{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1887860d",
   "metadata": {},
   "source": [
    "# Pancreatic Endocrinogenesis Reference Analysis\n",
    "\n",
    "This notebook uses pancreas reference outputs produced from the provided stage-1 and stage-2 checkpoints. It reads checkpoint-derived reference CSV/H5AD/attention files and does not train new model weights.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "916a63b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T10:15:05.441185Z",
     "iopub.status.busy": "2026-05-17T10:15:05.440774Z",
     "iopub.status.idle": "2026-05-17T10:15:07.396844Z",
     "shell.execute_reply": "2026-05-17T10:15:07.395831Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import anndata as ad\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scanpy as sc\n",
    "import pandas as pd\n",
    "from scipy import sparse\n",
    "\n",
    "PROJECT = Path.cwd().resolve()\n",
    "if PROJECT.name == \"notebooks\":\n",
    "    PROJECT = PROJECT.parent\n",
    "\n",
    "REFERENCE_DIR = PROJECT / \"data\" / \"pancreas\" / \"reference_outputs\"\n",
    "CHECKPOINT_DIR = PROJECT / \"data\" / \"pancreas\" / \"reference_checkpoints\"\n",
    "STAGE1_CKPT = CHECKPOINT_DIR / \"pancreas_stage1.ckpt\"\n",
    "STAGE2_CKPT = CHECKPOINT_DIR / \"pancreas_stage2.ckpt\"\n",
    "GENE_ORDER = CHECKPOINT_DIR / \"pancreas_genes.txt\"\n",
    "STAGE1_CSV = REFERENCE_DIR / \"pancreas_stage1_reference.csv\"\n",
    "STAGE2_CSV = REFERENCE_DIR / \"pancreas_stage2_reference.csv\"\n",
    "ATTENTION_H5AD = REFERENCE_DIR / \"pancreas_attention_scores.h5ad\"\n",
    "INSULIN_ACTIVITY_CSV = REFERENCE_DIR / \"pancreas_insulin_signaling_attention_activity.csv\"\n",
    "MEAN_ATTENTION_DIR = REFERENCE_DIR / \"pancreas_mean_attention_by_celltype\"\n",
    "BETA_MEAN_ATTENTION_NPZ = MEAN_ATTENTION_DIR / \"Beta_mean_attention.npz\"\n",
    "FIGURE_DIR = PROJECT / \"outputs\" / \"pancreas_reference_notebook_figures\"\n",
    "FIGURE_DIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "required_paths = [\n",
    "    STAGE1_CKPT,\n",
    "    STAGE2_CKPT,\n",
    "    GENE_ORDER,\n",
    "    STAGE1_CSV,\n",
    "    STAGE2_CSV,\n",
    "    ATTENTION_H5AD,\n",
    "    INSULIN_ACTIVITY_CSV,\n",
    "    BETA_MEAN_ATTENTION_NPZ,\n",
    "]\n",
    "for path in required_paths:\n",
    "    if not path.exists():\n",
    "        raise FileNotFoundError(path)\n",
    "\n",
    "CELLTYPE_ORDER = [\"Ductal\", \"Ngn3Low\", \"Ngn3High\", \"Pre-endocrine\", \"Alpha\", \"Beta\", \"Delta\", \"Epsilon\"]\n",
    "RANK_GROUP_ORDER = [\"Ngn3Low\", \"Beta\", \"Alpha\", \"Ductal\", \"Ngn3High\", \"Delta\", \"Epsilon\", \"Pre-endocrine\"]\n",
    "CELLTYPE_DISPLAY = {\"Ngn3Low\": \"Ngn3Low\", \"Ngn3High\": \"Ngn3High\"}\n",
    "\n",
    "STAGE1_CSV, STAGE2_CSV, ATTENTION_H5AD, INSULIN_ACTIVITY_CSV, BETA_MEAN_ATTENTION_NPZ, FIGURE_DIR\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c5cddcb",
   "metadata": {},
   "source": [
    "## What The Attention Score Means\n",
    "\n",
    "`pancreas_attention_scores.h5ad` stores per-cell attention-derived regulator scores for the pancreas reference. For each regulator in the prior network, the score sums attention from that regulator to its prior targets, after retaining regulators that appear in the top 10% attention columns for at least one target gene. Rows are cells and columns are regulators.\n",
    "\n",
    "For reproducible checkpoint-based analysis, keep the gene order paired with the released pancreas checkpoint and run inference/export from the provided checkpoint weights. The gene set can be identical while the index order differs, and checkpoint attention matrices are index-aligned.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2472301",
   "metadata": {},
   "source": [
    "## Shared Helpers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e6ca8535",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T10:15:07.401103Z",
     "iopub.status.busy": "2026-05-17T10:15:07.400801Z",
     "iopub.status.idle": "2026-05-17T10:15:07.406252Z",
     "shell.execute_reply": "2026-05-17T10:15:07.405307Z"
    }
   },
   "outputs": [],
   "source": [
    "def as_dense(matrix):\n",
    "    return matrix.toarray() if sparse.issparse(matrix) else np.asarray(matrix)\n",
    "\n",
    "\n",
    "def minmax_columns(values):\n",
    "    values = np.asarray(values, dtype=float)\n",
    "    mins = np.nanmin(values, axis=0)\n",
    "    maxs = np.nanmax(values, axis=0)\n",
    "    ranges = maxs - mins\n",
    "    ranges[ranges == 0] = 1.0\n",
    "    return (values - mins) / ranges\n",
    "\n",
    "\n",
    "def display_celltype(name):\n",
    "    return CELLTYPE_DISPLAY.get(name, name)\n",
    "\n",
    "\n",
    "def title_case_gene(name):\n",
    "    return name[:1].upper() + name[1:].lower()\n",
    "\n",
    "\n",
    "def require_genes(adata, genes):\n",
    "    genes_upper = [gene.upper() for gene in genes]\n",
    "    available = [gene for gene in genes_upper if gene in adata.var_names]\n",
    "    missing = [gene for gene in genes_upper if gene not in adata.var_names]\n",
    "    if missing:\n",
    "        print(\"Missing genes:\", missing)\n",
    "    return available"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54ea80f3",
   "metadata": {},
   "source": [
    "## Reference Output Preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d83fdf2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T10:15:07.409332Z",
     "iopub.status.busy": "2026-05-17T10:15:07.409154Z",
     "iopub.status.idle": "2026-05-17T10:15:07.433941Z",
     "shell.execute_reply": "2026-05-17T10:15:07.432817Z"
    }
   },
   "outputs": [
    {
     "data": {
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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>cellIndex</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>unsplice</th>\n",
       "      <th>splice</th>\n",
       "      <th>unsplice_predict</th>\n",
       "      <th>splice_predict</th>\n",
       "      <th>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0610010F05RIK</td>\n",
       "      <td>0.188744</td>\n",
       "      <td>0.240883</td>\n",
       "      <td>0.285733</td>\n",
       "      <td>0.230735</td>\n",
       "      <td>Pre-endocrine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0610010F05RIK</td>\n",
       "      <td>0.096946</td>\n",
       "      <td>0.108301</td>\n",
       "      <td>0.229186</td>\n",
       "      <td>0.108476</td>\n",
       "      <td>Ductal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0610010F05RIK</td>\n",
       "      <td>0.183575</td>\n",
       "      <td>0.251075</td>\n",
       "      <td>0.282041</td>\n",
       "      <td>0.235197</td>\n",
       "      <td>Alpha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0610010F05RIK</td>\n",
       "      <td>0.094704</td>\n",
       "      <td>0.174730</td>\n",
       "      <td>0.227560</td>\n",
       "      <td>0.150557</td>\n",
       "      <td>Ductal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0610010F05RIK</td>\n",
       "      <td>0.048288</td>\n",
       "      <td>0.233265</td>\n",
       "      <td>0.200468</td>\n",
       "      <td>0.170477</td>\n",
       "      <td>Ngn3 high EP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cellIndex      gene_name  unsplice    splice  unsplice_predict  \\\n",
       "0          0  0610010F05RIK  0.188744  0.240883          0.285733   \n",
       "1          1  0610010F05RIK  0.096946  0.108301          0.229186   \n",
       "2          2  0610010F05RIK  0.183575  0.251075          0.282041   \n",
       "3          3  0610010F05RIK  0.094704  0.174730          0.227560   \n",
       "4          4  0610010F05RIK  0.048288  0.233265          0.200468   \n",
       "\n",
       "   splice_predict       clusters  \n",
       "0        0.230735  Pre-endocrine  \n",
