{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9631bf4f",
   "metadata": {},
   "source": [
    "# Multi-dataset: Gene-Gene Covariation Analysis\n",
    "\n",
    "This example demonstrates how to identify **cross-cell gene-gene covariation** across\n",
    "multiple FOVs.\n",
    "\n",
    "The multi-FOV version pools anchor–neighbor cell pairs across all FOVs within a condition,\n",
    "increasing statistical power for detecting spatially-specific gene correlations.\n",
    "\n",
    "**Dataset:** CZI Kidney — normal vs autosomal dominant tubulointerstitial kidney disease (ADTKD) (spatial transcriptomics), availabel at https://cellxgene.cziscience.com/collections/8e880741-bf9a-4c8e-9227-934204631d2a \n",
    "\n",
    "**Key API:**\n",
    "- `spatial_query_multi.compute_gene_gene_correlation` — pools all non-anchor motif cell types together\n",
    "- `spatial_query_multi.compute_gene_gene_correlation_by_type` — tests each non-anchor cell type separately\n",
    "\n",
    "> **Tip:** For whole-transcriptomic data like this kidney dataset, we'd suggest pre-selecting highly variable\n",
    "> genes via the `genes` parameter to improve speed and reduce multiple-testing burden."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cf04205",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "26d6389e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import os\n",
    "import anndata as ad\n",
    "import scanpy as sc\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from SpatialQuery import spatial_query_multi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e91b50ec",
   "metadata": {},
   "source": [
    "## Load Data & Initialize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "837e0d9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 84 FOVs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 12906 × 17811\n",
       "    obs: 'assay_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'is_primary_data', 'organism_ontology_term_id', 'sample', 'tissue_ontology_term_id', 'disease_state', 'sex_ontology_term_id', 'genotype', 'development_stage_ontology_term_id', 'author_cell_type', 'cell_type_ontology_term_id', 'disease_ontology_term_id', 'donor_id', 'suspension_type', 'tissue_type', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid'\n",
       "    var: 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length'\n",
       "    uns: 'citation', 'schema_reference', 'schema_version', 'title'\n",
       "    obsm: 'X_spatial'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA_DIR = \"../data/CZI_kidney\"\n",
    "\n",
    "data_files = os.listdir(DATA_DIR)\n",
    "adatas = [ad.read_h5ad(os.path.join(DATA_DIR, f)) for f in data_files]\n",
    "\n",
    "print(f\"Loaded {len(adatas)} FOVs\")\n",
    "adatas[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "60c8336b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create condition labels and filter out mitochondrial / unannotated genes\n",
    "for i, adata in enumerate(adatas):\n",
    "    adata.obs['disease_state_genotype'] = (\n",
    "        adata.obs['disease_state'].astype(str) + '_' + adata.obs['genotype'].astype(str)\n",
    "    )\n",
    "    adata.var_names = adata.var['feature_name'].tolist()\n",
    "    adata = adata[:, ~adata.var_names.str.startswith('mt-')]\n",
    "    adata = adata[:, ~adata.var_names.str.endswith('Rik')]\n",
    "    adata = adata[:, ~adata.var_names.str.startswith('Gm')]\n",
    "    adatas[i] = adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c3336a75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map verbose condition names to short labels\n",
    "dataset_mapping = {\n",
    "    'early diabetic kidney disease_BTBR-ob/ob': 'DKD_BTBR-ob/ob',\n",
    "    'autosomal dominant tubulointersital kidney disease (ADTKD)_UMOD-KI/KI': 'ADTKD_UMOD-KI/KI',\n",
    "    'control_BTBR-wt/wt': 'control_BTBR-WT/WT',\n",
    "    'control_UMOD-WT/WT': 'control_UMOD-WT/WT',\n",
    "}\n",
    "\n",
    "dataset_name_col = 'disease_state_genotype'\n",
    "datasets = [adata.obs[dataset_name_col].unique()[0] for adata in adatas]\n",
    "dataset_names = [dataset_mapping[d] for d in datasets]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "83cbff14",
   "metadata": {},
   "outputs": [],
   "source": [
    "spatial_key = \"X_spatial\"\n",
    "label_key = \"cell_type\"\n",
