined data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ebf9cc1",
   "metadata": {},
   "source": [
    "Next, we apply a set of modifications, QC analyses and preprocessing as data is raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d680c21d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mitochondrial genes\n",
    "combined_data.var[\"mt\"] = combined_data.var_names.str.startswith(\"MT-\")\n",
    "# ribosomal genes\n",
    "combined_data.var[\"ribo\"] = combined_data.var_names.str.startswith((\"RPS\", \"RPL\"))\n",
    "# hemoglobin genes.\n",
    "combined_data.var[\"hb\"] = combined_data.var_names.str.contains((\"^HB[^(P)]\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4127d636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 122263 × 35976\n",
       "    obs: 'patient_id', 'time_after_LPS', 'Sex', 'age', 'batch', 'IR_VJ_1_locus_tcr', 'IR_VJ_2_locus_tcr', 'IR_VDJ_1_locus_tcr', 'IR_VDJ_2_locus_tcr', 'IR_VJ_1_cdr3_tcr', 'IR_VJ_2_cdr3_tcr', 'IR_VDJ_1_cdr3_tcr', 'IR_VDJ_2_cdr3_tcr', 'IR_VJ_1_cdr3_nt_tcr', 'IR_VJ_2_cdr3_nt_tcr', 'IR_VDJ_1_cdr3_nt_tcr', 'IR_VDJ_2_cdr3_nt_tcr', 'IR_VJ_1_expr_tcr', 'IR_VJ_2_expr_tcr', 'IR_VDJ_1_expr_tcr', 'IR_VDJ_2_expr_tcr', 'IR_VJ_1_expr_raw_tcr', 'IR_VJ_2_expr_raw_tcr', 'IR_VDJ_1_expr_raw_tcr', 'IR_VDJ_2_expr_raw_tcr', 'IR_VJ_1_v_gene_tcr', 'IR_VJ_2_v_gene_tcr', 'IR_VDJ_1_v_gene_tcr', 'IR_VDJ_2_v_gene_tcr', 'IR_VJ_1_d_gene_tcr', 'IR_VJ_2_d_gene_tcr', 'IR_VDJ_1_d_gene_tcr', 'IR_VDJ_2_d_gene_tcr', 'IR_VJ_1_j_gene_tcr', 'IR_VJ_2_j_gene_tcr', 'IR_VDJ_1_j_gene_tcr', 'IR_VDJ_2_j_gene_tcr', 'IR_VJ_1_c_gene_tcr', 'IR_VJ_2_c_gene_tcr', 'IR_VDJ_1_c_gene_tcr', 'IR_VDJ_2_c_gene_tcr', 'IR_VJ_1_junction_ins_tcr', 'IR_VJ_2_junction_ins_tcr', 'IR_VDJ_1_junction_ins_tcr', 'IR_VDJ_2_junction_ins_tcr', 'has_ir_tcr', 'multi_chain_tcr', 'IR_VJ_1_locus_bcr', 'IR_VJ_2_locus_bcr', 'IR_VDJ_1_locus_bcr', 'IR_VDJ_2_locus_bcr', 'IR_VJ_1_cdr3_bcr', 'IR_VJ_2_cdr3_bcr', 'IR_VDJ_1_cdr3_bcr', 'IR_VDJ_2_cdr3_bcr', 'IR_VJ_1_cdr3_nt_bcr', 'IR_VJ_2_cdr3_nt_bcr', 'IR_VDJ_1_cdr3_nt_bcr', 'IR_VDJ_2_cdr3_nt_bcr', 'IR_VJ_1_expr_bcr', 'IR_VJ_2_expr_bcr', 'IR_VDJ_1_expr_bcr', 'IR_VDJ_2_expr_bcr', 'IR_VJ_1_expr_raw_bcr', 'IR_VJ_2_expr_raw_bcr', 'IR_VDJ_1_expr_raw_bcr', 'IR_VDJ_2_expr_raw_bcr', 'IR_VJ_1_v_gene_bcr', 'IR_VJ_2_v_gene_bcr', 'IR_VDJ_1_v_gene_bcr', 'IR_VDJ_2_v_gene_bcr', 'IR_VJ_1_d_gene_bcr', 'IR_VJ_2_d_gene_bcr', 'IR_VDJ_1_d_gene_bcr', 'IR_VDJ_2_d_gene_bcr', 'IR_VJ_1_j_gene_bcr', 'IR_VJ_2_j_gene_bcr', 'IR_VDJ_1_j_gene_bcr', 'IR_VDJ_2_j_gene_bcr', 'IR_VJ_1_c_gene_bcr', 'IR_VJ_2_c_gene_bcr', 'IR_VDJ_1_c_gene_bcr', 'IR_VDJ_2_c_gene_bcr', 'IR_VJ_1_junction_ins_bcr', 'IR_VJ_2_junction_ins_bcr', 'IR_VDJ_1_junction_ins_bcr', 'IR_VDJ_2_junction_ins_bcr', 'has_ir_bcr', 'multi_chain_bcr', 'scr_th', 'doublet_scores', 'cal_th', 'scr_predicted_doublets', 'cal_dobulets', 'data_batch', 'individual_id', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'cell_typist_full_predicted_labels', 'cell_typist_full_majority_voting', 'cell_typist_initial_predicted_labels', 'cell_typist_initial_majority_voting', 'final_anno', 'timeline', 'sample_id', 'all_T', 'final_anno1', 'all_B', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'pct_counts_in_top_20_genes', 'log1p_total_counts_mt', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo', 'total_counts_hb', 'log1p_total_counts_hb', 'pct_counts_hb'\n",
       "    var: 'gene_ids', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'GEX-0', 'GEX-2', 'ribo', 'hb', 'log1p_mean_counts', 'log1p_total_counts'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.pp.calculate_qc_metrics( # calculate QC metrics\n",
    "    combined_data, qc_vars=[\"mt\", \"ribo\", \"hb\"], inplace=True, percent_top=[20], log1p=True\n",
    ")\n",
    "combined_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57a12167",
   "metadata": {},
   "source": [
    "Visualize QC metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e384a652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 804.6x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 804.6x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 810.02x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1 = sns.displot(combined_data.obs[\"total_counts\"], bins=100, kde=False)\n",
    "sc.pl.violin(combined_data, 'total_counts')\n",
    "p2 = sc.pl.violin(combined_data, \"pct_counts_mt\")\n",
    "p3 = sc.pl.scatter(combined_data, \"total_counts\", \"n_genes_by_counts\", color=\"pct_counts_mt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "345d480f",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data = combined_data[combined_data.obs['pct_counts_mt'] <= 8] # filter cells with high mitochondrial content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84fc3207",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/software/cellgen/team361/am74/envs/pertpy/lib/python3.9/site-packages/scanpy/preprocessing/_simple.py:165: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  adata.obs[\"n_counts\"] = number\n"