       "1        0.108476         Ductal  \n",
       "2        0.235197          Alpha  \n",
       "3        0.150557         Ductal  \n",
       "4        0.170477   Ngn3 high EP  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cellIndex</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>unsplice</th>\n",
       "      <th>splice</th>\n",
       "      <th>unsplice_predict</th>\n",
       "      <th>splice_predict</th>\n",
       "      <th>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0610010F05RIK</td>\n",
       "      <td>0.188744</td>\n",
       "      <td>0.240883</td>\n",
       "      <td>0.170604</td>\n",
       "      <td>0.231052</td>\n",
       "      <td>Pre-endocrine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0610010F05RIK</td>\n",
       "      <td>0.096946</td>\n",
       "      <td>0.108301</td>\n",
       "      <td>0.104260</td>\n",
       "      <td>0.108127</td>\n",
       "      <td>Ductal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0610010F05RIK</td>\n",
       "      <td>0.183575</td>\n",
       "      <td>0.251075</td>\n",
       "      <td>0.163577</td>\n",
       "      <td>0.236625</td>\n",
       "      <td>Alpha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0610010F05RIK</td>\n",
       "      <td>0.094704</td>\n",
       "      <td>0.174730</td>\n",
       "      <td>0.101195</td>\n",
       "      <td>0.153775</td>\n",
       "      <td>Ductal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0610010F05RIK</td>\n",
       "      <td>0.048288</td>\n",
       "      <td>0.233265</td>\n",
       "      <td>0.076497</td>\n",
       "      <td>0.178194</td>\n",
       "      <td>Ngn3 high EP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cellIndex      gene_name  unsplice    splice  unsplice_predict  \\\n",
       "0          0  0610010F05RIK  0.188744  0.240883          0.170604   \n",
       "1          1  0610010F05RIK  0.096946  0.108301          0.104260   \n",
       "2          2  0610010F05RIK  0.183575  0.251075          0.163577   \n",
       "3          3  0610010F05RIK  0.094704  0.174730          0.101195   \n",
       "4          4  0610010F05RIK  0.048288  0.233265          0.076497   \n",
       "\n",
       "   splice_predict       clusters  \n",
       "0        0.231052  Pre-endocrine  \n",
       "1        0.108127         Ductal  \n",
       "2        0.236625          Alpha  \n",
       "3        0.153775         Ductal  \n",
       "4        0.178194   Ngn3 high EP  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preview_cols = [\"cellIndex\", \"gene_name\", \"unsplice\", \"splice\", \"unsplice_predict\", \"splice_predict\", \"clusters\"]\n",
    "stage1_preview = pd.read_csv(STAGE1_CSV, usecols=preview_cols, nrows=5)\n",
    "stage2_preview = pd.read_csv(STAGE2_CSV, usecols=preview_cols, nrows=5)\n",
    "\n",
    "display(stage1_preview)\n",
    "display(stage2_preview)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8788ad3c",
   "metadata": {},
   "source": [
    "## Dynamic Gene Importance Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e4262030",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T10:15:07.437258Z",
     "iopub.status.busy": "2026-05-17T10:15:07.437107Z",
     "iopub.status.idle": "2026-05-17T10:15:07.509315Z",
     "shell.execute_reply": "2026-05-17T10:15:07.508238Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOXP2</th>\n",
       "      <th>NEUROG3</th>\n",
       "      <th>HMGN3</th>\n",
       "      <th>PYY</th>\n",
       "      <th>IAPP</th>\n",
       "      <th>CPE</th>\n",
       "      <th>GCG</th>\n",
       "      <th>SLC38A5</th>\n",
       "      <th>PDX1</th>\n",
       "      <th>INS2</th>\n",
       "      <th>SST</th>\n",
       "      <th>HHEX</th>\n",
       "      <th>GHRL</th>\n",
       "      <th>clusterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.755162</td>\n",
       "      <td>0.020473</td>\n",
       "      <td>0.004234</td>\n",
       "      <td>0.011028</td>\n",
       "      <td>0.016628</td>\n",
       "      <td>0.023333</td>\n",
       "      <td>0.015215</td>\n",
       "      <td>0.017793</td>\n",
       "      <td>0.080422</td>\n",
       "      <td>0.027896</td>\n",
       "      <td>0.021940</td>\n",
       "      <td>0.025587</td>\n",
       "      <td>0.014216</td>\n",
       "      <td>Ngn3Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.777351</td>\n",
       "      <td>0.024355</td>\n",
       "      <td>0.006867</td>\n",
       "      <td>0.009319</td>\n",
       "      <td>0.015361</td>\n",
       "      <td>0.023088</td>\n",
       "      <td>0.014962</td>\n",
       "      <td>0.016759</td>\n",
       "      <td>0.069372</td>\n",
       "      <td>0.029259</td>\n",
       "      <td>0.022845</td>\n",
       "      <td>0.069163</td>\n",
       "      <td>0.013144</td>\n",
       "      <td>Ngn3Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.881297</td>\n",
       "      <td>0.004649</td>\n",
       "      <td>0.010287</td>\n",
       "      <td>0.011557</td>\n",
       "      <td>0.018305</td>\n",
       "      <td>0.029824</td>\n",
       "      <td>0.019881</td>\n",
       "      <td>0.020477</td>\n",
       "      <td>0.134617</td>\n",
       "      <td>0.034727</td>\n",
       "      <td>0.028679</td>\n",
       "      <td>0.008866</td>\n",
       "      <td>0.017517</td>\n",
       "      <td>Ngn3Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.705656</td>\n",
       "      <td>0.051997</td>\n",
       "      <td>0.006194</td>\n",
       "      <td>0.010792</td>\n",
       "      <td>0.013806</td>\n",
       "      <td>0.023002</td>\n",
       "      <td>0.014517</td>\n",
       "      <td>0.016068</td>\n",
       "      <td>0.109997</td>\n",
       "      <td>0.026764</td>\n",
       "      <td>0.020390</td>\n",
       "      <td>0.021011</td>\n",
       "      <td>0.012446</td>\n",
       "      <td>Ngn3Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.694818</td>\n",
       "      <td>0.106666</td>\n",
       "      <td>0.006746</td>\n",
       "      <td>0.009698</td>\n",
       "      <td>0.013946</td>\n",
       "      <td>0.022039</td>\n",
       "      <td>0.013302</td>\n",
       "      <td>0.015491</td>\n",
       "      <td>0.117015</td>\n",
       "      <td>0.026374</td>\n",
       "      <td>0.019499</td>\n",
       "      <td>0.021170</td>\n",
       "      <td>0.012285</td>\n",
       "      <td>Ngn3Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      FOXP2   NEUROG3     HMGN3       PYY      IAPP       CPE       GCG  \\\n",
       "0  0.755162  0.020473  0.004234  0.011028  0.016628  0.023333  0.015215   \n",
       "1  0.777351  0.024355  0.006867  0.009319  0.015361  0.023088  0.014962   \n",
       "2  0.881297  0.004649  0.010287  0.011557  0.018305  0.029824  0.019881   \n",
       "3  0.705656  0.051997  0.006194  0.010792  0.013806  0.023002  0.014517   \n",
       "4  0.694818  0.106666  0.006746  0.009698  0.013946  0.022039  0.013302   \n",
       "\n",
       "    SLC38A5      PDX1      INS2       SST      HHEX      GHRL clusterid  \n",
       "0  0.017793  0.080422  0.027896  0.021940  0.025587  0.014216   Ngn3Low  \n",
       "1  0.016759  0.069372  0.029259  0.022845  0.069163  0.013144   Ngn3Low  \n",
       "2  0.020477  0.134617  0.034727  0.028679  0.008866  0.017517   Ngn3Low  \n",
       "3  0.016068  0.109997  0.026764  0.020390  0.021011  0.012446   Ngn3Low  \n",
       "4  0.015491  0.117015  0.026374  0.019499  0.021170  0.012285   Ngn3Low  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MARKER_GENES = [\n",
    "    \"FOXP2\", \"NEUROG3\", \"HMGN3\", \"PYY\", \"IAPP\", \"CPE\", \"GCG\",\n",
    "    \"SLC38A5\", \"PDX1\", \"INS2\", \"SST\", \"HHEX\", \"GHRL\",\n",
    "]\n",
    "MARKER_LABELS = {\n",
    "    \"FOXP2\": \"Foxp2\",\n",
    "    \"NEUROG3\": \"Neurog3\",\n",
    "    \"HMGN3\": \"Hmgn3\",\n",
    "    \"PYY\": \"Pyy\",\n",
