{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e82e3ea4",
   "metadata": {},
   "source": [
    ".. note::\n",
    "   To download the tutorial data, use the following commands:\n",
    "\n",
    "   **All tutorial data:**\n",
    "   ```python\n",
    "   from recon.data import fetch_all_tutorial_data\n",
    "   fetch_all_tutorial_data(data_dir='./data')\n",
    "   ```\n",
    "\n",
    "   **Specific file (e.g., RNA data):**\n",
    "   ```python\n",
    "   from recon.data import fetch_tutorial_data\n",
    "   fetch_tutorial_data('perturbation_tuto/rna.h5ad', data_dir='./data')\n",
    "   ```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe1a6b03",
   "metadata": {},
   "source": [
    "# Predicting Treatment Effects with ReCoN\n",
    "\n",
    "This tutorial demonstrates how to use ReCoN to **predict the cell-type-specific effects of a molecular treatment**. You will learn how to:\n",
    "\n",
    "1. Build a multilayer network integrating GRNs and cell-cell communication\n",
    "2. Select a molecule (ligand) to perturb\n",
    "3. Predict direct effects (on receptor-expressing cells) and indirect effects (via cell-cell signaling)\n",
    "4. Compare predicted responses across cell types\n",
    "\n",
    "**Use case**: Given a therapeutic molecule (e.g., a ligand or antibody), predict which genes will be affected in each cell type and identify cell-type-specific responses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "82f33142",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scanpy as sc  # single cell data\n",
    "import pandas as pd  # data manipulation\n",
    "import liana as li  # cell communication\n",
    "import recon  # multilayer and perturbation prediction\n",
    "import recon.data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecc33a94",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6491e08c",
   "metadata": {},
   "source": [
    "ReCoN uses single-cell RNA-seq data to infer cell-cell communication and gene regulatory networks.\n",
    "\n",
    "```{tip}\n",
    "The example data for this tutorial can be downloaded from the [ReCoN repository](https://github.com/cantinilab/ReCoN/tree/main/data/perturbation_tuto).\n",
    "```\n",
    "\n",
    "```{note}\n",
    "**Required packages**\n",
    "\n",
    "This tutorial requires `liana` for cell-cell communication inference:\n",
    "- Install with: `pip install liana`\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec72ad8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "rna = sc.read_h5ad(\"./data/perturbation_tuto/rna.h5ad\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc459eb2",
   "metadata": {},
   "source": [
    "Let's check what cell types are present in this dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "532ef40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['B_cell', 'ILC', 'Macrophage', 'MigDC', 'Monocyte']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rna.obs[\"celltype\"].unique().tolist()[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c63a38",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Build the Multilayer Network\n",
    "\n",
    "ReCoN integrates three main components:\n",
    "1. **Gene Regulatory Networks (GRNs)** - TF → target gene relationships\n",
    "2. **Cell-Cell Communication (CCC)** - Ligand-receptor interactions between cell types\n",
    "3. **Receptor-Gene Links** - How receptors connect to intracellular signaling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44ea69e4",
   "metadata": {},
   "source": [
    "### 1. Import Gene Regulatory Network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e50ab4d",
   "metadata": {},
   "source": [
    "You can either generate GRNs directly with ReCoN or import a previously generated one.\n",
    "\n",
    "```{tip}\n",
    "If you wish to generate GRNs directly with ReCoN, please follow [Tutorial 4: GRN Inference with HuMMuS](4.recon_hummus.ipynb).\n",
    "```\n",
    "\n",
    "```{warning}\n",
    "GRN inference requires a **Python 3.10** conda environment due to CellOracle dependencies. See the [Installation guide](../recon_explained/get_ready.rst) for details.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "66faa151",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Unnamed: 0</th>\n",
       "      <th>target</th>\n",
       "      <th>source</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Pax5</td>\n",
       "      <td>Mbd1_TF</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Pax5</td>\n",
       "      <td>Smad5_TF</td>\n",
       "      <td>0.000092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Pax5</td>\n",
       "      <td>Smad1_TF</td>\n",
       "      <td>0.000092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 target    source    weight\n",
       "0           0   Pax5   Mbd1_TF  0.000095\n",
       "2           2   Pax5  Smad5_TF  0.000092\n",
       "1           1   Pax5  Smad1_TF  0.000092"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grn_path = \"./data/perturbation_tuto/grn.csv\"\n",
    "grn = pd.read_csv(grn_path)\n",
    "grn = grn.sort_values(by=\"weight\", ascending=False)[:500_000]\n",
    "grn[\"source\"] = grn[\"source\"].str.capitalize()\n",
    "grn[\"source\"] = grn[\"source\"] + '_TF'\n",
    "grn[\"target\"] = grn[\"target\"].str.capitalize()\n",
    "grn.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7848ad27",
   "metadata": {},
   "source": [
    "### 2. Compute Cell-Cell Communication"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69012bb7",
   "metadata": {},
   "source": [
    "The cell-cell communication is inferred through LIANA+, an external package dedicated to this task.\n",
    "\n",
    "```{tip}\n",
    "For more information, check the [LIANA+ documentation](https://liana-py.readthedocs.io/en/latest/).\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ecd881a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using resource `mouseconsensus`.\n",