    "feature_name = 'feature_name'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3c30d677",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replacing _ with hyphen in DKD_BTBR-ob/ob.\n",
      "Replacing _ with hyphen in DKD_BTBR-ob/ob.\n",
      "Replacing _ with hyphen in control_BTBR-WT/WT.\n",
      "Replacing _ with hyphen in control_UMOD-WT/WT.\n",
      "Replacing _ with hyphen in ADTKD_UMOD-KI/KI.\n",
      "Replacing _ with hyphen in ADTKD_UMOD-KI/KI.\n",
      "Replacing _ with hyphen in ADTKD_UMOD-KI/KI.\n",
      "Replacing _ with hyphen in control_BTBR-WT/WT.\n",
      "Replacing _ with hyphen in control_BTBR-WT/WT.\n",
      "Replacing _ with hyphen in DKD_BTBR-ob/ob.\n",
      "...(492 lines omitted)...\n",
      "build_gene_index is False. Using adata.X for gene expression analysis.\n",
      "Log normalizing the expression data... If data is already log normalized, please set if_lognorm to False.\n"
     ]
    }
   ],
   "source": [
    "spm = spatial_query_multi(\n",
    "    adatas=adatas,\n",
    "    datasets=dataset_names,\n",
    "    spatial_key=spatial_key,\n",
    "    label_key=label_key,\n",
    "    feature_name=feature_name,\n",
    "    build_gene_index=False,\n",
    "    if_lognorm=True,\n",
    "    if_normalize_spatial_coord=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4c484be",
   "metadata": {},
   "source": [
    "### Select Highly Variable Genes\n",
    "\n",
    "For whole-transcriptomic data, pre-selecting highly variable genes (HVGs) speeds up\n",
    "computation and focuses the analysis on the most informative features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "710d7341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DKD-BTBR-ob/ob',\n",
       " 'ADTKD-UMOD-KI/KI',\n",
       " 'control-BTBR-WT/WT',\n",
       " 'control-UMOD-WT/WT']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_ds_names = list(set(s.dataset.split('_')[0] for s in spm.spatial_queries))\n",
    "valid_ds_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9b730fcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DKD-BTBR-ob/ob: (613317, 13586)\n",
      "ADTKD-UMOD-KI/KI: (501021, 14390)\n",
      "control-BTBR-WT/WT: (550106, 13751)\n",
      "control-UMOD-WT/WT: (336157, 14271)\n"
     ]
    }
   ],
   "source": [
    "# Select top 3000 HVGs per condition and take the union\n",
    "tt1 = np.array([f.replace('_','-') for f in dataset_names])\n",
    "selected_genes = {}\n",
    "tt2 = []\n",
    "for adata in adatas:\n",
    "    adata.var_names = adata.var[feature_name]\n",
    "    tt2.append(adata)\n",
    "\n",
    "for ds in valid_ds_names:\n",
    "    mask = np.where(tt1 == ds)[0]\n",
    "    adata_sub = ad.concat([tt2[i].copy() for i in mask], join='inner')\n",
    "    print(f\"{ds}: {adata_sub.shape}\")\n",
    "    sc.pp.normalize_total(adata_sub)\n",
    "    sc.pp.log1p(adata_sub)\n",
    "    sc.pp.highly_variable_genes(adata_sub, n_top_genes=3000)\n",
    "    selected_genes[ds] = adata_sub.var[adata_sub.var['highly_variable']].index.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bcc753a",
   "metadata": {},
   "source": [
    "## Define Anchor and Motif\n",
    "\n",
    "We use **macrophage** as the anchor cell type. The motif includes **fibroblast** and **thick ascending limb epithelial cell**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e269046",
   "metadata": {},
   "outputs": [],
   "source": [
    "anchor_ct = \"macrophage\"\n",
    "motif = ['kidney interstitial fibroblast', 'kidney loop of Henle thick ascending limb epithelial cell']\n",
    "max_dist = 5\n",
    "ds_control = 'control-UMOD-WT/WT'\n",
    "ds_case = 'ADTKD-UMOD-KI/KI'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c7182f",
   "metadata": {},
   "source": [
    "## Method 1: Pooled Covariation (`compute_gene_gene_correlation`)\n",
    "\n",
    "`compute_gene_gene_correlation` **pools all non-anchor cell types** in the motif into a\n",
    "single neighbor group before computing correlations across FOVs. It performs two tests:\n",
    "\n",
    "1. **Test 1 (spatial specificity):** correlation in motif+ anchor–neighbor pairs vs randomly paired cells\n",
    "2. **Test 2 (motif specificity):** correlation in motif+ pairs vs motif− anchor–neighbor pairs\n",
    "\n",
    "**Key parameters:**\n",
    "\n",
    "| Parameter | Description |\n",
    "|-----------|-------------|\n",
    "| `ct` | Anchor cell type |\n",