    "    \"IAPP\": \"Iapp\",\n",
    "    \"CPE\": \"Cpe\",\n",
    "    \"GCG\": \"Gcg\",\n",
    "    \"SLC38A5\": \"Slc38a5\",\n",
    "    \"PDX1\": \"Pdx1\",\n",
    "    \"INS2\": \"Ins2\",\n",
    "    \"SST\": \"Sst\",\n",
    "    \"HHEX\": \"Hhex\",\n",
    "    \"GHRL\": \"Ghrl\",\n",
    "}\n",
    "\n",
    "attention = ad.read_h5ad(ATTENTION_H5AD)\n",
    "if \"clusterid\" not in attention.obs:\n",
    "    attention.obs[\"clusterid\"] = attention.obs[\"cell_type\"].astype(str).map({\n",
    "        \"Ngn3 low EP\": \"Ngn3Low\",\n",
    "        \"Ngn3 high EP\": \"Ngn3High\",\n",
    "    }).fillna(attention.obs[\"cell_type\"].astype(str))\n",
    "\n",
    "available_markers = require_genes(attention, MARKER_GENES)\n",
    "marker_scores = as_dense(attention[:, available_markers].X)\n",
    "marker_scores = minmax_columns(marker_scores)\n",
    "\n",
    "score_df = pd.DataFrame(marker_scores, columns=available_markers, index=attention.obs_names)\n",
    "score_df[\"clusterid\"] = attention.obs[\"clusterid\"].astype(str).values\n",
    "score_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "192a4ccf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T10:15:07.513237Z",
     "iopub.status.busy": "2026-05-17T10:15:07.513072Z",
     "iopub.status.idle": "2026-05-17T10:15:08.091894Z",
     "shell.execute_reply": "2026-05-17T10:15:08.090941Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 820x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_cutoff = 0.10\n",
    "long_scores = score_df.melt(id_vars=\"clusterid\", var_name=\"gene\", value_name=\"score\")\n",
    "summary = (\n",
    "    long_scores.groupby([\"clusterid\", \"gene\"], observed=True)\n",
    "    .agg(\n",
    "        mean_score=(\"score\", \"mean\"),\n",
    "        fraction=(\"score\", lambda values: 100 * (values > score_cutoff).mean()),\n",
    "    )\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "grid = pd.MultiIndex.from_product([CELLTYPE_ORDER, available_markers], names=[\"clusterid\", \"gene\"]).to_frame(index=False)\n",
    "summary = grid.merge(summary, on=[\"clusterid\", \"gene\"], how=\"left\").fillna({\"mean_score\": 0.0, \"fraction\": 0.0})\n",
    "summary[\"x\"] = summary[\"gene\"].map({gene: idx for idx, gene in enumerate(available_markers)})\n",
    "summary[\"y\"] = summary[\"clusterid\"].map({name: idx for idx, name in enumerate(CELLTYPE_ORDER)})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8.2, 4.8))\n",
    "size_scale = 4.2\n",
    "scatter = ax.scatter(\n",
    "    summary[\"x\"],\n",
    "    summary[\"y\"],\n",
    "    s=8 + summary[\"fraction\"] * size_scale,\n",
    "    c=summary[\"mean_score\"],\n",
    "    cmap=\"GnBu\",\n",
    "    vmin=0,\n",
    "    vmax=1,\n",
    "    edgecolor=\"#5E6A6A\",\n",
    "    linewidth=0.45,\n",
    ")\n",
    "\n",
    "ax.set_title(\"Dynamic Gene Importance Score\", fontsize=14, weight=\"bold\", pad=10)\n",
    "ax.set_xticks(range(len(available_markers)))\n",
    "ax.set_xticklabels([MARKER_LABELS.get(gene, title_case_gene(gene)) for gene in available_markers], rotation=90)\n",
    "ax.set_yticks(range(len(CELLTYPE_ORDER)))\n",
    "ax.set_yticklabels([display_celltype(name) for name in CELLTYPE_ORDER])\n",
    "ax.set_xlim(-0.8, len(available_markers) - 0.2)\n",
    "ax.set_ylim(-0.8, len(CELLTYPE_ORDER) - 0.2)\n",
    "ax.grid(axis=\"y\", color=\"#E6E6E6\", linewidth=0.6)\n",
    "ax.set_axisbelow(True)\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_linewidth(0.8)\n",
    "\n",
    "legend_levels = [20, 40, 60, 80, 100]\n",
    "handles = [\n",
    "    ax.scatter([], [], s=8 + level * size_scale, color=\"#6D6D6D\", alpha=0.75, edgecolor=\"none\")\n",
    "    for level in legend_levels\n",
    "]\n",
    "size_legend = ax.legend(\n",
    "    handles,\n",
    "    [str(level) for level in legend_levels],\n",
    "    title=\"Fraction of cells\\nin group (%)\",\n",
    "    frameon=False,\n",
    "    bbox_to_anchor=(1.02, 0.72),\n",
    "    loc=\"center left\",\n",
    "    labelspacing=1.0,\n",
    ")\n",
    "ax.add_artist(size_legend)\n",
    "\n",
    "cbar = fig.colorbar(scatter, ax=ax, fraction=0.046, pad=0.18)\n",
    "cbar.set_label(\"Gene importance\\nin group\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURE_DIR / \"figure_d_dynamic_gene_importance.png\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab88e66c",
   "metadata": {},
   "source": [
    "## Insulin Signaling Pathway Activity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`INSULIN_ACTIVITY_CSV` is a precomputed per-cell table used to keep this tutorial lightweight. It stores the pathway activity values plus the cell metadata needed for plotting. The raw per-cell attention tensors used to make this table are larger than the notebook inputs and are not required for viewing the example analysis.\n",
    "\n",
    "To recompute the table, save the raw per-cell stage-1 attention matrix before it is aggregated into TF-score or cell-type mean outputs, and use the same model gene order used by the checkpoint. The activity score is calculated by summing attention weights inside the insulin signaling gene set, restricted to regulator-target pairs present in the prior network. For one cell:\n",
    "\n",
    "```python\n",
    "score = 0.0\n",
    "for target in INSULIN_SIGNALING_GENES:\n",
    "    for regulator in INSULIN_SIGNALING_GENES:\n",
    "        if (regulator.upper(), target.upper()) in prior_edges:\n",
    "            score += attention[gene_to_idx[target.upper()], gene_to_idx[regulator.upper()]]\n",
    "```\n",
    "\n",
    "If the saved attention tensor has multiple heads or layers, first average it to a gene-by-gene matrix. Then write one row per cell with `cellIndex`, `pathway_activity`, `cellID`, `clusters`, `embedding1`, and `embedding2`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bbf1d04d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T10:15:08.095236Z",
     "iopub.status.busy": "2026-05-17T10:15:08.095061Z",
     "iopub.status.idle": "2026-05-17T10:15:08.106866Z",
     "shell.execute_reply": "2026-05-17T10:15:08.105957Z"
    }
   },
   "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>cellIndex</th>\n",
       "      <th>pathway_activity</th>\n",
       "      <th>cellID</th>\n",
       "      <th>clusters</th>\n",
       "      <th>embedding1</th>\n",
       "      <th>embedding2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.979330</td>\n",
       "      <td>AAACCTGAGAGGGATA</td>\n",
       "      <td>Pre-endocrine</td>\n",
       "      <td>6.143066</td>\n",
       "      <td>-0.063644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.099439</td>\n",
       "      <td>AAACCTGAGCCTTGAT</td>\n",
       "      <td>Ductal</td>\n",
       "      <td>-9.906417</td>\n",
       "      <td>0.197778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.748969</td>\n",
       "      <td>AAACCTGAGGCAATTA</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>7.559791</td>\n",
       "      <td>0.583762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.272176</td>\n",
       "      <td>AAACCTGCATCATCCC</td>\n",
       "      <td>Ductal</td>\n",
       "      <td>-11.283765</td>\n",
       "      <td>4.218998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.067271</td>\n",
       "      <td>AAACCTGGTAAGTGGC</td>\n",
       "      <td>Ngn3 high EP</td>\n",
       "      <td>1.721565</td>\n",
       "      <td>-4.753407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cellIndex  pathway_activity            cellID       clusters  embedding1  \\\n",
       "0          0          0.979330  AAACCTGAGAGGGATA  Pre-endocrine    6.143066   \n",
       "1          1          1.099439  AAACCTGAGCCTTGAT         Ductal   -9.906417   \n",
       "2          2          0.748969  AAACCTGAGGCAATTA          Alpha    7.559791   \n",
       "3          3          1.272176  AAACCTGCATCATCCC         Ductal  -11.283765   \n",