      "Using `.X`!\n",
      "/pasteur/appa/homes/rtrimbou/miniconda3/envs/snakemake/envs/recon-grn/lib/python3.10/site-packages/anndata/_core/anndata.py:430: FutureWarning: The dtype argument is deprecated and will be removed in late 2024.\n",
      "15364 features of mat are empty, they will be removed.\n",
      "Make sure that normalized counts are passed!\n",
      "/pasteur/appa/homes/rtrimbou/miniconda3/envs/snakemake/envs/recon-grn/lib/python3.10/site-packages/liana/method/_pipe_utils/_pre.py:146: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "/pasteur/appa/homes/rtrimbou/miniconda3/envs/snakemake/envs/recon-grn/lib/python3.10/site-packages/liana/method/_pipe_utils/_pre.py:149: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "0.36 of entities in the resource are missing from the data.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating ligand-receptor stats for 1296 samples and 937 features\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:04<00:00, 216.62it/s]\n"
     ]
    }
   ],
   "source": [
    "li.method.cellphonedb(rna, \n",
    "            # NOTE by default the resource uses HUMAN gene symbols\n",
    "            resource_name=\"mouseconsensus\",\n",
    "            expr_prop=0.00,\n",
    "            use_raw=False,\n",
    "            groupby=\"celltype\",\n",
    "            verbose=True, key_added='cpdb_res')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3923e09d",
   "metadata": {},
   "source": [
    "Format the LIANA output for ReCoN (rename columns to standard format):\n",
    "\n",
    "```{note}\n",
    "**Required column names for ReCoN:**\n",
    "- `source`: ligand name\n",
    "- `target`: receptor name\n",
    "- `celltype_source`: cell type producing the ligand\n",
    "- `celltype_target`: cell type expressing the receptor\n",
    "- `weight`: interaction score (e.g., lr_means)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6f535c27",
   "metadata": {},
   "outputs": [],
   "source": [
    "ccc_network = rna.uns[\"cpdb_res\"].copy()\n",
    "ccc_network = ccc_network[[\"ligand\", \"receptor\", \"lr_means\", \"source\", \"target\"]]\n",
    "ccc_network = ccc_network.rename(columns={\n",
    "    \"lr_means\": \"weight\",\n",
    "    \"source\": \"celltype_source\",\n",
    "    \"target\": \"celltype_target\",\n",
    "    \"ligand\": \"source\",\n",
    "    \"receptor\": \"target\"\n",
    "})\n",
    "ccc_network = ccc_network[ccc_network['weight'] != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4c51bbb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weight</th>\n",
       "      <th>celltype_source</th>\n",
       "      <th>celltype_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>406685</th>\n",
       "      <td>App</td>\n",
       "      <td>Cd74</td>\n",
       "      <td>102.485008</td>\n",
       "      <td>cDC2</td>\n",
       "      <td>cDC1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405645</th>\n",
       "      <td>Copa</td>\n",
       "      <td>Cd74</td>\n",
       "      <td>102.370003</td>\n",
       "      <td>cDC1</td>\n",
       "      <td>cDC1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410237</th>\n",
       "      <td>Copa</td>\n",
       "      <td>Cd74</td>\n",
       "      <td>102.366211</td>\n",
       "      <td>eTAC</td>\n",
       "      <td>cDC1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       source target      weight celltype_source celltype_target\n",
       "406685    App   Cd74  102.485008            cDC2            cDC1\n",
       "405645   Copa   Cd74  102.370003            cDC1            cDC1\n",
       "410237   Copa   Cd74  102.366211            eTAC            cDC1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccc_network.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9eb5e85",
   "metadata": {},
   "source": [
    "### 3. Load Receptor-Gene Links\n",
    "\n",
    "These links connect membrane receptors to downstream target genes in the GRN, enabling signal propagation from extracellular to intracellular networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5dfdae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1bg</td>\n",
       "      <td>Abca1</td>\n",
       "      <td>0.005156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1bg</td>\n",
       "      <td>Abcb1a</td>\n",
       "      <td>0.005877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1bg</td>\n",
       "      <td>Abcb1b</td>\n",
       "      <td>0.005877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A1bg</td>\n",
       "      <td>Acsl1</td>\n",
       "      <td>0.005915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A1bg</td>\n",
       "      <td>Adk</td>\n",
       "      <td>0.005092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  source  target    weight\n",
       "2   A1bg   Abca1  0.005156\n",
       "3   A1bg  Abcb1a  0.005877\n",
       "4   A1bg  Abcb1b  0.005877\n",
       "7   A1bg   Acsl1  0.005915\n",
       "8   A1bg     Adk  0.005092"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "receptor_genes = recon.data.load_receptor_genes(\"mouse_receptor_gene_from_NichenetPKN\")\n",
    "# for human, use \"human_receptor_gene_from_NichenetPKN\"\n",
    "\n",
    "genes = np.unique(grn['source'].tolist() + grn['target'].tolist())\n",
    "receptor_genes = receptor_genes[receptor_genes['target'].isin(genes)]\n",
    "receptor_genes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26d14a6c",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Select a Molecule to Perturb\n",
    "\n",
    "For treatment effect prediction, we need to choose a molecule (ligand) to simulate as a treatment. There are several strategies:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c93dc7a1",
   "metadata": {},
   "source": [
