{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0e26b351-08be-44bb-8c58-d18723835452",
   "metadata": {},
   "source": [
    "# Tutorial 1: Querying Attributions\n",
    "\n",
    "SIGnature enables querying gene set activation across multiple cell types. \n",
    "\n",
    "This tutorial goes through the steps for generating cell-level gene set scores for any gene signature of interest using our pre-defined cell type atlases.\n",
    "\n",
    "Before running this tutorial, you need to:\n",
    "\n",
    "1. Download the SCimilarity model from https://zenodo.org/records/15729925\n",
    "2. Download the cell type-level attributions you are interested in from https://zenodo.org/communities/signature/. For this tutorial, you will need two attribution folders from Zenodo (https://zenodo.org/records/15794176):\n",
    "\n",
    "    1. `celltype_attributions_lung`: contains data for the first part of the tutorial showing a simple querying example. This folder can be extracted with the command `tar -xzf celltype_attributions_lung.tar.gz`\n",
    "    2. `celltype_attributions`: contains data from myeloid cells to replicate the MS1 signature analysis from the paper. This folder can be extracted with the command `tar -xzf celltype_attributions.tar.gz`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "25819111-f8bf-49f8-a5e2-c800be00c010",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "from os.path import join\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from SIGnature import SIGnature, SCimilarity, Meta\n",
    "import tiledb\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6d70e5b-6e56-45f7-b164-6a23ff565491",
   "metadata": {},
   "source": [
    "# Lung Example\n",
    "\n",
    "Compare type I pneumocyte marker genes in that cell type and compare to ionocytes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b07023d-ea0a-43e3-bcc7-fba7908fbdbd",
   "metadata": {},
   "source": [
    "### Load SCimilarity Model and Attributions TileDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6cec3f6e-c418-47aa-823a-4065b5560b4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Path to Scimilarity Model Downloaded from Zenodo (https://zenodo.org/records/15729925)\n",
    "scim_model_path = \"/home/scimilarity_model\"\n",
    "## Set use_gpu false when querying attributions\n",
    "scim = SCimilarity(model_path=scim_model_path, use_gpu=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "556ea882-1fec-4664-9835-760af0c2cad3",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Folder where the cell type attributions are stored\n",
    "## Downloaded from Zenodo (https://zenodo.org/records/15794176)\n",
    "ct_base = \"/home/celltype_attributions_lung\"\n",
    "all_ct = os.listdir(ct_base)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea1fcc05-9b25-4190-9344-8f541264ed7e",
   "metadata": {},
   "source": [
    "### Check Genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dfa6c796-5b09-4d60-bd17-fc43ad2d2ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Check genes (AT1 associated genes from CellMarker)\n",
    "goi = ['CAV1', 'CAV2', 'CLIC5']\n",
    "sig = SIGnature(gene_order=scim.gene_order, model=scim)\n",
    "gene_list = sig.check_genes(goi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00512a95-3fa0-43fa-bad1-70358b268553",
   "metadata": {},
   "source": [
    "### Calculate Mean Attribution Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0829ccaa-f685-43e2-bbde-a7a7d7689ea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "ctoi = [\"type I pneumocyte\", \"pulmonary ionocyte\"]\n",
    "score_name = \"AT1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a2613b3-b930-4dd8-8fd4-7fea1409b93f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00,  8.88it/s]\n"
     ]
    }
   ],
   "source": [
    "meta_list = []\n",
    "for ct in tqdm(ctoi):\n",
    "    ## Get specific tiledb path for location of interest\n",
    "    ct_path = join(ct_base, ct)\n",