       "4          4          1.067271  AAACCTGGTAAGTGGC   Ngn3 high EP    1.721565   \n",
       "\n",
       "   embedding2  \n",
       "0   -0.063644  \n",
       "1    0.197778  \n",
       "2    0.583762  \n",
       "3    4.218998  \n",
       "4   -4.753407  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INSULIN_SIGNALING_GENES = [\n",
    "    \"Ins2\", \"Pik3r1\", \"Pik3r3\", \"Akt3\", \"Calm2\", \"Calml4\", \"Pygl\", \"Pde3b\",\n",
    "    \"Prkacb\", \"Prkar2a\", \"Prkar1b\", \"Exoc7\", \"Gck\", \"Foxo1\", \"G6pc2\", \"Bad\",\n",
    "    \"Shc2\", \"Braf\", \"Mapk8\", \"Mapk10\", \"Inppl1\", \"Inpp5a\",\n",
    "]\n",
    "\n",
    "pathway_activity = pd.read_csv(INSULIN_ACTIVITY_CSV)\n",
    "pathway_activity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cac91096",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T10:15:08.109602Z",
     "iopub.status.busy": "2026-05-17T10:15:08.109470Z",
     "iopub.status.idle": "2026-05-17T10:15:08.448399Z",
     "shell.execute_reply": "2026-05-17T10:15:08.447257Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 660x580 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6.6, 5.8))\n",
    "scatter = ax.scatter(\n",
    "    pathway_activity[\"embedding1\"],\n",
    "    pathway_activity[\"embedding2\"],\n",
    "    c=pathway_activity[\"pathway_activity\"],\n",
    "    cmap=\"viridis\",\n",
    "    s=7,\n",
    "    linewidth=0,\n",
    ")\n",
    "ax.set_title(\"Insulin Signaling Pathway Activity\", fontsize=14, weight=\"bold\", pad=10)\n",
    "ax.set_xlabel(\"UMAP 1\")\n",
    "ax.set_ylabel(\"UMAP 2\")\n",
    "ax.set_aspect(\"equal\", adjustable=\"box\")\n",
    "ax.axis(\"off\")\n",
    "cbar = fig.colorbar(scatter, ax=ax, fraction=0.046, pad=0.02)\n",
    "cbar.set_label(\"Pathway Activity\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURE_DIR / \"figure_e_insulin_signaling_activity.png\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bba0e6a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T10:15:08.454906Z",
     "iopub.status.busy": "2026-05-17T10:15:08.454450Z",
     "iopub.status.idle": "2026-05-17T10:15:08.488297Z",
     "shell.execute_reply": "2026-05-17T10:15:08.487276Z"
    }
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clusterid</th>\n",
       "      <th></th>\n",
       "      <th></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>Ductal</th>\n",
       "      <td>916.0</td>\n",
       "      <td>1.173289</td>\n",
       "      <td>0.090704</td>\n",
       "      <td>1.009788</td>\n",
       "      <td>1.092108</td>\n",
       "      <td>1.157189</td>\n",
       "      <td>1.253498</td>\n",
       "      <td>1.331086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ngn3Low</th>\n",
       "      <td>262.0</td>\n",
       "      <td>1.132525</td>\n",
       "      <td>0.105283</td>\n",
       "      <td>0.983953</td>\n",
       "      <td>1.052862</td>\n",
       "      <td>1.098461</td>\n",
       "      <td>1.219752</td>\n",
       "      <td>1.511620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ngn3High</th>\n",
       "      <td>642.0</td>\n",
       "      <td>0.867863</td>\n",
       "      <td>0.161062</td>\n",
       "      <td>0.551875</td>\n",
       "      <td>0.754409</td>\n",
       "      <td>0.861131</td>\n",
       "      <td>0.955882</td>\n",
       "      <td>1.636565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pre-endocrine</th>\n",
       "      <td>592.0</td>\n",
       "      <td>0.677295</td>\n",
       "      <td>0.175751</td>\n",
       "      <td>0.302864</td>\n",
       "      <td>0.553564</td>\n",
       "      <td>0.646192</td>\n",
       "      <td>0.781494</td>\n",
       "      <td>1.511066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha</th>\n",
       "      <td>481.0</td>\n",
       "      <td>0.704523</td>\n",
       "      <td>0.148855</td>\n",
       "      <td>0.456200</td>\n",
       "      <td>0.579345</td>\n",
       "      <td>0.700906</td>\n",
       "      <td>0.803784</td>\n",
       "      <td>1.489411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>591.0</td>\n",
       "      <td>2.473439</td>\n",
       "      <td>1.716618</td>\n",
       "      <td>0.501347</td>\n",
       "      <td>0.965723</td>\n",
       "      <td>1.358620</td>\n",
       "      <td>4.192955</td>\n",
       "      <td>6.732128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delta</th>\n",
       "      <td>70.0</td>\n",
       "      <td>0.500472</td>\n",
       "      <td>0.132094</td>\n",
       "      <td>0.358377</td>\n",
       "      <td>0.399241</td>\n",
       "      <td>0.445940</td>\n",
       "      <td>0.581606</td>\n",
       "      <td>0.973334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epsilon</th>\n",
       "      <td>142.0</td>\n",
       "      <td>0.715686</td>\n",
       "      <td>0.052275</td>\n",
       "      <td>0.593212</td>\n",
       "      <td>0.678203</td>\n",
       "      <td>0.711773</td>\n",
       "      <td>0.750364</td>\n",
       "      <td>0.834830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count      mean       std       min       25%       50%  \\\n",
       "clusterid                                                                \n",
       "Ductal         916.0  1.173289  0.090704  1.009788  1.092108  1.157189   \n",
       "Ngn3Low        262.0  1.132525  0.105283  0.983953  1.052862  1.098461   \n",
       "Ngn3High       642.0  0.867863  0.161062  0.551875  0.754409  0.861131   \n",
       "Pre-endocrine  592.0  0.677295  0.175751  0.302864  0.553564  0.646192   \n",
       "Alpha          481.0  0.704523  0.148855  0.456200  0.579345  0.700906   \n",
       "Beta           591.0  2.473439  1.716618  0.501347  0.965723  1.358620   \n",
       "Delta           70.0  0.500472  0.132094  0.358377  0.399241  0.445940   \n",
       "Epsilon        142.0  0.715686  0.052275  0.593212  0.678203  0.711773   \n",
       "\n",
       "                    75%       max  \n",
       "clusterid                          \n",
       "Ductal         1.253498  1.331086  \n",
       "Ngn3Low        1.219752  1.511620  \n",
       "Ngn3High       0.955882  1.636565  \n",
       "Pre-endocrine  0.781494  1.511066  \n",
       "Alpha          0.803784  1.489411  \n",
       "Beta           4.192955  6.732128  \n",
       "Delta          0.581606  0.973334  \n",
       "Epsilon        0.750364  0.834830  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pathway_activity_by_celltype = (\n",
    "    pathway_activity.assign(clusterid=pathway_activity[\"clusters\"].replace({\n",
    "        \"Ngn3 low EP\": \"Ngn3Low\",\n",
    "        \"Ngn3 high EP\": \"Ngn3High\",\n",
    "    }))\n",
    "    .groupby(\"clusterid\", observed=True)[\"pathway_activity\"]\n",
    "    .describe()\n",
    "    .reindex(CELLTYPE_ORDER)\n",
    ")\n",
    "pathway_activity_by_celltype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "818a8109",
   "metadata": {},
   "source": [
    "## Type-Specific TF Discovery\n",
    "\n",
    "`scanpy.tl.rank_genes_groups` is run on the attention-derived regulator score matrix. Without `groups` or `reference`, Scanpy ranks each cell type against the rest of the dataset. The Beta list is then sorted by `logfoldchanges` in the Beta column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0bcdc041",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T10:15:08.490412Z",
     "iopub.status.busy": "2026-05-17T10:15:08.490248Z",
     "iopub.status.idle": "2026-05-17T10:15:08.510049Z",
     "shell.execute_reply": "2026-05-17T10:15:08.509129Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 3696 × 1791\n",
       "    obs: 'clusterid', 'cell_type'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attention_scores = attention.copy()\n",
    "attention_scores.obs[\"clusterid\"] = pd.Categorical(\n",