    "**Strategy 1**: Choose a receptor with high variance across cell types, then find its ligands.\n",
    "\n",
    "This helps identify molecules that may have cell-type-specific effects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8630d794",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "Cd74     932.779297\n",
       "Cd68     155.465271\n",
       "Itgal    152.500290\n",
       "Name: weight, dtype: float32"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# variance\n",
    "ccc_network.groupby(\"target\")[\"weight\"].var().sort_values(ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff64ed18",
   "metadata": {},
   "source": [
    "Now identify the natural ligands of this receptor. You can either:\n",
    "- Use a **designed molecule** targeting this receptor\n",
    "- Choose one of its **natural ligands** from the cell communication network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "eb03a6f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['App', 'Copa']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ligands = ccc_network[ccc_network[\"target\"]==\"Cd74\"]['source'].unique().tolist()\n",
    "ligands"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a5d4cfc",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Run Treatment Effect Prediction\n",
    "\n",
    "The `multicell_targets` function builds the multilayer network and runs Random Walk with Restart to predict treatment effects.\n",
    "\n",
    "```{note}\n",
    "**Direct vs Indirect Effects**\n",
    "\n",
    "- **Direct effect**: Impact on cells that directly express the receptor for your molecule\n",
    "- **Indirect effect**: Impact propagated through cell-cell communication from directly affected cells to other cell types\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "06cd48f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing celltype 1/6: B_cell\n",
      "Processing celltype 2/6: pDC\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/pasteur/helix/projects/ml4ig_hot/Users/rtrimbou/ReCoN/src/recon/explore/recon.py:122: UserWarning: \n",
      "                No receptor_graph provided,\n",
      "                an empty receptor graph will be created.\n",
      "                \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing celltype 3/6: Macrophage\n",
      "Processing celltype 4/6: NK_cell\n",
      "Processing celltype 5/6: T_cell_CD4\n",
      "Processing celltype 6/6: T_cell_CD8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/pasteur/helix/projects/ml4ig_hot/Users/rtrimbou/ReCoN/src/recon/explore/recon.py:387: UserWarning: The celltypes dictionary was converted toa list of Celltype objects.\n",
      "The keys of the dictionary will be the celltype names.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing intracellular contributions and direct effect...\n",
      "Computing intercellular contributions and indirect effect...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/6 [00:00<?, ?it/s][Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "100%|██████████| 6/6 [00:00<00:00, 31.15it/s]\n",
      "[Parallel(n_jobs=6)]: Done   2 out of   6 | elapsed:  1.1min remaining:  2.3min\n",
      "[Parallel(n_jobs=6)]: Done   3 out of   6 | elapsed:  1.1min remaining:  1.1min\n",
      "[Parallel(n_jobs=6)]: Done   4 out of   6 | elapsed:  1.2min remaining:   35.0s\n",
      "[Parallel(n_jobs=6)]: Done   6 out of   6 | elapsed:  1.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 2s, sys: 5.93 s, total: 1min 8s\n",
      "Wall time: 2min 11s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "direct_effect, indirect_effect = recon.explore.multicell_targets(\n",
    "        seeds=[\"Copa\"],\n",
    "        celltypes=[\"B_cell\", \"pDC\", \"Macrophage\", \"NK_cell\", \"T_cell_CD4\", \"T_cell_CD8\"],\n",
    "        grn=grn,\n",
    "        receptor_grn=receptor_genes,\n",
    "        ccc=ccc_network,\n",
    "        grn_graph_weighted=True,\n",
    "        receptor_grn_graph_weighted=True,\n",
    "        receptor_graph_weighted=False,\n",
    "        cell_communication_graph_weighted=True,\n",
    "        cell_communication_graph_directed=False,\n",
    "        restart_proba=0.6,\n",
    "        extend_seeds=True,\n",
    "        njobs=15\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "756abb81",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Analyze Treatment Effects\n",
    "\n",
    "### View Direct and Indirect Effects\n",
    "\n",
    "**Direct effects**: Genes affected in cells expressing the target receptor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c7e6d00e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>celltype_target</th>\n",
       "      <th>B_cell</th>\n",
       "      <th>pDC</th>\n",
       "      <th>Macrophage</th>\n",
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       "      <th>T_cell_CD4</th>\n",
       "      <th>T_cell_CD8</th>\n",
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       "    <tr>\n",
       "      <th>gene</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>Zzz3</th>\n",
       "      <td>5.568139e-09</td>\n",
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       "      <th>Zzef1</th>\n",
       "      <td>7.910818e-09</td>\n",
       "      <td>1.276460e-08</td>\n",
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       "      <td>2.241151e-09</td>\n",
       "      <td>2.276372e-09</td>\n",
       "      <td>2.427962e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zyx</th>\n",
       "      <td>2.311808e-08</td>\n",
       "      <td>2.538018e-08</td>\n",
       "      <td>2.369134e-08</td>\n",
       "      <td>2.052888e-08</td>\n",
       "      <td>2.055242e-08</td>\n",
       "      <td>2.059275e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zyg11b</th>\n",
       "      <td>1.309827e-09</td>\n",
       "      <td>2.150962e-09</td>\n",
       "      <td>1.419632e-09</td>\n",