    "    ## Load attributions tiledb path for given cell type\n",
    "    att_tdb_path = join(ct_path, \"attributions\")\n",
    "    ## Calculate\n",
    "    results = sig.query_attributions(\n",
    "        gene_list,\n",
    "        return_aggregate=True,\n",
    "        aggregate_type=\"mean\",\n",
    "        attribution_tiledb_uri=att_tdb_path,\n",
    "    )\n",
    "\n",
    "    ## Example including weights\n",
    "    # np.random.seed(114)\n",
    "    # weight_list = np.random.choice([0.5, 1, 2], len(gene_list)).tolist()\n",
    "    # results = sig.query_attributions(gene_list, return_aggregate=True, \n",
    "    #               aggregate_type='mean', weights = weight_list, \n",
    "    #               attribution_tiledb_uri=att_tdb_path)\n",
    "\n",
    "    ## Example of getting individual genes\n",
    "    # results = sig.query_attributions(gene_list, return_aggregate=False)\n",
    "\n",
    "    ## Load corresponding cell metadata\n",
    "    ct_path = join(ct_base, ct)\n",
    "    meta = pd.read_csv(join(ct_path, \"cell_metadata.csv.gz\"), index_col=0)\n",
    "\n",
    "    ## Put score\n",
    "    meta[score_name] = results\n",
    "    meta_list.append(meta.copy())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "808e12fb-cdc3-4c70-9041-8c63262155a7",
   "metadata": {},
   "source": [
    "### Analyze scores\n",
    "\n",
    "Compare mean attribution scores for AT1 epithelial genes between AT1 cells and pulmonary ionocytes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6cdbfbbd-0cb3-4ed7-ad08-f67320872edb",
   "metadata": {},
   "outputs": [],
   "source": [
    "big_meta = pd.concat(meta_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c3feea86-cfe8-4213-ab33-99bd1809117d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Cell Type')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4, 4))\n",
    "sns.violinplot(\n",
    "    data=big_meta,\n",
    "    x=\"prediction\",\n",
    "    y=\"AT1\",\n",
    "    palette=[\"tab:purple\", \"tab:green\"],\n",
    "    density_norm='width'\n",
    ")\n",
    "ax.set_ylabel(\"Mean Attribution Score\")\n",
    "ax.set_xlabel(\"Cell Type\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb4479b2-6a54-46ab-b878-de76bd1256c8",
   "metadata": {},
   "source": [
    "# MS1 Example\n",
    "\n",
    "Replicate analysis in paper by calculating mean attributions for MS1 gene set and comparing across disease."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "817c66f5-4a18-437f-8d45-4459bf86317a",
   "metadata": {},
   "source": [
    "### Load SCimilarity Model and Attributions TileDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "83c74202-d3a6-48d0-b2f1-ed9885d43130",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Path to Scimilarity Model Downloaded from Zenodo (https://zenodo.org/records/15794176)\n",
    "scim_model_path = \"/home/scimilarity_model\"\n",
    "## Set use_gpu false when querying attributions\n",
    "scim = SCimilarity(model_path=scim_model_path, use_gpu=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c354091a-a5ec-4663-a502-59119237fa19",
   "metadata": {},
   "outputs": [],
   "source": [
    "## This should be the folder where the attributions are stored\n",
    "## Path to cell type attributions downloaded from Zenodo (https://zenodo.org/records/15794176)\n",
    "ct_base = \"/home/celltype_attributions\"\n",
    "all_ct = os.listdir(ct_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4163a9e1-0939-42e5-9dd1-3aa94c91e538",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analyze all monocyte/macrophage cell populations in database\n",
    "ctoi = [\n",
    "    \"alveolar macrophage\",\n",
    "    \"classical monocyte\",\n",
    "    \"intermediate monocyte\",\n",
    "    \"macrophage\",\n",
    "    \"non-classical monocyte\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "805bcc57-3cdc-45b9-9898-406ed50635ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "ms1_genes = ['S100A8', 'S100A12', 'RETN', 'CLU', 'MCEMP1', 'IL1R2', 'CYP1B1', 'SELL', \n",