    "    attention_scores.obs[\"clusterid\"].astype(str),\n",
    "    categories=RANK_GROUP_ORDER,\n",
    "    ordered=True,\n",
    ")\n",
    "attention_scores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fc8f8bb2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T10:15:08.513666Z",
     "iopub.status.busy": "2026-05-17T10:15:08.513510Z",
     "iopub.status.idle": "2026-05-17T10:15:11.821657Z",
     "shell.execute_reply": "2026-05-17T10:15:11.820750Z"
    }
   },
   "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>regulator</th>\n",
       "      <th>scores</th>\n",
       "      <th>logfoldchanges</th>\n",
       "      <th>pvals</th>\n",
       "      <th>pvals_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>INS2</td>\n",
       "      <td>14.850327</td>\n",
       "      <td>143.993057</td>\n",
       "      <td>6.923081e-50</td>\n",
       "      <td>6.525915e-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>HMGN3</td>\n",
       "      <td>9.773632</td>\n",
       "      <td>43.448711</td>\n",
       "      <td>1.461177e-22</td>\n",
       "      <td>5.406959e-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IAPP</td>\n",
       "      <td>29.016233</td>\n",
       "      <td>20.586931</td>\n",
       "      <td>4.106259e-185</td>\n",
       "      <td>5.253079e-183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>FOS</td>\n",
       "      <td>9.836802</td>\n",
       "      <td>15.942451</td>\n",
       "      <td>7.815558e-23</td>\n",
       "      <td>2.928382e-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PDX1</td>\n",
       "      <td>33.795540</td>\n",
       "      <td>13.940424</td>\n",
       "      <td>2.293330e-250</td>\n",
       "      <td>1.026839e-247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NNAT</td>\n",
       "      <td>37.057472</td>\n",
       "      <td>12.705257</td>\n",
       "      <td>1.361306e-300</td>\n",
       "      <td>1.219049e-297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>GNAS</td>\n",
       "      <td>15.891160</td>\n",
       "      <td>11.971339</td>\n",
       "      <td>7.296437e-57</td>\n",
       "      <td>8.066617e-56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>TTR</td>\n",
       "      <td>18.748365</td>\n",
       "      <td>6.740270</td>\n",
       "      <td>1.996353e-78</td>\n",
       "      <td>3.471329e-77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>DYNLL1</td>\n",
       "      <td>18.920925</td>\n",
       "      <td>6.352026</td>\n",
       "      <td>7.669524e-80</td>\n",
       "      <td>1.360012e-78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TUBA1A</td>\n",
       "      <td>28.342644</td>\n",
       "      <td>6.172879</td>\n",
       "      <td>1.031334e-176</td>\n",
       "      <td>1.154449e-174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>CALM2</td>\n",
       "      <td>14.071762</td>\n",
       "      <td>5.577945</td>\n",
       "      <td>5.664064e-45</td>\n",
       "      <td>4.611063e-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>CALR</td>\n",
       "      <td>18.922230</td>\n",
       "      <td>5.554480</td>\n",
       "      <td>7.482122e-80</td>\n",
       "      <td>1.340048e-78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>REST</td>\n",
       "      <td>11.216324</td>\n",
       "      <td>5.474579</td>\n",
       "      <td>3.390754e-29</td>\n",
       "      <td>1.668363e-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PYY</td>\n",
       "      <td>25.685898</td>\n",
       "      <td>5.027620</td>\n",
       "      <td>1.680221e-145</td>\n",
       "      <td>9.403985e-144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HSPA5</td>\n",
       "      <td>29.335363</td>\n",
       "      <td>4.633273</td>\n",
       "      <td>3.673140e-189</td>\n",
       "      <td>5.482162e-187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SEC61B</td>\n",
       "      <td>34.085316</td>\n",
       "      <td>3.526182</td>\n",
       "      <td>1.217431e-254</td>\n",
       "      <td>7.268060e-252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>RBP4</td>\n",
       "      <td>24.131279</td>\n",
       "      <td>3.064357</td>\n",
       "      <td>1.174199e-128</td>\n",
       "      <td>5.257477e-127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>EZH2</td>\n",
       "      <td>4.902114</td>\n",
       "      <td>3.058743</td>\n",
       "      <td>9.481059e-07</td>\n",
       "      <td>1.343400e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>IGSF3</td>\n",
       "      <td>19.666351</td>\n",
       "      <td>2.923172</td>\n",
       "      <td>4.188503e-86</td>\n",
       "      <td>8.066245e-85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>ARRDC4</td>\n",
       "      <td>2.672083</td>\n",
       "      <td>2.800261</td>\n",
       "      <td>7.538204e-03</td>\n",
       "      <td>8.501841e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>CHGB</td>\n",
       "      <td>21.437046</td>\n",
       "      <td>2.755008</td>\n",
       "      <td>6.032355e-102</td>\n",
       "      <td>1.688117e-100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TRIM47</td>\n",
       "      <td>38.424515</td>\n",
       "      <td>2.703547</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>TTYH1</td>\n",
       "      <td>23.168863</td>\n",
       "      <td>2.621251</td>\n",
       "      <td>9.384674e-119</td>\n",
       "      <td>3.735100e-117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DLK1</td>\n",
       "      <td>32.852322</td>\n",
       "      <td>2.585310</td>\n",
       "      <td>1.055309e-236</td>\n",
       "      <td>3.780118e-234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MOSPD1</td>\n",
       "      <td>32.460712</td>\n",
       "      <td>2.560460</td>\n",
       "      <td>3.824583e-231</td>\n",
       "      <td>1.141638e-228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>CCND1</td>\n",
       "      <td>16.668463</td>\n",
       "      <td>2.276447</td>\n",
       "      <td>2.222348e-62</td>\n",
       "      <td>2.802976e-61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RAP1B</td>\n",
       "      <td>26.055569</td>\n",
       "      <td>2.129719</td>\n",
       "      <td>1.163440e-149</td>\n",
       "      <td>7.185247e-148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>GNG12</td>\n",
       "      <td>25.764826</td>\n",
       "      <td>1.664000</td>\n",
       "      <td>2.198992e-146</td>\n",
       "      <td>1.270450e-144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PDIA6</td>\n",
       "      <td>27.347610</td>\n",
       "      <td>1.624670</td>\n",
       "      <td>1.152940e-164</td>\n",
       "      <td>9.832929e-163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>TESC</td>\n",
       "      <td>5.508282</td>\n",
       "      <td>1.508494</td>\n",
       "      <td>3.623519e-08</td>\n",
       "      <td>5.653068e-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    regulator     scores  logfoldchanges          pvals      pvals_adj\n",
       "78       INS2  14.850327      143.993057   6.923081e-50   6.525915e-49\n",
       "128     HMGN3   9.773632       43.448711   1.461177e-22   5.406959e-22\n",
       "8        IAPP  29.016233       20.586931  4.106259e-185  5.253079e-183\n",
       "127       FOS   9.836802       15.942451   7.815558e-23   2.928382e-22\n",
       "3        PDX1  33.795540       13.940424  2.293330e-250  1.026839e-247\n",
       "1        NNAT  37.057472       12.705257  1.361306e-300  1.219049e-297\n",
       "68       GNAS  15.891160       11.971339   7.296437e-57   8.066617e-56\n",
       "50        TTR  18.748365        6.740270   1.996353e-78   3.471329e-77\n",
       "48     DYNLL1  18.920925        6.352026   7.669524e-80   1.360012e-78\n",
       "9      TUBA1A  28.342644        6.172879  1.031334e-176  1.154449e-174\n",