       "      <td>3.216471e-10</td>\n",
       "      <td>3.280418e-10</td>\n",
       "      <td>3.516321e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zxdc</th>\n",
       "      <td>1.692286e-09</td>\n",
       "      <td>2.749142e-09</td>\n",
       "      <td>1.834584e-09</td>\n",
       "      <td>4.614275e-10</td>\n",
       "      <td>4.729096e-10</td>\n",
       "      <td>5.125789e-10</td>\n",
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       "celltype_target        B_cell           pDC    Macrophage       NK_cell  \\\n",
       "gene                                                                      \n",
       "Zzz3             5.568139e-09  9.124538e-09  6.006453e-09  1.443905e-09   \n",
       "Zzef1            7.910818e-09  1.276460e-08  8.532004e-09  2.241151e-09   \n",
       "Zyx              2.311808e-08  2.538018e-08  2.369134e-08  2.052888e-08   \n",
       "Zyg11b           1.309827e-09  2.150962e-09  1.419632e-09  3.216471e-10   \n",
       "Zxdc             1.692286e-09  2.749142e-09  1.834584e-09  4.614275e-10   \n",
       "\n",
       "celltype_target    T_cell_CD4    T_cell_CD8  \n",
       "gene                                         \n",
       "Zzz3             1.465908e-09  1.560960e-09  \n",
       "Zzef1            2.276372e-09  2.427962e-09  \n",
       "Zyx              2.055242e-08  2.059275e-08  \n",
       "Zyg11b           3.280418e-10  3.516321e-10  \n",
       "Zxdc             4.729096e-10  5.125789e-10  "
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     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "direct_effect.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9a7bfb02",
   "metadata": {},
   "outputs": [
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       "      <td>1.741090e-14</td>\n",
       "      <td>7.543820e-15</td>\n",
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       "      <td>1.090120e-14</td>\n",
       "      <td>5.014306e-15</td>\n",
       "      <td>3.703540e-13</td>\n",
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       "      <td>9.401747e-15</td>\n",
       "      <td>1.659415e-14</td>\n",
       "      <td>1.279711e-14</td>\n",
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       "      <td>2.322816e-14</td>\n",
       "      <td>9.321181e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.410505e-14</td>\n",
       "      <td>5.392201e-15</td>\n",
       "      <td>4.702954e-13</td>\n",
       "      <td>5.585412e-15</td>\n",
       "      <td>1.191117e-14</td>\n",
       "      <td>2.415251e-14</td>\n",
       "      <td>1.715160e-14</td>\n",
       "      <td>6.699112e-15</td>\n",
       "      <td>5.532836e-15</td>\n",
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       "      <td>2.084081e-14</td>\n",
       "      <td>1.099187e-14</td>\n",
       "      <td>8.041645e-15</td>\n",
       "      <td>1.364838e-14</td>\n",
       "      <td>4.492823e-14</td>\n",
       "      <td>3.131443e-12</td>\n",
       "      <td>7.201589e-14</td>\n",
       "      <td>3.947233e-14</td>\n",
       "      <td>...</td>\n",
       "      <td>2.039161e-14</td>\n",
       "      <td>1.069780e-14</td>\n",
       "      <td>6.427157e-13</td>\n",
       "      <td>1.092050e-14</td>\n",
       "      <td>1.666707e-14</td>\n",
       "      <td>2.710333e-14</td>\n",
       "      <td>2.115135e-14</td>\n",
       "      <td>1.055248e-14</td>\n",
       "      <td>8.195229e-15</td>\n",
       "      <td>1.014019e-12</td>\n",
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       "      <th>Zyg11b</th>\n",
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       "      <td>2.836186e-15</td>\n",
       "      <td>2.123397e-15</td>\n",
       "      <td>2.913058e-15</td>\n",
       "      <td>6.033199e-15</td>\n",
       "      <td>7.885257e-13</td>\n",
       "      <td>7.826252e-15</td>\n",
       "      <td>3.810466e-15</td>\n",
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       "      <td>3.306024e-15</td>\n",
       "      <td>1.499960e-15</td>\n",
       "      <td>2.344538e-13</td>\n",
       "      <td>1.989635e-15</td>\n",
       "      <td>5.479759e-15</td>\n",
       "      <td>8.888517e-15</td>\n",
       "      <td>6.859509e-15</td>\n",
       "      <td>3.435869e-15</td>\n",
       "      <td>2.977849e-15</td>\n",
       "      <td>4.643857e-13</td>\n",
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       "      <td>3.212930e-15</td>\n",
       "      <td>1.931706e-15</td>\n",
       "      <td>1.274738e-15</td>\n",
       "      <td>1.452460e-15</td>\n",
       "      <td>5.061063e-15</td>\n",
       "      <td>1.132353e-12</td>\n",
       "      <td>1.440172e-14</td>\n",
       "      <td>3.223035e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>4.276130e-15</td>\n",
       "      <td>1.979657e-15</td>\n",
       "      <td>2.148038e-13</td>\n",
       "      <td>2.091834e-15</td>\n",
       "      <td>3.257674e-15</td>\n",
       "      <td>5.661517e-15</td>\n",
       "      <td>4.910019e-15</td>\n",
       "      <td>2.249046e-15</td>\n",
       "      <td>2.049784e-15</td>\n",
       "      <td>2.335122e-13</td>\n",
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       "celltype_target        B_cell                                            \\\n",
       "celltype_source        B_cell           pDC    Macrophage       NK_cell   \n",
       "gene                                                                      \n",
       "Zzz3             7.760396e-13  1.363160e-14  1.044374e-14  3.864495e-15   \n",
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       "Zxdc             3.455731e-13  4.689825e-15  3.212930e-15  1.931706e-15   \n",
       "\n",
       "celltype_target                                       pDC                \\\n",
       "celltype_source    T_cell_CD4    T_cell_CD8        B_cell           pDC   \n",