    "             'ALOX5AP', 'SLC39A8', 'PLAC8', 'ACSL1', 'CD163', 'VCAN', 'HP', 'CTSD', \n",
    "             'LGALS1', 'THBS1', 'CES1', 'S100P', 'ANXA6', 'VNN2', 'NAMPT', 'HAMP', \n",
    "             'DYSF', 'SDF2L1', 'NFE2', 'SLC2A3', 'BASP1', 'ADGRG3', 'SOD2', 'CTSA', \n",
    "             'PADI4', 'CALR', 'SOCS3', 'NKG7', 'FLOT1', 'IL1RN', 'ZDHHC19', 'LILRA5', \n",
    "             'ASGR2', 'FAM65B', 'MNDA', 'STEAP4', 'NCF4', 'LBR', 'RP11-295G20.2', \n",
    "             'UBR4', 'PADI2', 'NCF1', 'LINC00482', 'RUNX1', 'RRP12', 'HSPA1A', \n",
    "             'FLOT2', 'ANPEP', 'CXCR1', 'ECE1', 'ADAM19', 'RP11-196G18.3', 'IL4R', \n",
    "             'DNAJB11', 'FES', 'MBOAT7', 'SNHG25', 'RP1-55C23.7', 'CPEB4', 'PRR34-AS1', \n",
    "             'HSPA1B', 'LINC01001', 'C1QC', 'SBNO2', 'GTSE1', 'FOLR3', 'STAB1', 'PLK1', \n",
    "             'HYI-AS1', 'LINC01281', 'TNNT1', 'AC097495.2', 'CTB-35F21.5', 'C19orf35', \n",
    "             'AC109826.1', 'RP11-800A3.7', 'LILRA6', 'PDLIM7', 'NPLOC4', 'C15orf48', \n",
    "             'APOBR', 'CSF2RB', 'CTD-2105E13.14', 'C1QB', 'RP11-123K3.9', 'IQGAP3', \n",
    "             'GAPLINC', 'CTC-490G23.2', 'JAK3', 'CTC-246B18.10', 'MYO5B']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54547c40-4f74-4e44-b3d4-cf0449a1778d",
   "metadata": {},
   "source": [
    "### Check genes\n",
    "\n",
    "Subset the genes of interest to ones that are included in the SCimilarity model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c9d270bd-2b08-4c79-98b9-2b6b9d9ea3af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following genes are not included: C19orf35,AC097495.2,CTD-2105E13.14,CTC-246B18.10,FAM65B,CTC-490G23.2,HYI-AS1,RP11-295G20.2,RP11-800A3.7,RP1-55C23.7,RP11-123K3.9,CTB-35F21.5,RP11-196G18.3\n"
     ]
    }
   ],
   "source": [
    "sig = SIGnature(gene_order=scim.gene_order, model=scim)\n",
    "gene_list = sig.check_genes(ms1_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d358941d-9b8d-4005-b17a-cc747f98d984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gene_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc6bf4d-e73c-432f-8f77-a0d47239ee48",
   "metadata": {},
   "source": [
    "### Calculate Mean Attribution Scores for each Cell Type\n",
    "\n",
    "Iterate through tiledb of each cell type, calculate score, concatenate the metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "523e6830-ae1a-4385-87d8-7cccfecddeb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "score_name = \"MS1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5a86659d-c005-4167-818b-676d83a7e043",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:26<00:00,  5.32s/it]\n"
     ]
    }
   ],
   "source": [
    "meta_list = []\n",
    "for ct in tqdm(ctoi):\n",
    "    ## Get specific tiledb path for location of interest\n",
    "    ct_path = join(ct_base, ct)\n",
    "    ## Load attributions tiledb path for given cell type\n",
    "    att_tdb_path = join(ct_path, \"attributions\")\n",
    "    ## Calculate\n",
    "    results = sig.query_attributions(\n",
    "        gene_list,\n",
    "        return_aggregate=True,\n",
    "        aggregate_type=\"mean\",\n",
    "        attribution_tiledb_uri=att_tdb_path,\n",
    "    )\n",
    "\n",
    "    ## Example including weights\n",
    "    # np.random.seed(114)\n",
    "    # weight_list = np.random.choice([0.5, 1, 2], len(gene_list)).tolist()\n",
    "    # results = sig.query_attributions(gene_list, return_aggregate=True, \n",
    "    #               aggregate_type='mean', weights = weight_list, \n",
    "    #               attribution_tiledb_uri=att_tdb_path)\n",
    "\n",
    "    ## Example of getting individual genes\n",
    "    # results = sig.query_attributions(gene_list, return_aggregate=False, \n",
    "    #               attribution_tiledb_uri=att_tdb_path)\n",
    "\n",