     ]
    }
   ],
   "source": [
    "sc.pp.filter_cells(combined_data, max_counts=150000) # filter cells with too many counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64ddda8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_968620/3987137127.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(combined_data.obs['n_genes'], kde=False, bins=60)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='n_genes'>"
      ]
     },
     "execution_count": 15,
     "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": [
    "combined_data.obs['n_genes'] = (combined_data.X > 0).sum(1) # calculate number of genes per cell\n",
    "sns.distplot(combined_data.obs['n_genes'], kde=False, bins=60) # visualize number of genes per cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "212b053f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of genes after cell filter: 19186\n"
     ]
    }
   ],
   "source": [
    "sc.pp.filter_genes(combined_data, min_cells=20) # filter genes expressed in less than 20 cells\n",
    "print('Number of genes after cell filter: {:d}'.format(combined_data.n_vars)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e8f8df1",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data.obs['batch'] = combined_data.obs['batch'].cat.rename_categories({\n",
    "    '0': 'T cell lineage',\n",
    "    '1': 'B cell lineage',\n",
    "    '2': 'Myeloid lineage'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d1da5ab9-df5e-48cc-98e4-c38b250f31bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "final_anno\n",
       "CD14mono                     23888\n",
       "CD4_naive                    18268\n",
       "CD8_naive                    13249\n",
       "CD4_memory                   10259\n",
       "CD8_EM                        8869\n",
       "B_naive                       8185\n",
       "Int.mono                      7229\n",
       "NK_CD56dim                    4922\n",
       "CD14mono_M1                   4129\n",
       "gdT                           3325\n",
       "MAIT                          2577\n",
       "Treg                          2382\n",
       "NKT                           2376\n",
       "non-swtiched memory           1583\n",
       "CD16mono                      1213\n",
       "switched memory               1158\n",
       "CD8_CM                        1004\n",
       "DC2                            972\n",
       "Platelet                       862\n",
       "NK_CD56bright                  856\n",
       "pDC                            696\n",
       "DC3                            488\n",
       "non-switched memory/CD11c      301\n",
       "HSPC                           227\n",
       "plasma_IgA                     160\n",
       "DC1                             60\n",
       "plasma_dividing                 50\n",
       "plasma_IgG                      31\n",
       "ASDC                            23\n",
       "plasma_IgM                      18\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.obs['final_anno'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed0f7cc6-e7a4-44ca-9b27-824568b9982c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2717.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.X.max() # check max counts after filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e18f749f-bf16-4ed6-a044-9d29166794df",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data.obs['cell_type'] = combined_data.obs['final_anno'] # set cell_type based on final_anno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1b849809-6bec-4143-aa7c-983e77e13a4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAACCTGAGACAAAGG-1-0    IVLPS01_Baseline\n",
       "AAACCTGAGCGTTCCG-1-0    IVLPS01_Baseline\n",
       "AAACCTGAGTGTTTGC-1-0    IVLPS01_Baseline\n",
       "AAACCTGCAAACTGTC-1-0    IVLPS01_Baseline\n",
       "AAACCTGCACAACGTT-1-0    IVLPS01_Baseline\n",
       "                              ...       \n",
       "TTTGGTTTCGGAATCT-1-2         IVLPS12_10h\n",
       "TTTGTCAAGGCATGTG-1-2         IVLPS12_10h\n",
       "TTTGTCACAAAGGAAG-1-2         IVLPS12_10h\n",
       "TTTGTCATCAACACTG-1-2         IVLPS12_10h\n",
       "TTTGTCATCACAACGT-1-2         IVLPS12_10h\n",
       "Name: patient_id, Length: 119360, dtype: category\n",
       "Categories (23, object): ['IVLPS01_10h', 'IVLPS01_6h', 'IVLPS01_90min', 'IVLPS01_Baseline', ..., 'IVLPS06_Baseline', 'IVLPS12_10h', 'IVLPS12_6h', 'IVLPS12_90min']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.obs['patient_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8d3291db-cd12-44cb-8660-ee0fd844b839",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data.obs['donor_id'] = combined_data.obs['patient_id'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ff1cd3d7-bf24-4758-93a0-9af80d4a5eec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAACCTGAGACAAAGG-1-0     0m\n",