       "gene                                                                      \n",
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       "Zxdc             1.274738e-15  1.452460e-15  5.061063e-15  1.132353e-12   \n",
       "\n",
       "celltype_target                              ...    T_cell_CD4                \\\n",
       "celltype_source    Macrophage       NK_cell  ...    Macrophage       NK_cell   \n",
       "gene                                         ...                               \n",
       "Zzz3             1.741090e-14  7.543820e-15  ...  1.090120e-14  5.014306e-15   \n",
       "Zzef1            2.322816e-14  9.321181e-15  ...  1.410505e-14  5.392201e-15   \n",
       "Zyx              7.201589e-14  3.947233e-14  ...  2.039161e-14  1.069780e-14   \n",
       "Zyg11b           7.826252e-15  3.810466e-15  ...  3.306024e-15  1.499960e-15   \n",
       "Zxdc             1.440172e-14  3.223035e-15  ...  4.276130e-15  1.979657e-15   \n",
       "\n",
       "celltype_target                                T_cell_CD8                \\\n",
       "celltype_source    T_cell_CD4    T_cell_CD8        B_cell           pDC   \n",
       "gene                                                                      \n",
       "Zzz3             3.703540e-13  5.171489e-15  9.401747e-15  1.659415e-14   \n",
       "Zzef1            4.702954e-13  5.585412e-15  1.191117e-14  2.415251e-14   \n",
       "Zyx              6.427157e-13  1.092050e-14  1.666707e-14  2.710333e-14   \n",
       "Zyg11b           2.344538e-13  1.989635e-15  5.479759e-15  8.888517e-15   \n",
       "Zxdc             2.148038e-13  2.091834e-15  3.257674e-15  5.661517e-15   \n",
       "\n",
       "celltype_target                                                          \n",
       "celltype_source    Macrophage       NK_cell    T_cell_CD4    T_cell_CD8  \n",
       "gene                                                                     \n",
       "Zzz3             1.279711e-14  5.898745e-15  5.182911e-15  4.402922e-13  \n",
       "Zzef1            1.715160e-14  6.699112e-15  5.532836e-15  5.562709e-13  \n",
       "Zyx              2.115135e-14  1.055248e-14  8.195229e-15  1.014019e-12  \n",
       "Zyg11b           6.859509e-15  3.435869e-15  2.977849e-15  4.643857e-13  \n",
       "Zxdc             4.910019e-15  2.249046e-15  2.049784e-15  2.335122e-13  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indirect_effect.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5573a3d9",
   "metadata": {},
   "source": [
    "**Indirect effects**: Genes affected through cell-cell communication cascades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "38e01332",
   "metadata": {},
   "outputs": [
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       "      <td>0.000037</td>\n",
       "      <td>0.000036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zzef1</th>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zyx</th>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.000138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zyg11b</th>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zxdc</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "celltype_target    B_cell       pDC  Macrophage   NK_cell  T_cell_CD4  \\\n",
       "gene                                                                    \n",
       "Zzz3             0.000036  0.000030    0.000032  0.000036    0.000037   \n",
       "Zzef1            0.000048  0.000043    0.000044  0.000051    0.000049   \n",
       "Zyx              0.000085  0.000078    0.000071  0.000125    0.000129   \n",
       "Zyg11b           0.000022  0.000017    0.000017  0.000028    0.000020   \n",
       "Zxdc             0.000015  0.000023    0.000022  0.000021    0.000020   \n",
       "\n",
       "celltype_target  T_cell_CD8  \n",
       "gene                         \n",
       "Zzz3               0.000036  \n",
       "Zzef1              0.000047  \n",
       "Zyx                0.000138  \n",
       "Zyg11b             0.000032  \n",
       "Zxdc               0.000018  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_effect = recon.explore.combine_effects(direct_effect, indirect_effect, alpha=0.8)\n",
    "total_effect.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f373791",
   "metadata": {},
   "source": [
    "### Combine Direct and Indirect Effects\n",
    "\n",
    "The `combine_effects` function merges direct and indirect effects using a weighting parameter α:\n",
    "\n",
    "$$\\text{Total Effect} = \\alpha \\times \\text{Direct} + (1 - \\alpha) \\times \\text{Indirect}$$\n",
    "\n",
    "```{tip}\n",
    "The default **α = 0.8** gives more weight to direct effects, which has been empirically validated. You can adjust this based on your biological question.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6544d3e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='B_cell', ylabel='pDC'>"
      ]
     },
     "execution_count": 43,
     "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": [
    "%matplotlib inline\n",
    "total_effect.plot.scatter(x='B_cell', y='pDC')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14b6d2a4",
   "metadata": {},
   "source": [
    "### Visualize Cell Type Correlations\n",
    "\n",
    "Scatter plots showing correlation of predicted effects between cell types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7d766fa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='B_cell', ylabel='Macrophage'>"
      ]
     },
     "execution_count": 44,
     "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": [
    "%matplotlib inline\n",
    "total_effect.plot.scatter(x='B_cell', y='Macrophage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8cef1fba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='pDC', ylabel='Macrophage'>"
      ]
     },
     "execution_count": 45,