       "83      CALM2  14.071762        5.577945   5.664064e-45   4.611063e-44\n",
       "47       CALR  18.922230        5.554480   7.482122e-80   1.340048e-78\n",
       "114      REST  11.216324        5.474579   3.390754e-29   1.668363e-28\n",
       "18        PYY  25.685898        5.027620  1.680221e-145  9.403985e-144\n",
       "6       HSPA5  29.335363        4.633273  3.673140e-189  5.482162e-187\n",
       "2      SEC61B  34.085316        3.526182  1.217431e-254  7.268060e-252\n",
       "22       RBP4  24.131279        3.064357  1.174199e-128  5.257477e-127\n",
       "198      EZH2   4.902114        3.058743   9.481059e-07   1.343400e-06\n",
       "45      IGSF3  19.666351        2.923172   4.188503e-86   8.066245e-85\n",
       "236    ARRDC4   2.672083        2.800261   7.538204e-03   8.501841e-03\n",
       "35       CHGB  21.437046        2.755008  6.032355e-102  1.688117e-100\n",
       "0      TRIM47  38.424515        2.703547   0.000000e+00   0.000000e+00\n",
       "25      TTYH1  23.168863        2.621251  9.384674e-119  3.735100e-117\n",
       "4        DLK1  32.852322        2.585310  1.055309e-236  3.780118e-234\n",
       "5      MOSPD1  32.460712        2.560460  3.824583e-231  1.141638e-228\n",
       "62      CCND1  16.668463        2.276447   2.222348e-62   2.802976e-61\n",
       "16      RAP1B  26.055569        2.129719  1.163440e-149  7.185247e-148\n",
       "17      GNG12  25.764826        1.664000  2.198992e-146  1.270450e-144\n",
       "12      PDIA6  27.347610        1.624670  1.152940e-164  9.832929e-163\n",
       "189      TESC   5.508282        1.508494   3.623519e-08   5.653068e-08"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>group_index</th>\n",
       "      <th>group_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Ngn3Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Beta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Alpha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Ductal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Ngn3High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Epsilon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Pre-endocrine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group_index     group_name\n",
       "0            0        Ngn3Low\n",
       "1            1           Beta\n",
       "2            2          Alpha\n",
       "3            3         Ductal\n",
       "4            4       Ngn3High\n",
       "5            5          Delta\n",
       "6            6        Epsilon\n",
       "7            7  Pre-endocrine"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run Scanpy differential ranking on the attention score matrix.\n",
    "sc.tl.rank_genes_groups(attention_scores, \"clusterid\", method=\"t-test\", key_added=\"t-test\", use_raw=False)\n",
    "sc.tl.rank_genes_groups(\n",
    "    attention_scores,\n",
    "    \"clusterid\",\n",
    "    method=\"t-test_overestim_var\",\n",
    "    key_added=\"t-test_ov\",\n",
    "    use_raw=False,\n",
    ")\n",
    "sc.tl.rank_genes_groups(\n",
    "    attention_scores,\n",
    "    \"clusterid\",\n",
    "    method=\"wilcoxon\",\n",
    "    key_added=\"wilcoxon\",\n",
    "    use_raw=False,\n",
    "    tie_correct=True,\n",
    ")\n",
    "\n",
    "beta_one_vs_rest = sc.get.rank_genes_groups_df(attention_scores, group=\"Beta\", key=\"wilcoxon\")\n",
    "beta_one_vs_rest = beta_one_vs_rest.rename(columns={\"names\": \"regulator\"})\n",
    "beta_one_vs_rest.to_csv(FIGURE_DIR / \"beta_regulators_scanpy_wilcoxon_one_vs_rest.csv\", index=False)\n",
    "beta_specific_tfs = (\n",
    "    beta_one_vs_rest.query(\"pvals_adj <= 0.05 and scores > 0\")\n",
    "    .sort_values(\"logfoldchanges\", ascending=False)\n",
    "    .head(30)\n",
    ")\n",
    "beta_specific_tfs.to_csv(FIGURE_DIR / \"beta_specific_tfs_vs_rest_top30.csv\", index=False)\n",
    "display(beta_specific_tfs)\n",
    "\n",
    "# Positional extraction from adata.uns[\"wilcoxon\"].\n",
    "# With RANK_GROUP_ORDER, index 0 is Ngn3Low and index 1 is Beta.\n",
    "rank_groups_by_position = pd.DataFrame({\n",
    "    \"group_index\": range(len(attention_scores.uns[\"wilcoxon\"][\"names\"].dtype.names)),\n",
    "    \"group_name\": list(attention_scores.uns[\"wilcoxon\"][\"names\"].dtype.names),\n",
    "})\n",
    "display(rank_groups_by_position)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "599e79e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T10:15:11.826284Z",
     "iopub.status.busy": "2026-05-17T10:15:11.826097Z",
     "iopub.status.idle": "2026-05-17T10:15:12.027343Z",
     "shell.execute_reply": "2026-05-17T10:15:12.026233Z"
    }
   },
   "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>regulator</th>\n",
       "      <th>scores</th>\n",
       "      <th>logfoldchanges</th>\n",
       "      <th>pvals</th>\n",
       "      <th>pvals_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>HMGN3</td>\n",
       "      <td>18.885239</td>\n",
       "      <td>296.303192</td>\n",
       "      <td>1.508519e-79</td>\n",
       "      <td>2.059267e-79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>INS2</td>\n",
       "      <td>3.448934</td>\n",
       "      <td>138.319168</td>\n",
       "      <td>5.628047e-04</td>\n",
       "      <td>5.870607e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>FOS</td>\n",
       "      <td>13.857791</td>\n",
       "      <td>31.156979</td>\n",
       "      <td>1.141325e-43</td>\n",
       "      <td>1.384900e-43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GNAS</td>\n",
       "      <td>23.291525</td>\n",
       "      <td>28.969370</td>\n",
       "      <td>5.402930e-120</td>\n",
       "      <td>5.625958e-119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>HSPA8</td>\n",
       "      <td>12.441044</td>\n",
       "      <td>26.564337</td>\n",
       "      <td>1.564450e-35</td>\n",
       "      <td>1.855583e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>IAPP</td>\n",
       "      <td>19.079844</td>\n",
       "      <td>25.832048</td>\n",
       "      <td>3.713747e-81</td>\n",
       "      <td>5.108541e-81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>NNAT</td>\n",
       "      <td>14.330282</td>\n",
       "      <td>25.613579</td>\n",
       "      <td>1.415450e-46</td>\n",
       "      <td>1.737539e-46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>ACTB</td>\n",
       "      <td>6.168558</td>\n",
       "      <td>20.965637</td>\n",
       "      <td>6.891570e-10</td>\n",
       "      <td>7.426475e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TRIM47</td>\n",
       "      <td>23.308910</td>\n",
       "      <td>18.749823</td>\n",
       "      <td>3.600560e-120</td>\n",
       "      <td>4.763027e-119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MOSPD1</td>\n",
       "      <td>20.607502</td>\n",
       "      <td>18.563549</td>\n",
       "      <td>2.350561e-94</td>\n",
       "      <td>3.502375e-94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>TTYH1</td>\n",
       "      <td>14.201574</td>\n",
       "      <td>17.875153</td>\n",
       "      <td>8.957740e-46</td>\n",
       "      <td>1.094360e-45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>IGSF3</td>\n",
       "      <td>10.795223</td>\n",
       "      <td>17.185690</td>\n",
       "      <td>3.625813e-27</td>\n",
       "      <td>4.194981e-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>TESC</td>\n",
       "      <td>8.093443</td>\n",
       "      <td>16.907015</td>\n",
       "      <td>5.800141e-16</td>\n",
       "      <td>6.424275e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SLC25A5</td>\n",
       "      <td>23.297548</td>\n",
       "      <td>15.379690</td>\n",
       "      <td>4.694245e-120</td>\n",
       "      <td>5.333300e-119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CALM2</td>\n",
       "      <td>22.624271</td>\n",