    "| `motif` | List of neighbor cell types forming the motif |\n",
    "| `dataset` | Condition name(s) to restrict the analysis to |\n",
    "| `max_dist` | Neighborhood radius (use either `max_dist` or `k`) |\n",
    "| `k` | Number of nearest neighbors (use either `max_dist` or `k`) |\n",
    "| `genes` | List of genes to analyze. If `None`, uses all genes |\n",
    "| `min_nonzero` | Minimum non-zero expression values required to include a gene |\n",
    "| `alpha` | FDR significance threshold (default: 0.05) |\n",
    "\n",
    "**Key output columns:**\n",
    "\n",
    "| Column | Description |\n",
    "|--------|-------------|\n",
    "| `gene_center` | Gene expressed in the anchor cell |\n",
    "| `gene_motif` | Gene expressed in the neighbor cell |\n",
    "| `corr_neighbor` | Correlation between motif+ anchor–neighbor pairs |\n",
    "| `corr_non_neighbor` | Correlation between randomly paired cells (Test 1 baseline) |\n",
    "| `corr_center_no_motif` | Correlation for motif− anchors (Test 2 baseline) |\n",
    "| `combined_score` | Combined effect size from both tests |\n",
    "| `if_significant` | Whether the pair passes both FDR-corrected tests |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "95875277",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing covarying genes using expression data ...\n",
      "Gene coverage: 14390 genes in all FOVs, 18795 genes total (union)\n",
      "  -> 4405 genes present in subset of FOVs (will use available data)\n",
      "Analyzing 3000 genes across 24 FOVs\n",
      "\n",
      "================================================================================\n",
      "Step 1: Computing and accumulating statistics across FOVs\n",
      "================================================================================\n",
      "\n",
      "--- Processing FOV 1/24: ADTKD-UMOD-KI/KI_0 ---\n",
      "...(177 lines omitted)...\n",
      "Total gene pairs tested: 9000000\n",
      "Significant covarying pairs (pooled): 296\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_center</th>\n",
       "      <th>gene_motif</th>\n",
       "      <th>corr_neighbor</th>\n",
       "      <th>corr_non_neighbor</th>\n",
       "      <th>p_value_test1</th>\n",
       "      <th>delta_corr_test1</th>\n",
       "      <th>corr_center_no_motif</th>\n",
       "      <th>p_value_test2</th>\n",
       "      <th>delta_corr_test2</th>\n",
       "      <th>combined_score</th>\n",
       "      <th>adj-pval-test1</th>\n",
       "      <th>adj-pval-test2</th>\n",
       "      <th>if_significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wif1</td>\n",
       "      <td>Wif1</td>\n",
       "      <td>0.312311</td>\n",
       "      <td>-0.011133</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.323444</td>\n",
       "      <td>-0.000243</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.312554</td>\n",
       "      <td>94.746418</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Armc12</td>\n",
       "      <td>Wif1</td>\n",
       "      <td>0.300108</td>\n",
       "      <td>-0.010945</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.311053</td>\n",
       "      <td>-0.000126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300234</td>\n",
       "      <td>91.043878</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grid1</td>\n",
       "      <td>Grin3a</td>\n",
       "      <td>0.259830</td>\n",
       "      <td>0.000530</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.259300</td>\n",
       "      <td>-0.000535</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.260365</td>\n",
       "      <td>78.013763</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hpdl</td>\n",
       "      <td>Dnah3</td>\n",
       "      <td>0.236931</td>\n",
       "      <td>-0.001030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.237962</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.236859</td>\n",
       "      <td>71.156931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grid1</td>\n",
       "      <td>Myom2</td>\n",
       "      <td>0.221014</td>\n",
       "      <td>-0.000439</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.221453</td>\n",
       "      <td>0.000255</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.220759</td>\n",
       "      <td>66.290110</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cercam</td>\n",