       "AAACCTGAGCGTTCCG-1-0     0m\n",
       "AAACCTGAGTGTTTGC-1-0     0m\n",
       "AAACCTGCAAACTGTC-1-0     0m\n",
       "AAACCTGCACAACGTT-1-0     0m\n",
       "                       ... \n",
       "TTTGGTTTCGGAATCT-1-2    10h\n",
       "TTTGTCAAGGCATGTG-1-2    10h\n",
       "TTTGTCACAAAGGAAG-1-2    10h\n",
       "TTTGTCATCAACACTG-1-2    10h\n",
       "TTTGTCATCACAACGT-1-2    10h\n",
       "Name: time_after_LPS, Length: 119360, dtype: category\n",
       "Categories (4, object): ['0m', '10h', '6h', '90m']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.obs['time_after_LPS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "58236fc3-8e73-4b5c-a8a5-bea968a74208",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data.obs['time_after_LPS'] = combined_data.obs['time_after_LPS'].cat.rename_categories({\n",
    "    '0m': 'normal',\n",
    "    '10h': '10h_LPS',\n",
    "    '6h': '6h_LPS',\n",
    "    '90m': '90m_LPS'\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "556d9b42-b20b-4e1e-87f3-05dbbffbcd8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAACCTGAGACAAAGG-1-0     normal\n",
       "AAACCTGAGCGTTCCG-1-0     normal\n",
       "AAACCTGAGTGTTTGC-1-0     normal\n",
       "AAACCTGCAAACTGTC-1-0     normal\n",
       "AAACCTGCACAACGTT-1-0     normal\n",
       "                         ...   \n",
       "TTTGGTTTCGGAATCT-1-2    10h_LPS\n",
       "TTTGTCAAGGCATGTG-1-2    10h_LPS\n",
       "TTTGTCACAAAGGAAG-1-2    10h_LPS\n",
       "TTTGTCATCAACACTG-1-2    10h_LPS\n",
       "TTTGTCATCACAACGT-1-2    10h_LPS\n",
       "Name: time_after_LPS, Length: 119360, dtype: category\n",
       "Categories (4, object): ['normal', '10h_LPS', '6h_LPS', '90m_LPS']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.obs['time_after_LPS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dab0df9e-8e5a-45dc-926c-77411554c95e",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data.obs['study'] = 'Private_Data_Emily'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4b991ccc-d592-4209-a7aa-ffe04b12c02f",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data.obs['tissue'] = 'blood'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "29d28ad1-9228-4d2c-a3af-9fc133aae552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 119360 × 19186\n",
       "    obs: 'patient_id', 'time_after_LPS', 'Sex', 'age', 'batch', 'IR_VJ_1_locus_tcr', 'IR_VJ_2_locus_tcr', 'IR_VDJ_1_locus_tcr', 'IR_VDJ_2_locus_tcr', 'IR_VJ_1_cdr3_tcr', 'IR_VJ_2_cdr3_tcr', 'IR_VDJ_1_cdr3_tcr', 'IR_VDJ_2_cdr3_tcr', 'IR_VJ_1_cdr3_nt_tcr', 'IR_VJ_2_cdr3_nt_tcr', 'IR_VDJ_1_cdr3_nt_tcr', 'IR_VDJ_2_cdr3_nt_tcr', 'IR_VJ_1_expr_tcr', 'IR_VJ_2_expr_tcr', 'IR_VDJ_1_expr_tcr', 'IR_VDJ_2_expr_tcr', 'IR_VJ_1_expr_raw_tcr', 'IR_VJ_2_expr_raw_tcr', 'IR_VDJ_1_expr_raw_tcr', 'IR_VDJ_2_expr_raw_tcr', 'IR_VJ_1_v_gene_tcr', 'IR_VJ_2_v_gene_tcr', 'IR_VDJ_1_v_gene_tcr', 'IR_VDJ_2_v_gene_tcr', 'IR_VJ_1_d_gene_tcr', 'IR_VJ_2_d_gene_tcr', 'IR_VDJ_1_d_gene_tcr', 'IR_VDJ_2_d_gene_tcr', 'IR_VJ_1_j_gene_tcr', 'IR_VJ_2_j_gene_tcr', 'IR_VDJ_1_j_gene_tcr', 'IR_VDJ_2_j_gene_tcr', 'IR_VJ_1_c_gene_tcr', 'IR_VJ_2_c_gene_tcr', 'IR_VDJ_1_c_gene_tcr', 'IR_VDJ_2_c_gene_tcr', 'IR_VJ_1_junction_ins_tcr', 'IR_VJ_2_junction_ins_tcr', 'IR_VDJ_1_junction_ins_tcr', 'IR_VDJ_2_junction_ins_tcr', 'has_ir_tcr', 'multi_chain_tcr', 'IR_VJ_1_locus_bcr', 'IR_VJ_2_locus_bcr', 'IR_VDJ_1_locus_bcr', 'IR_VDJ_2_locus_bcr', 'IR_VJ_1_cdr3_bcr', 'IR_VJ_2_cdr3_bcr', 'IR_VDJ_1_cdr3_bcr', 'IR_VDJ_2_cdr3_bcr', 'IR_VJ_1_cdr3_nt_bcr', 'IR_VJ_2_cdr3_nt_bcr', 'IR_VDJ_1_cdr3_nt_bcr', 'IR_VDJ_2_cdr3_nt_bcr', 'IR_VJ_1_expr_bcr', 'IR_VJ_2_expr_bcr', 'IR_VDJ_1_expr_bcr', 'IR_VDJ_2_expr_bcr', 'IR_VJ_1_expr_raw_bcr', 'IR_VJ_2_expr_raw_bcr', 'IR_VDJ_1_expr_raw_bcr', 'IR_VDJ_2_expr_raw_bcr', 'IR_VJ_1_v_gene_bcr', 'IR_VJ_2_v_gene_bcr', 'IR_VDJ_1_v_gene_bcr', 'IR_VDJ_2_v_gene_bcr', 'IR_VJ_1_d_gene_bcr', 'IR_VJ_2_d_gene_bcr', 'IR_VDJ_1_d_gene_bcr', 'IR_VDJ_2_d_gene_bcr', 'IR_VJ_1_j_gene_bcr', 'IR_VJ_2_j_gene_bcr', 'IR_VDJ_1_j_gene_bcr', 'IR_VDJ_2_j_gene_bcr', 'IR_VJ_1_c_gene_bcr', 'IR_VJ_2_c_gene_bcr', 'IR_VDJ_1_c_gene_bcr', 'IR_VDJ_2_c_gene_bcr', 'IR_VJ_1_junction_ins_bcr', 'IR_VJ_2_junction_ins_bcr', 'IR_VDJ_1_junction_ins_bcr', 'IR_VDJ_2_junction_ins_bcr', 'has_ir_bcr', 'multi_chain_bcr', 'scr_th', 'doublet_scores', 'cal_th', 'scr_predicted_doublets', 'cal_dobulets', 'data_batch', 'individual_id', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'cell_typist_full_predicted_labels', 'cell_typist_full_majority_voting', 'cell_typist_initial_predicted_labels', 'cell_typist_initial_majority_voting', 'final_anno', 'timeline', 'sample_id', 'all_T', 'final_anno1', 'all_B', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'pct_counts_in_top_20_genes', 'log1p_total_counts_mt', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo', 'total_counts_hb', 'log1p_total_counts_hb', 'pct_counts_hb', 'n_counts', 'cell_type', 'donor_id', 'study', 'tissue'\n",
