{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "634137d5",
   "metadata": {},
   "source": [
    "# scCS Tutorial — Multi-Condition Analysis with `MultiScorer`\n",
    "\n",
    "This tutorial demonstrates `MultiScorer` — the scCS class for analyses with **3 or more conditions** — on a real-data illustrative example: the **scVelo pancreatic endocrinogenesis** dataset stratified by per-cell RNA-velocity magnitude into **three tertiles** (`low_velocity`, `med_velocity`, `high_velocity`).\n",
    "\n",
    "> The velocity-tertile split is an **illustrative stratification**, not a biological perturbation. In a real experiment, `condition_obs_key` would point to actual experimental conditions (e.g., `drug`, `genotype`, `timepoint`). The downstream patterns shown here — per-condition driver discovery, pathway enrichment, expression trends — work identically on real biological condition labels.\n",
    "\n",
    "### What's covered\n",
    "1. Loading pancreas data and creating the 3-way velocity-tertile split\n",
    "2. Initializing `MultiScorer` and fitting the shared embedding\n",
    "3. **Tier 1** — Score all conditions on the shared embedding\n",
    "4. **Tier 2** — Omnibus tests (Kruskal-Wallis / ANOVA) + post-hoc pairwise comparisons\n",
    "5. **Tier 3** — Mixed-effects models with reference contrasts\n",
    "6. Standard multi-condition visualizations\n",
    "7. **Driver genes per condition** (velocity + DEG)\n",
    "8. **Pathway enrichment per condition**\n",
    "9. **Expression trends along fate arms per condition**\n",
    "10. **Transfer per-cell labels back to the full AnnData**\n",
    "\n",
    "For 2-condition comparisons use `PairScorer` (see `scCS_tutorial_pairwise.ipynb`).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8af078ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scCS version: 0.7.4\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import warnings, numpy as np, pandas as pd\n",
    "import anndata as ad\n",
    "import scanpy as sc, scvelo as scv\n",
    "import matplotlib.pyplot as plt\n",
    "import scCS\n",
    "\n",
    "from scCS.drivers import (\n",
    "    get_velocity_drivers,\n",
    "    get_deg_drivers,\n",
    "    get_velocity_fate_drivers,\n",
    ")\n",
    "from scCS.enrichment import run_enrichment_per_fate\n",
    "from scCS.plot import plot_expression_trends\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "try:\n",
    "    from statsmodels.tools.sm_exceptions import ConvergenceWarning\n",
    "    warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n",
    "except ImportError:\n",
    "    pass\n",
    "pd.set_option(\"display.max_rows\", 25)\n",
    "pd.set_option(\"display.max_columns\", 15)\n",
    "pd.set_option(\"display.width\", 160)\n",
    "\n",
    "print(f\"scCS version: {scCS.__version__}\")\n",
    "\n",
    "\n",
    "# -------- compact display helpers for multi-condition tutorials --------\n",
    "def _stack_drivers_by_condition(driver_dicts, n_per_fate=5, cols=None):\n",
    "    \"\"\"{cond: {fate: DataFrame}}  ->  one long table (condition, fate, ...).\"\"\"\n",
    "    out = []\n",
    "    for cond, fate_dict in driver_dicts.items():\n",
    "        for fate, df in fate_dict.items():\n",
    "            if df is None or len(df) == 0:\n",
    "                continue\n",
    "            block = df.head(n_per_fate).copy()\n",
    "            block.insert(0, \"fate\", fate)\n",
    "            block.insert(0, \"condition\", cond)\n",
    "            out.append(block)\n",
    "    if not out:\n",
    "        return pd.DataFrame()\n",
    "    big = pd.concat(out, ignore_index=True)\n",
    "    if cols is not None:\n",
    "        keep = [\"condition\", \"fate\"] + [c for c in cols if c in big.columns]\n",
    "        big = big[keep]\n",
    "    return big\n",
    "\n",
    "\n",
    "def _stack_enrichment_by_condition(enrichment_dicts, n_per_group=3):\n",
    "    \"\"\"{cond: {fate: {direction: df}}}  ->  one long table.\"\"\"\n",
    "    rows = []\n",
    "    for cond, fate_results in enrichment_dicts.items():\n",
    "        for fate, dir_results in fate_results.items():\n",
    "            for direction in (\"up\", \"down\"):\n",
    "                df = dir_results.get(direction, None)\n",
    "                if df is None or df.empty:\n",
    "                    continue\n",
    "                top = df.head(n_per_group).copy()\n",
    "                top.insert(0, \"direction\", direction)\n",
    "                top.insert(0, \"fate\", fate)\n",
    "                top.insert(0, \"condition\", cond)\n",
    "                rows.append(top)\n",
    "    if not rows:\n",
    "        return pd.DataFrame()\n",
    "    return pd.concat(rows, ignore_index=True)\n",
    "\n",
    "\n",
    "def _driver_overlap(per_cond_drivers, n_top=10, gene_col=\"gene\"):\n",