       "      <td>Rergl</td>\n",
       "      <td>0.212219</td>\n",
       "      <td>-0.000659</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.212879</td>\n",
       "      <td>0.004272</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.207947</td>\n",
       "      <td>62.828013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kap</td>\n",
       "      <td>Kap</td>\n",
       "      <td>0.368871</td>\n",
       "      <td>-0.012893</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.381763</td>\n",
       "      <td>0.251811</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.117059</td>\n",
       "      <td>58.941187</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cyp2d12</td>\n",
       "      <td>Hmx2</td>\n",
       "      <td>0.192851</td>\n",
       "      <td>-0.000510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.193361</td>\n",
       "      <td>0.001287</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.191563</td>\n",
       "      <td>57.630835</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ptprr</td>\n",
       "      <td>Tmc7</td>\n",
       "      <td>0.191837</td>\n",
       "      <td>-0.000012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.191850</td>\n",
       "      <td>0.003037</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.188800</td>\n",
       "      <td>56.914508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lypd1</td>\n",
       "      <td>Notumos</td>\n",
       "      <td>0.180410</td>\n",
       "      <td>-0.000194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.180604</td>\n",
       "      <td>-0.000311</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.180721</td>\n",
       "      <td>54.205766</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gene_center gene_motif  corr_neighbor  corr_non_neighbor  p_value_test1  \\\n",
       "0        Wif1       Wif1       0.312311          -0.011133            0.0   \n",
       "1      Armc12       Wif1       0.300108          -0.010945            0.0   \n",
       "2       Grid1     Grin3a       0.259830           0.000530            0.0   \n",
       "3        Hpdl      Dnah3       0.236931          -0.001030            0.0   \n",
       "4       Grid1      Myom2       0.221014          -0.000439            0.0   \n",
       "5      Cercam      Rergl       0.212219          -0.000659            0.0   \n",
       "6         Kap        Kap       0.368871          -0.012893            0.0   \n",
       "7     Cyp2d12       Hmx2       0.192851          -0.000510            0.0   \n",
       "8       Ptprr       Tmc7       0.191837          -0.000012            0.0   \n",
       "...(23 lines omitted)...\n",
       "8       56.914508             0.0             0.0            True  \n",
       "9       54.205766             0.0             0.0            True  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covarying_pooled = spm.compute_gene_gene_correlation(\n",
    "    ct=anchor_ct,\n",
    "    motif=motif,\n",
    "    dataset=ds_case,\n",
    "    max_dist=max_dist,\n",
    "    genes=selected_genes[ds_case],\n",
    "    alpha=0.05,\n",
    ")\n",
    "\n",
    "covarying_pooled_sig = covarying_pooled[covarying_pooled[\"if_significant\"]].copy()\n",
    "print(f\"Total gene pairs tested: {len(covarying_pooled)}\")\n",
    "print(f\"Significant covarying pairs (pooled): {len(covarying_pooled_sig)}\")\n",
    "covarying_pooled_sig.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6498744a",
   "metadata": {},
   "source": [
    "### Top Frequent Genes in Covarying Pairs (Pooled)\n",
    "\n",
    "Visualize the top 20 most frequently appearing genes on the anchor side (`gene_center`)\n",
    "and the motif side (`gene_motif`) among significant covarying pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0cdb90bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_n = 20\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "covarying_pooled_sig[\"gene_center\"].value_counts().head(top_n).plot.barh(\n",
    "    ax=axes[0], color=\"steelblue\")\n",
    "axes[0].set_title(f\"Top {top_n} Anchor Genes (gene_center)\")\n",
    "axes[0].set_xlabel(\"Number of significant pairs\")\n",
    "axes[0].invert_yaxis()\n",
    "\n",
    "covarying_pooled_sig[\"gene_motif\"].value_counts().head(top_n).plot.barh(\n",
    "    ax=axes[1], color=\"coral\")\n",