       "    var: 'gene_ids', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'GEX-0', 'GEX-2', 'ribo', 'hb', 'log1p_mean_counts', 'log1p_total_counts', 'n_cells'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f0f40697-1c2b-44f5-9124-79188f73d129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age\n",
       "23.0    29443\n",
       "37.0    29386\n",
       "32.0    21621\n",
       "36.0    18532\n",
       "29.0    15735\n",
       "24.0     4643\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.obs['age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9be04851-6803-434b-8563-efe3839298e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def age_to_group(age):\n",
    "    if pd.isna(age):\n",
    "        return 'Unknown'\n",
    "    elif age < 0:\n",
    "        # Prenatal age, optional handling\n",
    "        return 'Prenatal'\n",
    "    elif age <= 18:\n",
    "        return 'Childhood'\n",
    "    elif 18 < age <= 25:\n",
    "        return 'Young Adult'\n",
    "    elif 26 <= age <= 64:\n",
    "        return 'Adult'\n",
    "    elif age >= 65:\n",
    "        return 'Old'\n",
    "    else:\n",
    "        return 'Unknown'\n",
    "\n",
    "combined_data.obs['development_stage'] = combined_data.obs['age'].apply(age_to_group)\n",
    "\n",
    "# Optionally convert to categorical with order:\n",
    "combined_data.obs['development_stage'] = pd.Categorical(\n",
    "    combined_data.obs['development_stage'],\n",
    "    categories=['Prenatal', 'Childhood', 'Young Adult', 'Adult', 'Old', 'Unknown'],\n",
    "    ordered=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f6bef455-c97f-4e56-a559-9ec22a24c098",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "development_stage\n",
       "Adult          85274\n",
       "Young Adult    34086\n",
       "Childhood          0\n",
       "Prenatal           0\n",
       "Old                0\n",
       "Unknown            0\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.obs['development_stage'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "062dfe92-2248-42ab-9c41-a8829485c315",
   "metadata": {},
   "outputs": [],
   "source": [
    "ref = pd.read_csv(\"../../../../Ensembl_symbol_Human_(GRCh38.p14)/mart_export.txt\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4d27fcb6-e6d9-47f2-b79d-4065d6e52ffa",
   "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>HGNC symbol</th>\n",
       "      <th>Gene stable ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MT-TF</td>\n",
       "      <td>ENSG00000210049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MT-RNR1</td>\n",
       "      <td>ENSG00000211459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MT-TV</td>\n",
       "      <td>ENSG00000210077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MT-RNR2</td>\n",
       "      <td>ENSG00000210082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MT-TL1</td>\n",
       "      <td>ENSG00000209082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86363</th>\n",
       "      <td>SNHG12</td>\n",
       "      <td>ENSG00000197989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86364</th>\n",
       "      <td>TAF12-DT</td>\n",
       "      <td>ENSG00000229388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86365</th>\n",
       "      <td>NaN</td>\n",
       "      <td>ENSG00000289291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86366</th>\n",
       "      <td>RNU11</td>\n",
       "      <td>ENSG00000274978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86367</th>\n",
       "      <td>NaN</td>\n",
       "      <td>ENSG00000296488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86368 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      HGNC symbol   Gene stable ID\n",
       "0           MT-TF  ENSG00000210049\n",
       "1         MT-RNR1  ENSG00000211459\n",
       "2           MT-TV  ENSG00000210077\n",
       "3         MT-RNR2  ENSG00000210082\n",
       "4          MT-TL1  ENSG00000209082\n",
       "...           ...              ...\n",
       "86363      SNHG12  ENSG00000197989\n",
       "86364    TAF12-DT  ENSG00000229388\n",
       "86365         NaN  ENSG00000289291\n",
       "86366       RNU11  ENSG00000274978\n",
       "86367         NaN  ENSG00000296488\n",
       "\n",