     "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": [
    "%matplotlib inline\n",
    "total_effect.plot.scatter(x='pDC', y='Macrophage')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a73304b1",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Compare Cell-Type-Specific Responses\n",
    "\n",
    "A key application of ReCoN is identifying **differential responses** between cell types. This helps understand:\n",
    "- Which cell types are most affected by a treatment\n",
    "- Which genes drive cell-type-specific responses\n",
    "- Potential off-target effects on non-target cell populations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7c08bf2",
   "metadata": {},
   "source": [
    "### T cells CD4+ vs B cells\n",
    "\n",
    "Compare predicted treatment response between T helper cells and B cells:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3c3fb366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gene\n",
       "Fos       0.004969\n",
       "Map4k1    0.004299\n",
       "Ctnnb1    0.003570\n",
       "Myc       0.003014\n",
       "Ccnd1     0.002946\n",
       "Arc       0.002888\n",
       "Sucla2    0.002392\n",
       "Cr2       0.002220\n",
       "Ifng      0.002124\n",
       "Rela      0.001742\n",
       "Hmox1     0.001735\n",
       "Il4       0.001699\n",
       "Cdkn1a    0.001655\n",
       "Chuk      0.001628\n",
       "Nfkbia    0.001590\n",
       "Bcl2l1    0.001563\n",
       "Mapk9     0.001519\n",
       "Il1b      0.001379\n",
       "Egr1      0.001368\n",
       "Vegfa     0.001333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(total_effect[\"T_cell_CD4\"] - total_effect[\"B_cell\"]).sort_values(ascending=False)[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d51012e2",
   "metadata": {},
   "source": [
    "```{note}\n",
    "**Biological interpretation**\n",
    "\n",
    "Genes with higher scores in T cells CD4+ include known markers like **Cd40lg**, **Cxcr4**, and **Cxcr6** - confirming that ReCoN captures biologically meaningful cell-type-specific signatures.\n",
    "```\n",
    "\n",
    "Visualize with a comparison plot highlighting the most differential genes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f32aaea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recon.plot.plot_celltype_comparison(total_effect, \"T_cell_CD4\", \"B_cell\", quantile=0.998)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9c1cc86",
   "metadata": {},
   "source": [
    "### B cells vs T cells CD4+\n",
    "\n",
    "Now look at genes with higher scores in B cells:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8413f631",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gene\n",
       "Cd86        0.004334\n",
       "Dusp4       0.003410\n",
       "Il1r1       0.001595\n",
       "Ggh         0.001451\n",
       "Ebi3        0.001430\n",
       "Zhx2        0.001403\n",
       "Il27        0.001391\n",
       "Sit1        0.001321\n",
       "Zfp219      0.001284\n",
       "Actr1b      0.001268\n",
       "Begain      0.001242\n",
       "Smpd3       0.001242\n",
       "Slc25a44    0.001241\n",
       "Aptx        0.001230\n",
       "Lipc        0.001223\n",
       "Asb1        0.001221\n",
       "Epcam       0.001220\n",
       "Pmvk        0.001212\n",
       "Mplkip      0.001203\n",
       "Tdgf1       0.001192\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(total_effect[\"B_cell\"] - total_effect[\"T_cell_CD4\"]).sort_values(ascending=False)[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4521a797",
   "metadata": {},
   "source": [
    "```{note}\n",
    "**Biological interpretation**\n",
    "\n",
    "Genes with higher expression in B cells include:\n",
    "- **Cd86**: Marker of antigen presenting cells (including B cells)\n",
    "- **Ccr6**: Classical marker of B cells, important for germinal center formation\n",
    "\n",
    "_References:_\n",
    "1. _Suan D et al. (2017) Immunity 47(6):1142-1153. doi:10.1016/j.immuni.2017.11.022_\n",
    "2. _Lee AYS & Körner H. (2019) Immunobiology 224(3):449-454. doi:10.1016/j.imbio.2019.01.005_\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b991aacc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recon.plot.plot_celltype_comparison(total_effect, \"B_cell\", \"T_cell_CD4\", quantile=0.999)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d939b8f",
   "metadata": {},
   "source": [
    "### Macrophages vs Other Cell Types\n",
    "\n",
    "Compare macrophage response to the average of all other cell types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "314e45e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gene\n",
       "Tnf         0.001129\n",
       "Ebi3        0.000747\n",
       "Il1r1       0.000734\n",
       "Id3         0.000691\n",
       "Ggh         0.000684\n",
       "Timp2       0.000655\n",
       "Il27        0.000654\n",
       "Zhx2        0.000635\n",
       "Sit1        0.000621\n",
       "Arhgap31    0.000620\n",
       "Actr1b      0.000598\n",
       "Slc25a44    0.000597\n",
       "Zfp219      0.000596\n",
       "Begain      0.000584\n",
       "Smpd3       0.000579\n",
       "Aptx        0.000579\n",
       "Lipc        0.000573\n",
       "Pmvk        0.000567\n",
       "Mplkip      0.000567\n",
       "Asb1        0.000565\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(total_effect[\"Macrophage\"] - total_effect.loc[:, ~total_effect.columns.isin([\"Macrophage\"])].mean(1)).sort_values(ascending=False)[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d132ce45",
   "metadata": {},
   "source": [
    "```{note}\n",
    "**Biological interpretation**\n",
    "\n",
    "Macrophage-specific genes include markers of innate immunity and tissue remodeling:\n",
    "- **Tnf**: Pro-inflammatory cytokine\n",
    "- **Tgfb1**: Key regulator of tissue remodeling\n",