    "axes[1].set_title(f\"Top {top_n} Motif Genes (gene_motif)\")\n",
    "axes[1].set_xlabel(\"Number of significant pairs\")\n",
    "axes[1].invert_yaxis()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd942b2b",
   "metadata": {},
   "source": [
    "## Method 2: Per-cell-type Covariation (`compute_gene_gene_correlation_by_type`)\n",
    "\n",
    "`compute_gene_gene_correlation_by_type` tests each **non-anchor cell type separately**,\n",
    "so the output includes a `cell_type` column.\n",
    "\n",
    "**Key parameters:**\n",
    "\n",
    "| Parameter | Description |\n",
    "|-----------|-------------|\n",
    "| `ct` | Anchor cell type |\n",
    "| `motif` | List of neighbor cell types forming the motif |\n",
    "| `dataset` | Condition name(s) to restrict the analysis to. If `None`, uses all datasets |\n",
    "| `genes` | List of genes to analyze. If `None`, uses intersection of genes across FOVs |\n",
    "| `max_dist` | Neighborhood radius (use either `max_dist` or `k`) |\n",
    "| `k` | Number of nearest neighbors (use either `max_dist` or `k`) |\n",
    "| `min_size` | Minimum neighborhood size for each anchor cell |\n",
    "| `min_nonzero` | Minimum non-zero expression values required to include a gene |\n",
    "| `alpha` | FDR significance threshold (default: 0.05) |\n",
    "\n",
    "**Key output columns:**\n",
    "\n",
    "| Column | Description |\n",
    "|--------|-------------|\n",
    "| `cell_type` | The neighbor cell type in the pair |\n",
    "| `gene_center` | Gene expressed in the anchor cell |\n",
    "| `gene_motif` | Gene expressed in the neighbor cell |\n",
    "| `corr_neighbor` | Correlation between motif+ anchor–neighbor pairs |\n",
    "| `corr_non_neighbor` | Correlation between randomly paired cells (Test 1 baseline) |\n",
    "| `corr_center_no_motif` | Correlation for motif− anchors (Test 2 baseline) |\n",
    "| `combined_score` | Combined effect size from both tests |\n",
    "| `if_significant` | Whether the pair passes both FDR-corrected tests |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6f5b0f88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing covarying genes using expression data ...\n",
      "Analyzing 2 non-center cell types in motif: ['kidney interstitial fibroblast', 'kidney loop of Henle thick ascending limb epithelial cell']\n",
      "================================================================================\n",
      "Selected 24 FOVs for analysis\n",
      "Gene coverage: 14390 genes in all FOVs, 18795 genes total (union)\n",
      "  -> 4405 genes present in subset of FOVs (will use available data)\n",
      "Analyzing 3000 genes across 24 FOVs\n",
      "\n",
      "================================================================================\n",
      "Step 1: Computing Correlation-3 (Center without motif vs Neighbors)\n",
      "...(137 lines omitted)...\n",
      "Total gene pairs tested: 15536862\n",
      "Significant covarying pairs: 3501\n"
     ]
    }
   ],
   "source": [
    "covarying = spm.compute_gene_gene_correlation_by_type(\n",
    "    ct=anchor_ct,\n",
    "    motif=motif,\n",
    "    dataset=ds_case,\n",
    "    max_dist=max_dist,\n",
    "    genes=selected_genes[ds_case],\n",
    "    alpha=0.05,\n",
    ")\n",
    "\n",
    "covarying_sig = covarying[covarying[\"if_significant\"]].copy()\n",
    "print(f\"Total gene pairs tested: {len(covarying)}\")\n",
    "print(f\"Significant covarying pairs: {len(covarying_sig)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0484e23a",
   "metadata": {},
   "source": [
    "### Top Frequent Genes in Covarying Pairs (Per Cell Type)\n",
    "\n",
    "For `compute_gene_gene_correlation_by_type`, visualize the top 20 genes\n",
    "separately for each neighbor cell type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ab8a082f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_n = 20\n",
    "cell_types = covarying_sig[\"cell_type\"].unique()\n",
    "\n",
    "for ct in cell_types:\n",
    "    ct_df = covarying_sig[covarying_sig[\"cell_type\"] == ct]\n",
    "    if len(ct_df) == 0:\n",
    "        continue\n",
    "\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "    fig.suptitle(f\"Neighbor cell type: {ct}\", fontsize=14)\n",
    "\n",
    "    ct_df[\"gene_center\"].value_counts().head(top_n).plot.barh(\n",
    "        ax=axes[0], color=\"steelblue\")\n",