       "[86368 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2afddd66-cd62-460b-b9c3-7da64738e746",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Current gene symbols from var_names\n",
    "gene_symbols = combined_data.var_names.to_list()\n",
    "\n",
    "# Create mapping: symbol -> Ensembl ID\n",
    "symbol_to_ensembl = dict(zip(ref['HGNC symbol'], ref['Gene stable ID']))\n",
    "\n",
    "# Map gene symbols to Ensembl IDs (or None if not found)\n",
    "ensembl_ids = [symbol_to_ensembl.get(gene, None) for gene in gene_symbols]\n",
    "\n",
    "# Create a mask to keep only genes with Ensembl IDs starting with \"ENSG\"\n",
    "keep_mask = [ensembl is not None and ensembl.startswith(\"ENSG\") for ensembl in ensembl_ids]\n",
    "\n",
    "# Filter var and var_names accordingly\n",
    "combined_data = combined_data[:, keep_mask].copy()  # subset cells and genes\n",
    "\n",
    "# Update var_names to Ensembl IDs for kept genes\n",
    "combined_data.var['ensembl_id'] = [ensembl_ids[i] for i, keep in enumerate(keep_mask) if keep]\n",
    "combined_data.var_names = combined_data.var['ensembl_id'].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "900b5dc2-59ec-4c36-80aa-a2e4fb995e42",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "combined_data.var['gene_symbol'] = combined_data.var['gene_ids']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1b474ef7-bf8f-4f95-a6b1-966cdfc44677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2672.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.X.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98c9299d-f87a-4509-953a-b4046980a143",
   "metadata": {},
   "outputs": [],
   "source": [
    "if hasattr(combined_data.X, 'sum'):\n",
    "    combined_data.obs['n_counts'] = np.ravel(combined_data.X.sum(axis=1))\n",
    "else:\n",
    "    combined_data.obs['n_counts'] = combined_data.X.sum(axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3947833d-ec40-44ed-bbac-412b7ca5dbbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAACCTGAGACAAAGG-1-0           NKT\n",
       "AAACCTGAGCGTTCCG-1-0     CD8_naive\n",
       "AAACCTGAGTGTTTGC-1-0    CD4_memory\n",
       "AAACCTGCAAACTGTC-1-0           NKT\n",
       "AAACCTGCACAACGTT-1-0     CD4_naive\n",
       "                           ...    \n",
       "TTTGGTTTCGGAATCT-1-2      Int.mono\n",
       "TTTGTCAAGGCATGTG-1-2      Int.mono\n",
       "TTTGTCACAAAGGAAG-1-2      CD14mono\n",
       "TTTGTCATCAACACTG-1-2      CD14mono\n",
       "TTTGTCATCACAACGT-1-2      CD14mono\n",
       "Name: cell_type, Length: 119360, dtype: category\n",
       "Categories (30, object): ['ASDC', 'B_naive', 'CD4_memory', 'CD4_naive', ..., 'plasma_IgG', 'plasma_IgM', 'plasma_dividing', 'switched memory']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.obs['cell_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40311163",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data.write_h5ad('./raw_data_LPS_Private_Haniffa_lab.h5ad') # save the processed data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "682c2023",
   "metadata": {},
   "source": [
    "## 1.3. Concatenation of public and preprocessed prived data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d7e8d42e-7f9e-4e42-8215-e744db4db30f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfs/users/nfs_a/am74/.cache/pypoetry/virtualenvs/cytomeister-AO4Zveyp-py3.10/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "  utils.warn_names_duplicates(\"var\")\n"
     ]
    }
   ],
   "source": [
    "adata1 = sc.read('./Private_emily/raw_data_LPS_Private_Haniffa_lab.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce344fef-3daf-481f-885b-464fb1d0bdd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata2 = sc.read('./Public_Emily/Public_LPS_PP.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5619e66b-ada2-4505-9a15-474b773e52d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make var_names unique to avoid conflicts during concatenation\n",
    "adata2.var_names_make_unique()\n",
    "adata1.var_names_make_unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba786e61-b7db-438b-b8d0-251820e8aff5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_983311/3094885911.py:1: FutureWarning: Use anndata.concat instead of AnnData.concatenate, AnnData.concatenate is deprecated and will be removed in the future. See the tutorial for concat at: https://anndata.readthedocs.io/en/latest/concatenation.html\n",
      "  adata = adata1.concatenate(adata2)\n"
     ]
    }
   ],
   "source": [
    "# Concatenate the two datasets\n",
    "adata = adata1.concatenate(adata2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdb275ba-f559-49b2-9c6e-3a38bb3a11f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 224150 × 13826\n",