    "    ## Load corresponding cell metadata\n",
    "    ct_path = join(ct_base, ct)\n",
    "    meta = pd.read_csv(join(ct_path, \"cell_metadata.csv.gz\"), index_col=0)\n",
    "\n",
    "    ## Put score\n",
    "    meta[score_name] = results\n",
    "    meta_list.append(meta.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dde998ea-c7d5-4235-aeb1-d59e68eef5a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "big_meta = pd.concat(meta_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "25e3fc17-0894-41af-9c51-8a310ad70b07",
   "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>study</th>\n",
       "      <th>sample</th>\n",
       "      <th>prediction</th>\n",
       "      <th>fm_signature_score</th>\n",
       "      <th>n_genes_by_counts</th>\n",
       "      <th>total_counts</th>\n",
       "      <th>total_counts_mt</th>\n",
       "      <th>pct_counts_mt</th>\n",
       "      <th>prediction_nn_dist</th>\n",
       "      <th>data_type</th>\n",
       "      <th>...</th>\n",
       "      <th>ESC-derived</th>\n",
       "      <th>xenograft</th>\n",
       "      <th>tissue</th>\n",
       "      <th>cell_line_cleaned</th>\n",
       "      <th>disease</th>\n",
       "      <th>FACS_sort</th>\n",
       "      <th>sex_cleaned</th>\n",
       "      <th>author_label</th>\n",
       "      <th>celltype_index</th>\n",
       "      <th>MS1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>298997</th>\n",
       "      <td>GSE160794</td>\n",
       "      <td>GSM4879379</td>\n",
       "      <td>alveolar macrophage</td>\n",
       "      <td>0.346716</td>\n",
       "      <td>3197</td>\n",
       "      <td>21991.0</td>\n",
       "      <td>959.0</td>\n",
       "      <td>4.360875</td>\n",
       "      <td>0.007285</td>\n",
       "      <td>unlabeled</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>lung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.460732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298998</th>\n",
       "      <td>GSE160794</td>\n",
       "      <td>GSM4879379</td>\n",
       "      <td>alveolar macrophage</td>\n",
       "      <td>-0.290656</td>\n",
       "      <td>1167</td>\n",
       "      <td>3657.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>4.976757</td>\n",
       "      <td>0.011017</td>\n",
       "      <td>unlabeled</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>lung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.703938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298999</th>\n",
       "      <td>GSE160794</td>\n",
       "      <td>GSM4879379</td>\n",
       "      <td>alveolar macrophage</td>\n",
       "      <td>0.695440</td>\n",
       "      <td>3515</td>\n",
       "      <td>21938.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>2.725864</td>\n",
       "      <td>0.021277</td>\n",
       "      <td>unlabeled</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>lung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0.433652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299001</th>\n",
       "      <td>GSE160794</td>\n",
       "      <td>GSM4879379</td>\n",
       "      <td>alveolar macrophage</td>\n",
       "      <td>-0.030102</td>\n",
       "      <td>2906</td>\n",
       "      <td>14594.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>4.995203</td>\n",
       "      <td>0.011253</td>\n",
       "      <td>unlabeled</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>lung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0.485425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299002</th>\n",
       "      <td>GSE160794</td>\n",
       "      <td>GSM4879379</td>\n",
       "      <td>alveolar macrophage</td>\n",
       "      <td>0.146310</td>\n",
       "      <td>3367</td>\n",
       "      <td>17256.0</td>\n",
       "      <td>536.0</td>\n",
       "      <td>3.106166</td>\n",
       "      <td>0.012170</td>\n",