    "    axes[0].set_title(f\"Top {top_n} Anchor Genes (gene_center)\")\n",
    "    axes[0].set_xlabel(\"Number of significant pairs\")\n",
    "    axes[0].invert_yaxis()\n",
    "\n",
    "    ct_df[\"gene_motif\"].value_counts().head(top_n).plot.barh(\n",
    "        ax=axes[1], color=\"coral\")\n",
    "    axes[1].set_title(f\"Top {top_n} Motif Genes (gene_motif)\")\n",
    "    axes[1].set_xlabel(\"Number of significant pairs\")\n",
    "    axes[1].invert_yaxis()\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa391dcc",
   "metadata": {},
   "source": [
    "## Summary by Neighbor Cell Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8df6d991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cell_type\n",
       "kidney interstitial fibroblast                               2525\n",
       "kidney loop of Henle thick ascending limb epithelial cell     976\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covarying_sig[\"cell_type\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "460abdf3",
   "metadata": {},
   "source": [
    "## Inspect Top Covarying Gene Pairs\n",
    "\n",
    "Sort by combined score (absolute value) to see the strongest associations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0ec7b72f",
   "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>cell_type</th>\n",
       "      <th>gene_center</th>\n",
       "      <th>gene_motif</th>\n",
       "      <th>corr_neighbor</th>\n",
       "      <th>corr_non_neighbor</th>\n",
       "      <th>p_value_test1</th>\n",
       "      <th>delta_corr_test1</th>\n",
       "      <th>corr_center_no_motif</th>\n",
       "      <th>p_value_test2</th>\n",
       "      <th>delta_corr_test2</th>\n",
       "      <th>combined_score</th>\n",
       "      <th>q_value_test1</th>\n",
       "      <th>q_value_test2</th>\n",
       "      <th>reject_test1_fdr</th>\n",
       "      <th>reject_test2_fdr</th>\n",
       "      <th>abs_combined_score</th>\n",
       "      <th>if_significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kidney interstitial fibroblast</td>\n",
       "      <td>Wif1</td>\n",
       "      <td>Lmo3</td>\n",
       "      <td>0.815627</td>\n",
       "      <td>-0.020971</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.836598</td>\n",
       "      <td>-0.000371</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.815997</td>\n",
       "      <td>246.653183</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>246.653183</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kidney interstitial fibroblast</td>\n",
       "      <td>Wif1</td>\n",
       "      <td>Wif1</td>\n",
       "      <td>0.786223</td>\n",
       "      <td>-0.035668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.821892</td>\n",
       "      <td>-0.000243</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.786467</td>\n",
       "      <td>239.128233</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>239.128233</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kidney interstitial fibroblast</td>\n",
       "      <td>Armc12</td>\n",
       "      <td>Lmo3</td>\n",
       "      <td>0.748789</td>\n",
       "      <td>-0.020264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.769053</td>\n",
       "      <td>-0.000078</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.748867</td>\n",
       "      <td>226.476755</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>226.476755</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>kidney interstitial fibroblast</td>\n",
       "      <td>Rapgef3os1</td>\n",
       "      <td>Slamf1</td>\n",
       "      <td>0.742925</td>\n",
       "      <td>-0.017139</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.760064</td>\n",
       "      <td>-0.000098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.743023</td>\n",
       "      <td>224.440595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>224.440595</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kidney interstitial fibroblast</td>\n",
       "      <td>Armc12</td>\n",
       "      <td>Wif1</td>\n",
       "      <td>0.721795</td>\n",
       "      <td>-0.034466</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.756261</td>\n",
       "      <td>-0.000126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.721921</td>\n",