       "    obs: 'patient_id', 'time_after_LPS', 'Sex', 'age', 'batch', 'IR_VJ_1_locus_tcr', 'IR_VJ_2_locus_tcr', 'IR_VDJ_1_locus_tcr', 'IR_VDJ_2_locus_tcr', 'IR_VJ_1_cdr3_tcr', 'IR_VJ_2_cdr3_tcr', 'IR_VDJ_1_cdr3_tcr', 'IR_VDJ_2_cdr3_tcr', 'IR_VJ_1_cdr3_nt_tcr', 'IR_VJ_2_cdr3_nt_tcr', 'IR_VDJ_1_cdr3_nt_tcr', 'IR_VDJ_2_cdr3_nt_tcr', 'IR_VJ_1_expr_tcr', 'IR_VJ_2_expr_tcr', 'IR_VDJ_1_expr_tcr', 'IR_VDJ_2_expr_tcr', 'IR_VJ_1_expr_raw_tcr', 'IR_VJ_2_expr_raw_tcr', 'IR_VDJ_1_expr_raw_tcr', 'IR_VDJ_2_expr_raw_tcr', 'IR_VJ_1_v_gene_tcr', 'IR_VJ_2_v_gene_tcr', 'IR_VDJ_1_v_gene_tcr', 'IR_VDJ_2_v_gene_tcr', 'IR_VJ_1_d_gene_tcr', 'IR_VJ_2_d_gene_tcr', 'IR_VDJ_1_d_gene_tcr', 'IR_VDJ_2_d_gene_tcr', 'IR_VJ_1_j_gene_tcr', 'IR_VJ_2_j_gene_tcr', 'IR_VDJ_1_j_gene_tcr', 'IR_VDJ_2_j_gene_tcr', 'IR_VJ_1_c_gene_tcr', 'IR_VJ_2_c_gene_tcr', 'IR_VDJ_1_c_gene_tcr', 'IR_VDJ_2_c_gene_tcr', 'IR_VJ_1_junction_ins_tcr', 'IR_VJ_2_junction_ins_tcr', 'IR_VDJ_1_junction_ins_tcr', 'IR_VDJ_2_junction_ins_tcr', 'has_ir_tcr', 'multi_chain_tcr', 'IR_VJ_1_locus_bcr', 'IR_VJ_2_locus_bcr', 'IR_VDJ_1_locus_bcr', 'IR_VDJ_2_locus_bcr', 'IR_VJ_1_cdr3_bcr', 'IR_VJ_2_cdr3_bcr', 'IR_VDJ_1_cdr3_bcr', 'IR_VDJ_2_cdr3_bcr', 'IR_VJ_1_cdr3_nt_bcr', 'IR_VJ_2_cdr3_nt_bcr', 'IR_VDJ_1_cdr3_nt_bcr', 'IR_VDJ_2_cdr3_nt_bcr', 'IR_VJ_1_expr_bcr', 'IR_VJ_2_expr_bcr', 'IR_VDJ_1_expr_bcr', 'IR_VDJ_2_expr_bcr', 'IR_VJ_1_expr_raw_bcr', 'IR_VJ_2_expr_raw_bcr', 'IR_VDJ_1_expr_raw_bcr', 'IR_VDJ_2_expr_raw_bcr', 'IR_VJ_1_v_gene_bcr', 'IR_VJ_2_v_gene_bcr', 'IR_VDJ_1_v_gene_bcr', 'IR_VDJ_2_v_gene_bcr', 'IR_VJ_1_d_gene_bcr', 'IR_VJ_2_d_gene_bcr', 'IR_VDJ_1_d_gene_bcr', 'IR_VDJ_2_d_gene_bcr', 'IR_VJ_1_j_gene_bcr', 'IR_VJ_2_j_gene_bcr', 'IR_VDJ_1_j_gene_bcr', 'IR_VDJ_2_j_gene_bcr', 'IR_VJ_1_c_gene_bcr', 'IR_VJ_2_c_gene_bcr', 'IR_VDJ_1_c_gene_bcr', 'IR_VDJ_2_c_gene_bcr', 'IR_VJ_1_junction_ins_bcr', 'IR_VJ_2_junction_ins_bcr', 'IR_VDJ_1_junction_ins_bcr', 'IR_VDJ_2_junction_ins_bcr', 'has_ir_bcr', 'multi_chain_bcr', 'scr_th', 'doublet_scores', 'cal_th', 'scr_predicted_doublets', 'cal_dobulets', 'data_batch', 'individual_id', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'cell_typist_full_predicted_labels', 'cell_typist_full_majority_voting', 'cell_typist_initial_predicted_labels', 'cell_typist_initial_majority_voting', 'final_anno', 'timeline', 'sample_id', 'all_T', 'final_anno1', 'all_B', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'pct_counts_in_top_20_genes', 'log1p_total_counts_mt', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo', 'total_counts_hb', 'log1p_total_counts_hb', 'pct_counts_hb', 'n_counts', 'cell_type', 'donor_id', 'study', 'tissue', 'development_stage', 'full_clustering', 'initial_clustering', 'Resample', 'Collection_Day', 'Age_interval', 'Swab_result', 'Status', 'Smoker', 'Status_on_day_collection', 'Status_on_day_collection_summary', 'Days_from_onset', 'Site', 'Worst_Clinical_Status', 'Outcome', 'life_stage'\n",
       "    var: 'gene_ids-0', 'mt-0', 'n_cells_by_counts-0', 'mean_counts-0', 'pct_dropout_by_counts-0', 'total_counts-0', 'GEX-0-0', 'GEX-2-0', 'ribo-0', 'hb-0', 'log1p_mean_counts-0', 'log1p_total_counts-0', 'n_cells-0', 'ensembl_id-0', 'gene_symbol-0', 'ensembl_id-1', 'gene_symbol-1', 'feature_types-1'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata # check the merged data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f9df4b0-cefd-44b4-ad2d-55c82c336808",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time_after_LPS\n",
       "normal     148742\n",
       "6h_LPS      43810\n",
       "10h_LPS     20890\n",
       "90m_LPS     10708\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs['time_after_LPS'].value_counts() # check time points available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4ec4b0d-2a00-441c-99d0-cda5c80fe134",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cell_type\n",
       "CD14mono                     23888\n",
       "CD4_naive                    18268\n",
       "B_naive                      15093\n",
       "CD4.Naive                    13829\n",
       "NK_16hi                      13473\n",
       "CD8_naive                    13249\n",
       "CD4_memory                   10259\n",
       "CD8_EM                        8869\n",
       "gdT                           8273\n",
       "CD8.Naive                     8105\n",
       "CD4.CM                        7505\n",
       "Int.mono                      7229\n",
       "CD83_CD14_mono                6772\n",
       "CD8.TE                        6501\n",
       "CD4.IL22                      6379\n",
       "MAIT                          6042\n",
       "CD8.EM                        5734\n",
       "NK_CD56dim                    4922\n",
       "CD14mono_M1                   4129\n",
       "CD14_mono                     3774\n",
       "NKT                           3240\n",
       "CD16_mono                     3007\n",
       "Treg                          2457\n",