    "    \"\"\"Build a fate x gene x condition presence matrix from {cond: {fate: df}}.\"\"\"\n",
    "    rows = []\n",
    "    conds = list(per_cond_drivers.keys())\n",
    "    if not conds:\n",
    "        return pd.DataFrame()\n",
    "    fates = list(next(iter(per_cond_drivers.values())).keys())\n",
    "    for fate in fates:\n",
    "        sets = {}\n",
    "        for cond in conds:\n",
    "            df = per_cond_drivers[cond].get(fate)\n",
    "            if df is None or len(df) == 0 or gene_col not in df.columns:\n",
    "                sets[cond] = set()\n",
    "            else:\n",
    "                sets[cond] = set(df.head(n_top)[gene_col].astype(str))\n",
    "        all_genes = sorted(set.union(*sets.values())) if sets else []\n",
    "        for g in all_genes:\n",
    "            row = {\"fate\": fate, \"gene\": g}\n",
    "            for cond in conds:\n",
    "                row[cond] = g in sets[cond]\n",
    "            row[\"n_conds\"] = sum(row[c] for c in conds)\n",
    "            row[\"shared_all\"] = row[\"n_conds\"] == len(conds)\n",
    "            rows.append(row)\n",
    "    return pd.DataFrame(rows)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff04a01b",
   "metadata": {},
   "source": [
    "## 1. Load pancreas data and build the 3-way velocity-tertile split\n",
    "\n",
    "We use the same dataset as the pairwise tutorial (`scvelo.datasets.pancreas`) but split cells into **three** groups based on per-cell RNA-velocity magnitude tertiles instead of a median split. This gives us 3 conditions on which to exercise `MultiScorer`.\n",
    "\n",
    "\n",
    "> **Note on velocity-tertile demo:** the split is **illustrative**, not a real biological perturbation. Fates are imbalanced across tertiles (e.g., high-velocity cells are mostly Ngn3 progenitors with few terminal Alpha/Delta cells; low-velocity cells are mostly mature post-mitotic populations). Statistical tests below will pick up this structural difference. For a real condition variable (e.g., treatment, genotype) the tutorial code is identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ea785ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtered out 20801 genes that are detected 20 counts (shared).\n",
      "Normalized count data: X, spliced, unspliced.\n",
      "computing moments based on connectivities\n",
      "    finished (0:00:00) --> added \n",
      "    'Ms' and 'Mu', moments of un/spliced abundances (adata.layers)\n",
      "computing velocities\n",
      "    finished (0:00:00) --> added \n",
      "    'velocity', velocity vectors for each individual cell (adata.layers)\n",
      "computing velocity graph (using 1/24 cores)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d6860512a6f4e5c9473e4d5a55afeee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3696 [00:00<?, ?cells/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/emil/miniforge3/envs/lab-py312/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=5066) is multi-threaded, use of fork() may lead to deadlocks in the child.\n",
      "  self.pid = os.fork()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    finished (0:00:01) --> added \n",
      "    'velocity_graph', sparse matrix with cosine correlations (adata.uns)\n",
      "computing terminal states\n",
      "    identified 3 regions of root cells and 2 regions of end points .\n",
      "    finished (0:00:00) --> added\n",
      "    'root_cells', root cells of Markov diffusion process (adata.obs)\n",
      "    'end_points', end points of Markov diffusion process (adata.obs)\n",
      "AnnData object with n_obs × n_vars = 3696 × 2000\n",
      "    obs: 'clusters_coarse', 'clusters', 'S_score', 'G2M_score', 'initial_size_unspliced', 'initial_size_spliced', 'initial_size', 'n_counts', 'velocity_self_transition', 'root_cells', 'end_points', 'velocity_pseudotime', 'condition', 'sample'\n",
      "    var: 'highly_variable_genes', 'gene_count_corr', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'velocity_gamma', 'velocity_qreg_ratio', 'velocity_r2', 'velocity_genes'\n",
      "    uns: 'clusters_coarse_colors', 'clusters_colors', 'day_colors', 'neighbors', 'pca', 'hvg', 'velocity_params', 'velocity_graph', 'velocity_graph_neg'\n",
      "    obsm: 'X_pca', 'X_umap'\n",
      "    layers: 'spliced', 'unspliced', 'Ms', 'Mu', 'velocity'\n",
      "    obsp: 'distances', 'connectivities'\n",
      "\n",
      "Condition counts:\n",
      "condition\n",
      "med_velocity     1232\n",
      "high_velocity    1232\n",
      "low_velocity     1232\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Replicates per condition:\n",
      "condition\n",
      "high_velocity    3\n",
      "low_velocity     3\n",
      "med_velocity     3\n",