       "      <td>15.265243</td>\n",
       "      <td>2.500639e-113</td>\n",
       "      <td>4.937866e-113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PDX1</td>\n",
       "      <td>20.002249</td>\n",
       "      <td>13.592208</td>\n",
       "      <td>5.264365e-89</td>\n",
       "      <td>7.622051e-89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>H3F3B</td>\n",
       "      <td>10.714921</td>\n",
       "      <td>11.396209</td>\n",
       "      <td>8.663157e-27</td>\n",
       "      <td>9.977951e-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>TTR</td>\n",
       "      <td>18.488804</td>\n",
       "      <td>11.260492</td>\n",
       "      <td>2.541307e-76</td>\n",
       "      <td>3.437675e-76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PYY</td>\n",
       "      <td>23.327314</td>\n",
       "      <td>9.836381</td>\n",
       "      <td>2.342355e-120</td>\n",
       "      <td>4.763027e-119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CALR</td>\n",
       "      <td>19.413620</td>\n",
       "      <td>9.730735</td>\n",
       "      <td>5.920498e-84</td>\n",
       "      <td>8.297035e-84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>DYNLL1</td>\n",
       "      <td>17.297688</td>\n",
       "      <td>9.164405</td>\n",
       "      <td>4.896505e-67</td>\n",
       "      <td>6.401197e-67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CHGB</td>\n",
       "      <td>23.228565</td>\n",
       "      <td>7.888491</td>\n",
       "      <td>2.343163e-119</td>\n",
       "      <td>1.182142e-118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TUBA1A</td>\n",
       "      <td>22.203434</td>\n",
       "      <td>7.789117</td>\n",
       "      <td>3.181911e-109</td>\n",
       "      <td>5.495470e-109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RBP4</td>\n",
       "      <td>23.322552</td>\n",
       "      <td>7.134286</td>\n",
       "      <td>2.618085e-120</td>\n",
       "      <td>4.763027e-119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>HSP90AA1</td>\n",
       "      <td>11.821387</td>\n",
       "      <td>6.193028</td>\n",
       "      <td>3.026461e-32</td>\n",
       "      <td>3.566047e-32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HSPA5</td>\n",
       "      <td>22.011541</td>\n",
       "      <td>5.822803</td>\n",
       "      <td>2.232686e-107</td>\n",
       "      <td>3.723222e-107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>SLC38A5</td>\n",
       "      <td>10.572734</td>\n",
       "      <td>5.474253</td>\n",
       "      <td>3.986998e-26</td>\n",
       "      <td>4.580317e-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SEC61B</td>\n",
       "      <td>23.166809</td>\n",
       "      <td>5.219037</td>\n",
       "      <td>9.842641e-119</td>\n",
       "      <td>3.497653e-118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>CHGA</td>\n",
       "      <td>16.809673</td>\n",
       "      <td>5.003044</td>\n",
       "      <td>2.073093e-63</td>\n",
       "      <td>2.682738e-63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RAP1B</td>\n",
       "      <td>23.321348</td>\n",
       "      <td>3.523076</td>\n",
       "      <td>2.692841e-120</td>\n",
       "      <td>4.763027e-119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    regulator     scores  logfoldchanges          pvals      pvals_adj\n",
       "33      HMGN3  18.885239      296.303192   1.508519e-79   2.059267e-79\n",
       "144      INS2   3.448934      138.319168   5.628047e-04   5.870607e-04\n",
       "53        FOS  13.857791       31.156979   1.141325e-43   1.384900e-43\n",
       "6        GNAS  23.291525       28.969370  5.402930e-120  5.625958e-119\n",
       "64      HSPA8  12.441044       26.564337   1.564450e-35   1.855583e-35\n",
       "31       IAPP  19.079844       25.832048   3.713747e-81   5.108541e-81\n",
       "50       NNAT  14.330282       25.613579   1.415450e-46   1.737539e-46\n",
       "115      ACTB   6.168558       20.965637   6.891570e-10   7.426475e-10\n",
       "4      TRIM47  23.308910       18.749823  3.600560e-120  4.763027e-119\n",
       "24     MOSPD1  20.607502       18.563549   2.350561e-94   3.502375e-94\n",
       "51      TTYH1  14.201574       17.875153   8.957740e-46   1.094360e-45\n",
       "75      IGSF3  10.795223       17.185690   3.625813e-27   4.194981e-27\n",
       "99       TESC   8.093443       16.907015   5.800141e-16   6.424275e-16\n",
       "5     SLC25A5  23.297548       15.379690  4.694245e-120  5.333300e-119\n",
       "15      CALM2  22.624271       15.265243  2.500639e-113  4.937866e-113\n",
       "27       PDX1  20.002249       13.592208   5.264365e-89   7.622051e-89\n",
       "79      H3F3B  10.714921       11.396209   8.663157e-27   9.977951e-27\n",
       "34        TTR  18.488804       11.260492   2.541307e-76   3.437675e-76\n",
       "0         PYY  23.327314        9.836381  2.342355e-120  4.763027e-119\n",
       "29       CALR  19.413620        9.730735   5.920498e-84   8.297035e-84\n",
       "37     DYNLL1  17.297688        9.164405   4.896505e-67   6.401197e-67\n",
       "8        CHGB  23.228565        7.888491  2.343163e-119  1.182142e-118\n",
       "16     TUBA1A  22.203434        7.789117  3.181911e-109  5.495470e-109\n",
       "1        RBP4  23.322552        7.134286  2.618085e-120  4.763027e-119\n",
       "68   HSP90AA1  11.821387        6.193028   3.026461e-32   3.566047e-32\n",
       "17      HSPA5  22.011541        5.822803  2.232686e-107  3.723222e-107\n",
       "80    SLC38A5  10.572734        5.474253   3.986998e-26   4.580317e-26\n",
       "10     SEC61B  23.166809        5.219037  9.842641e-119  3.497653e-118\n",
       "39       CHGA  16.809673        5.003044   2.073093e-63   2.682738e-63\n",
       "2       RAP1B  23.321348        3.523076  2.692841e-120  4.763027e-119"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explicit pairwise comparison: Beta versus Ngn3Low.\n",
    "pairwise = attention_scores[attention_scores.obs[\"clusterid\"].isin([\"Beta\", \"Ngn3Low\"])].copy()\n",
    "pairwise.obs[\"clusterid\"] = pd.Categorical(\n",
    "    pairwise.obs[\"clusterid\"].astype(str),\n",
    "    categories=[\"Ngn3Low\", \"Beta\"],\n",
    "    ordered=True,\n",
    ")\n",
    "\n",
    "sc.tl.rank_genes_groups(\n",
    "    pairwise,\n",
    "    \"clusterid\",\n",
    "    groups=[\"Beta\"],\n",
    "    reference=\"Ngn3Low\",\n",
    "    method=\"wilcoxon\",\n",
    "    key_added=\"wilcoxon_beta_vs_ngn3low\",\n",
    "    use_raw=False,\n",
    "    tie_correct=True,\n",
    ")\n",
    "\n",
    "beta_vs_ngn3low = sc.get.rank_genes_groups_df(pairwise, group=\"Beta\", key=\"wilcoxon_beta_vs_ngn3low\")\n",
    "beta_vs_ngn3low = beta_vs_ngn3low.rename(columns={\"names\": \"regulator\"})\n",
    "beta_vs_ngn3low.to_csv(FIGURE_DIR / \"beta_vs_ngn3low_scanpy_wilcoxon.csv\", index=False)\n",
    "\n",
    "beta_vs_ngn3low_top30 = (\n",
    "    beta_vs_ngn3low.query(\"pvals_adj <= 0.05 and scores > 0\")\n",
    "    .sort_values(\"logfoldchanges\", ascending=False)\n",
    "    .head(30)\n",
    ")\n",
    "beta_vs_ngn3low_top30.to_csv(FIGURE_DIR / \"beta_specific_tfs_vs_ngn3low_top30.csv\", index=False)\n",
    "display(beta_vs_ngn3low_top30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cec08ee7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-17T10:15:12.030866Z",
     "iopub.status.busy": "2026-05-17T10:15:12.030706Z",
     "iopub.status.idle": "2026-05-17T10:15:12.418057Z",
     "shell.execute_reply": "2026-05-17T10:15:12.417355Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 680x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_to_plot = beta_specific_tfs.head(20).iloc[::-1]\n",
    "fig, ax = plt.subplots(figsize=(6.8, 5.2))\n",
    "ax.barh(top_to_plot[\"regulator\"], top_to_plot[\"logfoldchanges\"], color=\"#2C7FB8\")\n",