    "- **Mmp9**: Matrix metalloproteinase involved in extracellular matrix degradation\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "99127786",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_effect[\"average_no_macrophage\"] = total_effect.loc[:, ~total_effect.columns.isin([\"Macrophage\"])].mean(axis=1)\n",
    "recon.plot.plot_celltype_comparison(total_effect, \"Macrophage\", \"average_no_macrophage\", quantile=0.998)\n",
    "# delete to not include it by mistake later\n",
    "del total_effect[\"average_no_macrophage\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf538ef6",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Gene Ranking Analysis\n",
    "\n",
    "### Compare Gene Ranks Across Cell Types\n",
    "\n",
    "We can also compare the **rank** of each gene across different cell types to identify genes that are consistently high or show cell-type-specific rankings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b8ec0ae8",
   "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>celltype_target</th>\n",
       "      <th>B_cell</th>\n",
       "      <th>pDC</th>\n",
       "      <th>Macrophage</th>\n",
       "      <th>NK_cell</th>\n",
       "      <th>T_cell_CD4</th>\n",
       "      <th>T_cell_CD8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ms4a4d</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hist1h2ac</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ifi27l2a</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3galt2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hus1b</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kantr</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pcdhga6</th>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sugp1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trim65</th>\n",
       "      <td>13.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tarm1</th>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "celltype_target  B_cell   pDC  Macrophage  NK_cell  T_cell_CD4  T_cell_CD8\n",
       "gene                                                                      \n",
       "Ms4a4d              1.0   1.0         1.0      1.0         1.0         1.0\n",
       "Hist1h2ac           2.0   2.0         2.0      2.0         2.0         2.0\n",
       "Ifi27l2a            3.0   3.0         3.0      3.0         3.0         3.0\n",
       "A3galt2             4.0   4.0         4.0      4.0         4.0         4.0\n",
       "Hus1b               5.0   6.0         6.0      5.0         7.0         5.0\n",
       "Kantr               6.0   5.0         5.0      6.0         5.0         6.0\n",
       "Pcdhga6             9.0   7.0         7.0      7.0         6.0         8.0\n",
       "Sugp1               7.0   9.0         8.0      8.0        10.0         7.0\n",
       "Trim65             13.0  21.0        12.0      9.0        14.0         9.0\n",
       "Tarm1              12.0  11.0        10.0     10.0         8.0        11.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_effect.rank().sort_values(by=\"NK_cell\")[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "387475a2",
   "metadata": {},
   "source": [
    "```{tip}\n",
    "The top-ranked genes are often generic markers and metabolism-related genes that appear in all cell types. For biological interpretation, it's usually more informative to focus on **known markers** or **genes of interest** specific to your system.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b63df0",
   "metadata": {},
   "source": [
    "### Focus on Known Cell Type Markers\n",
    "\n",
    "```{warning}\n",
    "**Interpretation caveat**\n",
    "\n",
    "These predictions are for **one specific ligand** (Copa). The results don't represent the actual expression state of your cells - they represent the predicted **response** to this specific perturbation. You won't necessarily retrieve all cell-type markers.\n",
    "```\n",
    "\n",
    "Let's check how known immune cell markers rank in each cell type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e98f489d",
   "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>celltype_target</th>\n",
       "      <th>B_cell</th>\n",
       "      <th>pDC</th>\n",
       "      <th>Macrophage</th>\n",
       "      <th>NK_cell</th>\n",
       "      <th>T_cell_CD4</th>\n",
       "      <th>T_cell_CD8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cd4</th>\n",
       "      <td>2391.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2480.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cd19</th>\n",
       "      <td>492.0</td>\n",
       "      <td>814.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>398.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>407.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S100a8</th>\n",
       "      <td>1304.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>1499.0</td>\n",
       "      <td>641.0</td>\n",
       "      <td>787.0</td>\n",
       "      <td>639.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Csf1r</th>\n",
       "      <td>1419.0</td>\n",
       "      <td>1781.0</td>\n",
       "      <td>586.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>655.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S100a9</th>\n",
       "      <td>1503.0</td>\n",
       "      <td>1587.0</td>\n",
       "      <td>1557.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cd79a</th>\n",
       "      <td>876.0</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>1142.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>757.0</td>\n",
       "      <td>734.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sdc1</th>\n",
       "      <td>1505.0</td>\n",
       "      <td>1414.0</td>\n",
       "      <td>1439.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>832.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Itgax</th>\n",
       "      <td>1663.0</td>\n",