       "      <td>219.666989</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>219.666989</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>kidney interstitial fibroblast</td>\n",
       "      <td>Luzp2</td>\n",
       "      <td>Serpina1e</td>\n",
       "      <td>0.699316</td>\n",
       "      <td>-0.024935</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.724251</td>\n",
       "      <td>0.005572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.693744</td>\n",
       "      <td>210.868927</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>210.868927</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>kidney interstitial fibroblast</td>\n",
       "      <td>Rapgef3os1</td>\n",
       "      <td>Cerox1</td>\n",
       "      <td>0.671484</td>\n",
       "      <td>-0.019855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.691338</td>\n",
       "      <td>-0.000087</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.671571</td>\n",
       "      <td>203.250420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>203.250420</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kidney interstitial fibroblast</td>\n",
       "      <td>Fam178b</td>\n",
       "      <td>Ppp1r3d</td>\n",
       "      <td>0.601521</td>\n",
       "      <td>-0.000445</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.601967</td>\n",
       "      <td>-0.000137</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.601659</td>\n",
       "      <td>180.525313</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>180.525313</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>kidney interstitial fibroblast</td>\n",
       "      <td>Gap43</td>\n",
       "      <td>Best3</td>\n",
       "      <td>0.528982</td>\n",
       "      <td>-0.016956</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.545937</td>\n",
       "      <td>-0.000250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.529231</td>\n",
       "      <td>160.272942</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>160.272942</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>kidney interstitial fibroblast</td>\n",
       "      <td>Cercam</td>\n",
       "      <td>Rergl</td>\n",
       "      <td>0.493953</td>\n",
       "      <td>-0.001935</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.495888</td>\n",
       "      <td>0.004272</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.489681</td>\n",
       "      <td>147.462958</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>147.462958</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        cell_type gene_center gene_motif  corr_neighbor  \\\n",
       "0  kidney interstitial fibroblast        Wif1       Lmo3       0.815627   \n",
       "1  kidney interstitial fibroblast        Wif1       Wif1       0.786223   \n",
       "2  kidney interstitial fibroblast      Armc12       Lmo3       0.748789   \n",
       "3  kidney interstitial fibroblast  Rapgef3os1     Slamf1       0.742925   \n",
       "4  kidney interstitial fibroblast      Armc12       Wif1       0.721795   \n",
       "5  kidney interstitial fibroblast       Luzp2  Serpina1e       0.699316   \n",
       "6  kidney interstitial fibroblast  Rapgef3os1     Cerox1       0.671484   \n",
       "7  kidney interstitial fibroblast     Fam178b    Ppp1r3d       0.601521   \n",
       "8  kidney interstitial fibroblast       Gap43      Best3       0.528982   \n",
       "...(47 lines omitted)...\n",
       "8            True  \n",
       "9            True  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covarying_sig.sort_values(\"abs_combined_score\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a4ba250",
   "metadata": {},
   "source": [
    "## Visualize Covarying Gene Modules\n",
    "\n",
    "> **Note:** `plot_gene_pair_heatmap` is available on the single-dataset `spatial_query` class.\n",
    "> For multi-dataset results, you can use the standalone plotting function from `SpatialQuery.plotting`\n",
    "> or apply clustering directly to the result DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3336eabe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kidney loop of Henle thick ascending limb epithelial cell: Skipping neg with 4 rows and 1 columns.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from SpatialQuery.plotting import plot_gene_pair_heatmap\n",
    "\n",
    "if len(covarying_sig) > 0:\n",