       "      <td>unlabeled</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>lung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0.561418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            study      sample           prediction  fm_signature_score  \\\n",
       "298997  GSE160794  GSM4879379  alveolar macrophage            0.346716   \n",
       "298998  GSE160794  GSM4879379  alveolar macrophage           -0.290656   \n",
       "298999  GSE160794  GSM4879379  alveolar macrophage            0.695440   \n",
       "299001  GSE160794  GSM4879379  alveolar macrophage           -0.030102   \n",
       "299002  GSE160794  GSM4879379  alveolar macrophage            0.146310   \n",
       "\n",
       "        n_genes_by_counts  total_counts  total_counts_mt  pct_counts_mt  \\\n",
       "298997               3197       21991.0            959.0       4.360875   \n",
       "298998               1167        3657.0            182.0       4.976757   \n",
       "298999               3515       21938.0            598.0       2.725864   \n",
       "299001               2906       14594.0            729.0       4.995203   \n",
       "299002               3367       17256.0            536.0       3.106166   \n",
       "\n",
       "        prediction_nn_dist  data_type  ...  ESC-derived  xenograft  tissue  \\\n",
       "298997            0.007285  unlabeled  ...        False      False    lung   \n",
       "298998            0.011017  unlabeled  ...        False      False    lung   \n",
       "298999            0.021277  unlabeled  ...        False      False    lung   \n",
       "299001            0.011253  unlabeled  ...        False      False    lung   \n",
       "299002            0.012170  unlabeled  ...        False      False    lung   \n",
       "\n",
       "        cell_line_cleaned  disease FACS_sort sex_cleaned author_label  \\\n",
       "298997                NaN      NaN       NaN         NaN          NaN   \n",
       "298998                NaN      NaN       NaN         NaN          NaN   \n",
       "298999                NaN      NaN       NaN         NaN          NaN   \n",
       "299001                NaN      NaN       NaN         NaN          NaN   \n",
       "299002                NaN      NaN       NaN         NaN          NaN   \n",
       "\n",
       "       celltype_index       MS1  \n",
       "298997              0  0.460732  \n",
       "298998              1  0.703938  \n",
       "298999              2  0.433652  \n",
       "299001              3  0.485425  \n",
       "299002              4  0.561418  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_meta.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38abb391-5efa-47a1-aa29-47fd56a2f896",
   "metadata": {},
   "source": [
    "### Analyze Scores\n",
    "\n",
    "Load metadata into \"Meta\" object to perform gene signature analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae7c8666-5596-4661-86dd-34b450f9bc73",
   "metadata": {},
   "source": [
    "#### Load and Filter Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "992f57b2-a1a2-4ea8-ba43-09d89a05b94f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create a Meta object to manipulate the dataframe and perform standardized actions\n",
    "momac_meta = Meta(big_meta)\n",
    "## Subset to high quality cells (SCimilarity prediction dist <0.02)\n",
    "momac_meta.subset_hq()\n",
    "## Subset to only in vivo cells\n",
    "momac_meta.subset_invivo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b1ebb967-2ec0-43d5-9003-98d93c644fad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing 2338447 monocytes and macrophages from 3443 samples in 244 studies\n"
     ]
    }
   ],
   "source": [
    "ncell = momac_meta.df.shape[0]\n",
    "nsamp = momac_meta.df[\"sample\"].nunique()\n",
    "nstudy = momac_meta.df[\"study\"].nunique()\n",
    "print(\n",