       "      <td>1948.0</td>\n",
       "      <td>1903.0</td>\n",
       "      <td>1168.0</td>\n",
       "      <td>1108.0</td>\n",
       "      <td>1095.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ptprc</th>\n",
       "      <td>1805.0</td>\n",
       "      <td>1935.0</td>\n",
       "      <td>1919.0</td>\n",
       "      <td>1826.0</td>\n",
       "      <td>1766.0</td>\n",
       "      <td>1606.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cd3e</th>\n",
       "      <td>3690.0</td>\n",
       "      <td>3482.0</td>\n",
       "      <td>3183.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>2100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Klrk1</th>\n",
       "      <td>547.0</td>\n",
       "      <td>503.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>2221.0</td>\n",
       "      <td>2104.0</td>\n",
       "      <td>2156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lyz2</th>\n",
       "      <td>4595.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>4159.0</td>\n",
       "      <td>5039.0</td>\n",
       "      <td>4668.0</td>\n",
       "      <td>5129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zbtb46</th>\n",
       "      <td>5398.0</td>\n",
       "      <td>5408.0</td>\n",
       "      <td>5093.0</td>\n",
       "      <td>5368.0</td>\n",
       "      <td>5522.0</td>\n",
       "      <td>5602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms4a1</th>\n",
       "      <td>5631.0</td>\n",
       "      <td>6793.0</td>\n",
       "      <td>7326.0</td>\n",
       "      <td>5512.0</td>\n",
       "      <td>4492.0</td>\n",
       "      <td>5131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cd8a</th>\n",
       "      <td>4227.0</td>\n",
       "      <td>6156.0</td>\n",
       "      <td>5033.0</td>\n",
       "      <td>5599.0</td>\n",
       "      <td>5480.0</td>\n",
       "      <td>4063.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ncr1</th>\n",
       "      <td>7048.0</td>\n",
       "      <td>7893.0</td>\n",
       "      <td>7954.0</td>\n",
       "      <td>6411.0</td>\n",
       "      <td>6168.0</td>\n",
       "      <td>6597.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "celltype_target  B_cell     pDC  Macrophage  NK_cell  T_cell_CD4  T_cell_CD8\n",
       "gene                                                                        \n",
       "Cd4              2391.0   274.0       273.0     53.0      2480.0        60.0\n",
       "Cd19              492.0   814.0       797.0    398.0       365.0       407.0\n",
       "S100a8           1304.0  1550.0      1499.0    641.0       787.0       639.0\n",
       "Csf1r            1419.0  1781.0       586.0    669.0       725.0       655.0\n",
       "S100a9           1503.0  1587.0      1557.0    696.0       710.0       710.0\n",
       "Cd79a             876.0  1049.0      1142.0    750.0       757.0       734.0\n",
       "Sdc1             1505.0  1414.0      1439.0    828.0       709.0       832.0\n",
       "Itgax            1663.0  1948.0      1903.0   1168.0      1108.0      1095.0\n",
       "Ptprc            1805.0  1935.0      1919.0   1826.0      1766.0      1606.0\n",
       "Cd3e             3690.0  3482.0      3183.0   1988.0      1951.0      2100.0\n",
       "Klrk1             547.0   503.0       549.0   2221.0      2104.0      2156.0\n",
       "Lyz2             4595.0  3840.0      4159.0   5039.0      4668.0      5129.0\n",
       "Zbtb46           5398.0  5408.0      5093.0   5368.0      5522.0      5602.0\n",
       "Ms4a1            5631.0  6793.0      7326.0   5512.0      4492.0      5131.0\n",
       "Cd8a             4227.0  6156.0      5033.0   5599.0      5480.0      4063.0\n",
       "Ncr1             7048.0  7893.0      7954.0   6411.0      6168.0      6597.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markers = pd.Series([\n",
    "    \"Ptprc\",   # pan-leukocyte (CD45)\n",
    "    \"Cd3e\", \"Trac\",              # T cells\n",
    "    \"Cd4\", \"Cd8a\",               # helper vs cytotoxic T\n",
    "    \"Ncr1\", \"Klrk1\",             # NK cells\n",
    "    \"Ms4a1\", \"Cd19\", \"Cd79a\",    # B cells\n",
    "    \"Sdc1\",                      # plasmablasts/plasma cells\n",
    "    \"Lyz2\", \"Adgre1\", \"Csf1r\",   # monocytes/macrophages\n",
    "    \"Itgax\", \"Zbtb46\",           # conventional dendritic cells\n",
    "    \"Siglech\",                   # plasmacytoid dendritic cells\n",
    "    \"Ly6g\", \"S100a8\", \"S100a9\"   # neutrophils\n",
    "])\n",
    "\n",
    "markers = markers[markers.isin(total_effect.index).values]\n",
    "\n",
    "total_effect.rank(ascending=False).loc[markers].sort_values(by=\"NK_cell\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7481355",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Summary\n",
    "\n",
    "In this tutorial, you learned how to:\n",
    "\n",
    "1. **Build a multilayer network** integrating GRNs, cell-cell communication, and receptor-gene links\n",
    "2. **Select a molecule** to simulate as a treatment based on receptor variance\n",
    "3. **Predict treatment effects** using `multicell_targets()` to get direct and indirect effects\n",
    "4. **Combine effects** with the α parameter (default 0.8 for direct-weighted)\n",
    "5. **Compare cell-type responses** to identify differential effects and cell-type-specific genes\n",
    "\n",
    "```{tip}\n",
    "**Next steps**\n",
    "\n",
    "- [Tutorial 2](2.recon_multicellular_coordination.ipynb): Explore multicellular coordination around a pathway\n",
    "- [Tutorial 3](3.recon_molecular_cascades.ipynb): Visualize molecular cascades with Sankey diagrams\n",
    "- [Tutorial 4](4.recon_hummus.ipynb): Generate your own GRNs with HuMMuS\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e703be9",
   "metadata": {},
   "source": []
  }
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