    "ax.set_xlabel(\"Log fold change: Beta vs rest\")\n",
    "ax.set_ylabel(\"\")\n",
    "ax.set_title(\"Beta-Specific Regulators vs Rest\", fontsize=13, weight=\"bold\", pad=10)\n",
    "ax.grid(axis=\"x\", color=\"#E6E6E6\", linewidth=0.7)\n",
    "ax.set_axisbelow(True)\n",
    "for spine in [\"top\", \"right\"]:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURE_DIR / \"beta_specific_tfs_vs_rest.png\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TF Top Target Genes\n",
    "\n",
    "Cell-type mean attention networks can also be used to inspect the strongest target genes for a selected regulator. The example below uses the Beta mean attention network exported from the provided stage-1 pancreas checkpoint and retrieves the top 25 targets of PDX1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>regulator</th>\n",
       "      <th>target_gene</th>\n",
       "      <th>target_gene_upper</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Sphkap</td>\n",
       "      <td>SPHKAP</td>\n",
       "      <td>0.377538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Pclo</td>\n",
       "      <td>PCLO</td>\n",
       "      <td>0.360077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Sntg1</td>\n",
       "      <td>SNTG1</td>\n",
       "      <td>0.326232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>St18</td>\n",
       "      <td>ST18</td>\n",
       "      <td>0.260459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Prdm16</td>\n",
       "      <td>PRDM16</td>\n",
       "      <td>0.254502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Rfx6</td>\n",
       "      <td>RFX6</td>\n",
       "      <td>0.214374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Pnliprp1</td>\n",
       "      <td>PNLIPRP1</td>\n",
       "      <td>0.205907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Chst9</td>\n",
       "      <td>CHST9</td>\n",
       "      <td>0.200043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Mtss1</td>\n",
       "      <td>MTSS1</td>\n",
       "      <td>0.191846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Efcab1</td>\n",
       "      <td>EFCAB1</td>\n",
       "      <td>0.177671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Nkx2-2</td>\n",
       "      <td>NKX2-2</td>\n",
       "      <td>0.176846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Ttyh1</td>\n",
       "      <td>TTYH1</td>\n",
       "      <td>0.173887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Aff2</td>\n",
       "      <td>AFF2</td>\n",
       "      <td>0.170449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Papss2</td>\n",
       "      <td>PAPSS2</td>\n",
       "      <td>0.168306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Myt1l</td>\n",
       "      <td>MYT1L</td>\n",
       "      <td>0.164107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Tnr</td>\n",
       "      <td>TNR</td>\n",
       "      <td>0.164032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Emb</td>\n",
       "      <td>EMB</td>\n",
       "      <td>0.150766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Pyy</td>\n",
       "      <td>PYY</td>\n",
       "      <td>0.150329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Iapp</td>\n",
       "      <td>IAPP</td>\n",
       "      <td>0.150007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Rpgrip1</td>\n",
       "      <td>RPGRIP1</td>\n",
       "      <td>0.139450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Gstt2</td>\n",
       "      <td>GSTT2</td>\n",
       "      <td>0.138903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Npas3</td>\n",
       "      <td>NPAS3</td>\n",
       "      <td>0.138548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Habp2</td>\n",
       "      <td>HABP2</td>\n",
       "      <td>0.138320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Gcnt2</td>\n",
       "      <td>GCNT2</td>\n",
       "      <td>0.137287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Pdx1</td>\n",
       "      <td>Pax4</td>\n",
       "      <td>PAX4</td>\n",
       "      <td>0.136594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "    rank regulator target_gene target_gene_upper    weight\n",
       "0      1      Pdx1      Sphkap            SPHKAP  0.377538\n",
       "1      2      Pdx1        Pclo              PCLO  0.360077\n",
       "2      3      Pdx1       Sntg1             SNTG1  0.326232\n",
       "3      4      Pdx1        St18              ST18  0.260459\n",
       "4      5      Pdx1      Prdm16            PRDM16  0.254502\n",
       "5      6      Pdx1        Rfx6              RFX6  0.214374\n",
       "6      7      Pdx1    Pnliprp1          PNLIPRP1  0.205907\n",
       "7      8      Pdx1       Chst9             CHST9  0.200043\n",
       "8      9      Pdx1       Mtss1             MTSS1  0.191846\n",
       "9     10      Pdx1      Efcab1            EFCAB1  0.177671\n",
       "10    11      Pdx1      Nkx2-2            NKX2-2  0.176846\n",
       "11    12      Pdx1       Ttyh1             TTYH1  0.173887\n",
       "12    13      Pdx1        Aff2              AFF2  0.170449\n",
       "13    14      Pdx1      Papss2            PAPSS2  0.168306\n",
       "14    15      Pdx1       Myt1l             MYT1L  0.164107\n",
       "15    16      Pdx1         Tnr               TNR  0.164032\n",
       "16    17      Pdx1         Emb               EMB  0.150766\n",
       "17    18      Pdx1         Pyy               PYY  0.150329\n",
       "18    19      Pdx1        Iapp              IAPP  0.150007\n",
       "19    20      Pdx1     Rpgrip1           RPGRIP1  0.139450\n",
       "20    21      Pdx1       Gstt2             GSTT2  0.138903\n",
       "21    22      Pdx1       Npas3             NPAS3  0.138548\n",
       "22    23      Pdx1       Habp2             HABP2  0.138320\n",
       "23    24      Pdx1       Gcnt2             GCNT2  0.137287\n",
       "24    25      Pdx1        Pax4              PAX4  0.136594"
      ]
     }
    }
   ],
   "source": [
    "reference_genes = [line.strip().upper() for line in GENE_ORDER.read_text().splitlines() if line.strip()]\n",
    "gene_to_idx = {gene: idx for idx, gene in enumerate(reference_genes)}\n",
    "\n",
    "beta_mean_attention = sparse.load_npz(BETA_MEAN_ATTENTION_NPZ).toarray()\n",
    "if beta_mean_attention.shape != (len(reference_genes), len(reference_genes)):\n",
    "    raise ValueError(\n",
    "        f\"Beta attention matrix shape {beta_mean_attention.shape} does not match gene order length {len(reference_genes)}\"\n",
    "    )\n",
    "\n",
    "def rank_tf_targets_from_mean_attention(matrix, genes, tf_gene, top_k=25):\n",
    "    tf_key = tf_gene.upper()\n",
    "    if tf_key not in gene_to_idx:\n",
    "        raise KeyError(f\"{tf_gene} is not present in the reference gene order\")\n",
    "    tf_idx = gene_to_idx[tf_key]\n",
    "    weights = np.asarray(matrix[:, tf_idx], dtype=float)\n",
    "    order = np.argsort(-weights)\n",
    "    rows = []\n",
    "    for target_idx in order:\n",
    "        target = genes[target_idx]\n",
    "        if target == tf_key:\n",
    "            continue\n",
    "        rows.append({\n",
    "            \"rank\": len(rows) + 1,\n",
    "            \"regulator\": title_case_gene(tf_key),\n",
    "            \"target_gene\": title_case_gene(target),\n",
    "            \"target_gene_upper\": target,\n",
    "            \"weight\": weights[target_idx],\n",
    "        })\n",
    "        if len(rows) >= top_k:\n",
    "            break\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "pdx1_beta_top25 = rank_tf_targets_from_mean_attention(beta_mean_attention, reference_genes, \"PDX1\", top_k=25)\n",
    "pdx1_beta_top25.to_csv(FIGURE_DIR / \"beta_pdx1_top25_targets.csv\", index=False)\n",
    "display(pdx1_beta_top25)\n"
   ]
  }
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