    "    f\"Analyzing {ncell} monocytes and macrophages from {nsamp} samples in {nstudy} studies\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "faa1ccc9-29fe-4869-8643-70dfc69f2fe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Clarify some disease names for downstream figure\n",
    "figure_disname_dict = {\n",
    "    \"atopic eczema\": \"Atopic Dermatitis\",\n",
    "    \"COVID-19;healthy\": \"COVID-19 & Healthy (Mixed)\",\n",
    "    \"Epstein-Barr virus infection;hemophagocytic syndrome\": \"EBV-Associated HLH\",\n",
    "    \"Immune dysregulation-polyendocrinopathy-enteropathy-X-linked syndrome\": \"IPEX\",\n",
    "    \"mucocutaneous lymph node syndrome\": \"Kawasaki Disease\",\n",
    "    \"systemic scleroderma;interstitial lung disease\": \"SSC-ILD\",\n",
    "    \"thrombocytopenia\": \"Severe Fever Thrombocytopenia Syndrome\",\n",
    "}\n",
    "\n",
    "momac_meta.df[\"disease\"] = [\n",
    "    figure_disname_dict.get(x, x) for x in momac_meta.df[\"disease\"]\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ae97d49-64b9-4604-afcb-6e92c38e77c4",
   "metadata": {},
   "source": [
    "#### Define \"Hits\" Based on Cutoff\n",
    "\n",
    "Show multiple options for how to define hits and add to metadata (e.g., 90th percentile, 0.95 quantile, 2 standard deviations above the mean)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "829668e4-a9dc-4d62-8b5c-5d4025ce68ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "hit_col = \"MS1\"\n",
    "## 90th percentile\n",
    "momac_meta.add_hits(\n",
    "    column_name=hit_col,\n",
    "    mode=\"percentile\",\n",
    "    cut_val=90,\n",
    "    hit_type=\"above\",\n",
    "    string_append=\"__hit90p\",\n",
    ")\n",
    "\n",
    "## Above 0.95 quantile\n",
    "# momac_meta.add_hits(\n",
    "#     column_name=hit_col,\n",
    "#     mode=\"quantile\",\n",
    "#     cut_val=0.95,\n",
    "#     hit_type=\"above\",\n",
    "#     string_append=\"__hit95p\",\n",
    "# )\n",
    "\n",
    "## Above set value (2 stdev above mean)\n",
    "# mean = np.mean(momac_meta[hit_col].values)\n",
    "# std = np.std(momac_meta[hit_col].values)\n",
    "# hit_val = mean + (2 * std)\n",
    "# momac_meta.add_hits(\n",
    "#     column_name=hit_col,\n",
    "#     mode=\"value\",\n",
    "#     cut_val=hit_val,\n",
    "#     hit_type=\"above\",\n",
    "#     string_append=\"__2zscore\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f227a83-d598-4052-acbe-e0b00566133c",
   "metadata": {},
   "source": [
    "#### Calculate hit percentage per sample\n",
    "\n",
    "For all the monocytes/macrophages in each sample, calculate the percentage that are hits. Only consider samples with at least 25 monocytes/macrophages and diseases that have at least 3 samples. Then show the top 10 diseases by mean hit percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "73f59c9b-69e1-49b8-b418-5295a7a80049",
   "metadata": {},
   "outputs": [],
   "source": [
    "samphit_df_90 = momac_meta.samphit_df(\n",
    "    cell_min=25, samp_min=3, hit_col=\"MS1__hit90p\", num_dis=10\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7b6eb8-b186-4a72-9f07-2cbdf1ee4cb1",
   "metadata": {},
   "source": [
    "#### Generate sample boxplot (like figure 4a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "960c9d2b-882a-4487-977d-51a04901080d",
   "metadata": {},
   "outputs": [],
   "source": [
    "title = \"MS1-like Cells in SCimilarity Database\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5368a44d-d517-43da-9133-1b7d71e5968b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1150x850 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Meta.samphit_boxplot(\n",
    "    samphit_df_90,\n",
    "    hit_label=\"MS1__hit90p\",\n",
    "    swarm=True,\n",
    "    figsize=(11.5, 8.5),\n",
    "    fe=1.7,\n",
    "    title=title,\n",
    "    dotsize=9,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e98b6563-124d-40c5-a9be-783944f52e01",
   "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": "3.10.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}