    "    modules = plot_gene_pair_heatmap(\n",
    "        gene_pair_df=covarying_sig,\n",
    "        figsize=(12, 6),\n",
    "    )\n",
    "else:\n",
    "    print(\"No significant covarying gene pairs found.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4f2b3870",
   "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>gene_center</th>\n",
       "      <th>gene_motif</th>\n",
       "      <th>combined_score</th>\n",
       "      <th>cluster_type</th>\n",
       "      <th>cluster_row</th>\n",
       "      <th>cluster_col</th>\n",
       "      <th>cell_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AA465934</td>\n",
       "      <td>Adamts7</td>\n",
       "      <td>25.321869</td>\n",
       "      <td>positive</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>kidney interstitial fibroblast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AA465934</td>\n",
       "      <td>Il12rb2</td>\n",
       "      <td>26.951418</td>\n",
       "      <td>positive</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>kidney interstitial fibroblast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AA465934</td>\n",
       "      <td>Tmem143</td>\n",
       "      <td>1.009324</td>\n",
       "      <td>positive</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>kidney interstitial fibroblast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AA914427</td>\n",
       "      <td>Dgki</td>\n",
       "      <td>0.660621</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>kidney interstitial fibroblast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AA914427</td>\n",
       "      <td>Iqcm</td>\n",
       "      <td>0.763277</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>kidney interstitial fibroblast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3492</th>\n",
       "      <td>Zfp983</td>\n",
       "      <td>Eps8l3</td>\n",
       "      <td>42.379554</td>\n",
       "      <td>positive</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>kidney loop of Henle thick ascending limb epit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3493</th>\n",
       "      <td>Zfp991</td>\n",
       "      <td>Nuggc</td>\n",
       "      <td>0.338234</td>\n",
       "      <td>positive</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>kidney loop of Henle thick ascending limb epit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3494</th>\n",
       "      <td>Zfp992</td>\n",
       "      <td>B3galt1</td>\n",
       "      <td>0.399425</td>\n",
       "      <td>positive</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>kidney loop of Henle thick ascending limb epit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3495</th>\n",
       "      <td>Zfp992</td>\n",
       "      <td>Sectm1b</td>\n",
       "      <td>0.568542</td>\n",
       "      <td>positive</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>kidney loop of Henle thick ascending limb epit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3496</th>\n",
       "      <td>Zkscan5</td>\n",
       "      <td>Mypn</td>\n",
       "      <td>0.492021</td>\n",
       "      <td>positive</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>kidney loop of Henle thick ascending limb epit...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3497 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     gene_center gene_motif  combined_score cluster_type  cluster_row  \\\n",
       "0       AA465934    Adamts7       25.321869     positive            0   \n",
       "1       AA465934    Il12rb2       26.951418     positive            0   \n",
       "2       AA465934    Tmem143        1.009324     positive            0   \n",
       "3       AA914427       Dgki        0.660621     positive            1   \n",
       "4       AA914427       Iqcm        0.763277     positive            1   \n",
       "...          ...        ...             ...          ...          ...   \n",
       "3492      Zfp983     Eps8l3       42.379554     positive            0   \n",
       "3493      Zfp991      Nuggc        0.338234     positive            0   \n",
       "3494      Zfp992    B3galt1        0.399425     positive            0   \n",
       "...(15 lines omitted)...\n",
       "\n",
       "[3497 rows x 7 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab7c4a67",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}