       "Platelets                     2363\n",
       "NK_56hi                       2190\n",
       "DC2                           2024\n",
       "non-swtiched memory           1583\n",
       "DC3                           1463\n",
       "B_switched_memory             1395\n",
       "pDC                           1374\n",
       "CD16mono                      1213\n",
       "switched memory               1158\n",
       "CD8_CM                        1004\n",
       "B_immature                     966\n",
       "Platelet                       862\n",
       "NK_CD56bright                  856\n",
       "CD4.Tfh                        800\n",
       "B_non-switched_memory          647\n",
       "B_exhausted                    334\n",
       "non-switched memory/CD11c      301\n",
       "RBC                            301\n",
       "HSPC                           227\n",
       "NK_prolif                      221\n",
       "ILC1_3                         213\n",
       "C1_CD16_mono                   198\n",
       "CD4.EM                         188\n",
       "Plasma_cell_IgA                173\n",
       "Plasma_cell_IgG                168\n",
       "DC1                            164\n",
       "plasma_IgA                     160\n",
       "CD4.Th1                        101\n",
       "Plasmablast                     95\n",
       "Plasma_cell_IgM                 84\n",
       "HSC_erythroid                   74\n",
       "CD8.Prolif                      70\n",
       "HSC_CD38pos                     61\n",
       "plasma_dividing                 50\n",
       "plasma_IgG                      31\n",
       "ASDC                            23\n",
       "plasma_IgM                      18\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs['cell_type'].value_counts() # check cell types available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a003d6ef-1293-4307-a927-114bac642486",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell_type_harmonized\n",
      "CD4+ T cells                   57329\n",
      "CD14 monocytes                 45792\n",
      "CD8+ T cells                   43532\n",
      "NK                             21662\n",
      "B cell                         21477\n",
      "gamma-delta T cell              8273\n",
      "mucosal invariant T cell        6042\n",
      "CD16 monocytes                  4418\n",
      "Dendritic cells                 3651\n",
      "NKT                             3240\n",
      "platelet                        3225\n",
      "regulatory T cell               2457\n",
      "Plasmocytoid dendritic cell     1374\n",
      "plasma cell                      779\n",
      "Red_Blood_Cell                   301\n",
      "hematopoietic stem cell          227\n",
      "ILC1_3                           213\n",
      "HSC_Erythroid                     74\n",
      "HSC_CD38pos                       61\n",
      "ASDC                              23\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Define a mapping dictionary for renaming\n",
    "cell_type_mapping = {\n",
    "    # Monocytes\n",
    "    \"CD14mono\": \"CD14 monocytes\",\n",
    "    \"CD14_mono\": \"CD14 monocytes\",\n",
    "    \"CD83_CD14_mono\": \"CD14 monocytes\",\n",
    "    \"CD14mono_M1\": \"CD14 monocytes\",\n",
    "    \"CD16_mono\": \"CD16 monocytes\",\n",
    "    \"CD16mono\": \"CD16 monocytes\",\n",
    "    \"C1_CD16_mono\": \"CD16 monocytes\",\n",
    "    # CD4 T cells\n",
    "    \"CD4_naive\": \"CD4+ T cells\",\n",
    "    \"CD4.Naive\": \"CD4+ T cells\",\n",
    "    \"CD4_memory\": \"CD4+ T cells\",\n",
    "    \"CD4.CM\": \"CD4+ T cells\",\n",
    "    \"CD4.Tfh\": \"CD4+ T cells\",\n",
    "    \"CD4.Th1\": \"CD4+ T cells\",\n",
    "    \"CD4.IL22\": \"CD4+ T cells\",\n",
    "    \"CD4.EM\": \"CD4+ T cells\",\n",
    "    # CD8 T cells\n",
    "    \"CD8_naive\": \"CD8+ T cells\",\n",
    "    \"CD8.Naive\": \"CD8+ T cells\",\n",
    "    \"CD8_EM\": \"CD8+ T cells\",\n",
    "    \"CD8.EM\": \"CD8+ T cells\",\n",
    "    \"CD8_CM\": \"CD8+ T cells\",\n",
    "    \"CD8.TE\": \"CD8+ T cells\",\n",
    "    \"CD8.Prolif\": \"CD8+ T cells\",\n",
    "    # NK cells\n",
    "    \"NK_16hi\": \"NK\",\n",
    "    \"NK_56hi\": \"NK\",\n",
    "    \"NK_CD56dim\": \"NK\",\n",
    "    \"NK_CD56bright\": \"NK\",\n",
    "    \"NK_prolif\": \"NK\",\n",
    "    # B cells\n",
    "    \"B_naive\": \"B cell\",\n",
    "    \"B_switched_memory\": \"B cell\",\n",
    "    \"non-swtiched memory\" : \"B cell\",\n",
    "    \"switched memory\": \"B cell\",\n",
    "    \"B_non-switched_memory\": \"B cell\",\n",
    "    \"non-switched memory\": \"B cell\",\n",
    "    \"non-switched memory/CD11c\": \"B cell\",\n",
    "    \"B_immature\": \"B cell\",\n",
    "    \"B_exhausted\": \"B cell\",\n",
    "    # Plasma cells\n",
    "    \"Plasma_cell_IgA\": \"plasma cell\",\n",
    "    \"plasma_IgA\": \"plasma cell\",\n",
    "    \"Plasma_cell_IgG\": \"plasma cell\",\n",
    "    \"plasma_IgG\": \"plasma cell\",\n",
    "    \"Plasma_cell_IgM\": \"plasma cell\",\n",
    "    \"plasma_IgM\": \"plasma cell\",\n",
    "    \"Plasmablast\": \"plasma cell\",\n",
    "    \"plasma_dividing\": \"plasma cell\",\n",
    "    # Others\n",
    "    \"Platelets\": \"platelet\",\n",
    "    \"Platelet\": \"platelet\",\n",
    "    \"Treg\": \"regulatory T cell\",\n",
    "    \"MAIT\": \"mucosal invariant T cell\",\n",
    "    \"gdT\": \"gamma-delta T cell\",\n",
    "    \"NKT\": \"NKT\",\n",
    "    \"pDC\": \"Plasmocytoid dendritic cell\",\n",
    "    \"DC1\": \"Dendritic cells\",\n",
    "    \"DC2\": \"Dendritic cells\",\n",
    "    \"DC3\": \"Dendritic cells\",\n",
    "    \"ASDC\": \"ASDC\",\n",
    "    \"Int.mono\": \"CD14 monocytes\",\n",
    "    \"RBC\": \"Red_Blood_Cell\",\n",
    "    \"HSPC\": \"hematopoietic stem cell\",\n",
    "    \"HSC_erythroid\": \"HSC_Erythroid\",\n",
    "    \"HSC_CD38pos\": \"HSC_CD38pos\",\n",
    "    \"ILC1_3\": \"ILC1_3\",\n",
    "}\n",
    "\n",
    "# Apply the mapping to adata.obs['cell_type']\n",
    "adata.obs['cell_type_harmonized'] = adata.obs['cell_type'].map(cell_type_mapping).fillna(adata.obs['cell_type'])\n",
    "\n",
    "# Verify the new counts\n",
    "print(adata.obs['cell_type_harmonized'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b1eb036-6c9f-4086-89de-7f585966a76d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cell types not present in all timepoints: ['ASDC', 'HSC_CD38pos', 'HSC_Erythroid', 'ILC1_3', 'Red_Blood_Cell']\n"
     ]
    }
   ],
   "source": [
    "# Get unique timepoints\n",
    "all_timepoints = adata.obs['time_after_LPS'].unique()\n",
    "\n",
    "# Group by cell_type and get unique timepoints per cell type\n",
    "cell_type_timepoints = (\n",
    "    adata.obs\n",
    "    .groupby('cell_type_harmonized')['time_after_LPS']\n",
    "    .unique()\n",
    "    .apply(list)\n",
    ")\n",
    "\n",
    "# Find cell types that do not appear in all timepoints\n",
    "missing_in_any_timepoint = [\n",
    "    ct for ct in cell_type_timepoints.index \n",
    "    if not set(all_timepoints).issubset(set(cell_type_timepoints[ct]))\n",
    "]\n",
    "\n",
    "print(\"Cell types not present in all timepoints:\", missing_in_any_timepoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "df9c46bc-9bba-4850-b585-da3ee33e1eea",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "adata = sc.read('./full_lps.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bc260ed0-f373-4719-b375-67f612792379",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cell_types_to_remove = ['ASDC', 'HSC_CD38pos', 'HSC_Erythroid', 'ILC1_3', 'Red_Blood_Cell']\n",
    "keep_mask = ~adata.obs['cell_type_harmonized'].isin(cell_types_to_remove)\n",
    "adata_filtered = adata[keep_mask, :].copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a175253-9dbe-446b-97c5-44590c3a99bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cell_type_harmonized\n",
       "CD4+ T cells                   57329\n",
       "CD14 monocytes                 45792\n",
       "CD8+ T cells                   43532\n",
       "NK                             21662\n",
       "B cell                         21477\n",
       "gamma-delta T cell              8273\n",
       "mucosal invariant T cell        6042\n",
       "CD16 monocytes                  4418\n",
       "Dendritic cells                 3651\n",
       "NKT                             3240\n",
       "platelet                        3225\n",
       "regulatory T cell               2457\n",
       "Plasmocytoid dendritic cell     1374\n",
       "plasma cell                      779\n",
       "hematopoietic stem cell          227\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_filtered.obs['cell_type_harmonized'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9d255125-d205-4cac-86a3-d6789e94c75e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(adata_filtered.obs['cell_type_harmonized']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9c1c5aa5-371d-48eb-aa70-97cc3d726cf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float32(4066.0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_filtered.X.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "258d1d3a-6ee8-49e2-91c7-e66bb0ffdd68",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_filtered.write_h5ad('./full_lps.h5ad') # save the final processed data"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "scanpy",
   "language": "python",
   "name": "scanpy"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}