      "Name: sample, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Load pancreas + compute velocity\n",
    "adata = scv.datasets.pancreas()\n",
    "import inspect as _inspect\n",
    "_fn_sig = _inspect.signature(scv.pp.filter_and_normalize).parameters\n",
    "if \"n_top_genes\" in _fn_sig:\n",
    "    scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=2000)\n",
    "else:\n",
    "    # scvelo builds without n_top_genes: do filter_and_normalize then HVG.\n",
    "    # Use flavor=\"cell_ranger\" not the default \"seurat\": seurat passes\n",
    "    # int n_bins to pd.cut on log-dispersions which contain +/-inf for\n",
    "    # low-mean genes — pandas >= 2.2 rejects this with ValueError.\n",
    "    # cell_ranger uses explicit bin edges including +/-inf and is\n",
    "    # robust across pandas/py versions (scCS changelog v0.7.4).\n",
    "    scv.pp.filter_and_normalize(adata, min_shared_counts=20)\n",
    "    sc.pp.highly_variable_genes(\n",
    "        adata, n_top_genes=2000, subset=True, flavor=\"cell_ranger\"\n",
    "    )\n",
    "sc.pp.neighbors(adata)\n",
    "scv.pp.moments(adata, n_pcs=None, n_neighbors=None)\n",
    "\n",
    "# We use mode=\"deterministic\" here because it's fast (~5s) and avoids a\n",
    "# known scvelo + numpy 1.25+ scalar-conversion issue with mode=\"stochastic\".\n",
    "# For the dynamical model (slower, ~5-10 min on pancreas) instead use:\n",
    "#     scv.tl.recover_dynamics(adata)\n",
    "#     scv.tl.velocity(adata, mode=\"dynamical\")\n",
    "scv.tl.velocity(adata, mode=\"deterministic\")\n",
    "scv.tl.velocity_graph(adata)\n",
    "scv.tl.velocity_pseudotime(adata)\n",
    "\n",
    "# Per-cell velocity magnitude (NaN-safe across genes)\n",
    "velocity_matrix = adata.layers[\"velocity\"]\n",
    "vel_magnitude = np.sqrt(np.nanmean(velocity_matrix ** 2, axis=1))\n",
    "\n",
    "# 3-way tertile split\n",
    "t1, t2 = np.nanquantile(vel_magnitude, [1 / 3, 2 / 3])\n",
    "adata.obs[\"condition\"] = pd.cut(\n",
    "    vel_magnitude,\n",
    "    bins=[-np.inf, t1, t2, np.inf],\n",
    "    labels=[\"low_velocity\", \"med_velocity\", \"high_velocity\"],\n",
    ").astype(str)\n",
    "\n",
    "# Synthetic biological replicates (3 per condition) — for the mixed model later\n",
    "rng = np.random.default_rng(42)\n",
    "adata.obs[\"sample\"] = [\n",
    "    f\"{cond}_rep{rng.integers(1, 4)}\" for cond in adata.obs[\"condition\"]\n",
    "]\n",
    "\n",
    "print(adata)\n",
    "print(\"\\nCondition counts:\")\n",
    "print(adata.obs[\"condition\"].value_counts())\n",
    "print(\"\\nReplicates per condition:\")\n",
    "print(adata.obs.groupby(\"condition\", observed=True)[\"sample\"].nunique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd7900b",
   "metadata": {},
   "source": [
    "## 2. Initialize `MultiScorer`\n",
    "\n",
    "`MultiScorer` requires `>=` 3 conditions (it raises a helpful error otherwise pointing to `PairScorer` if you pass exactly 2). The `condition_obs_key` points to the column we just built. Replicate handling for Tier 3 mixed-models uses `replicate_key`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "673ea0b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[scCS] MultiScorer initialized.\n",
      "       Conditions (3): ['high_velocity', 'low_velocity', 'med_velocity']\n",
      "       Root: 'Ductal', Branches: ['Alpha', 'Beta', 'Delta', 'Epsilon']\n",
      "MultiScorer(root='Ductal', branches=['Alpha', 'Beta', 'Delta', 'Epsilon'], conditions=['high_velocity', 'low_velocity', 'med_velocity'], status='uninitialised')\n",
      "\n",
      "Conditions detected: ['high_velocity', 'low_velocity', 'med_velocity']\n"
     ]
    }
   ],
   "source": [
    "mscorer = scCS.MultiScorer(\n",
    "    adata,\n",
    "    root=\"Ductal\",\n",
    "    branches=[\"Alpha\", \"Beta\", \"Delta\", \"Epsilon\"],\n",
    "    condition_obs_key=\"condition\",\n",
    "    obs_key=\"clusters\",\n",
    ")\n",
    "print(mscorer)\n",
    "print(\"\\nConditions detected:\", mscorer.conditions)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "287ee571",
   "metadata": {},
   "source": [
    "## 3. Build the shared embedding and fit `FateMap`\n",
    "\n",
    "`MultiScorer` builds **one** star embedding on the pooled cells from all conditions, then scores each condition on that shared coordinate system. This is what makes cross-condition comparison valid: the geometry is identical.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "811b9d64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[scCS] Building SHARED embedding on pooled data (3696 cells, 3 conditions)...\n",
      "[scCS] Building star embedding: root='Ductal', k=4 fates, metric='velocity_pseudotime'\n",
      "[scCS] Subsetting: 2200 / 3696 cells kept\n",
      "       (1496 cells from other populations excluded)\n",
      "       Alpha: 481 cells (fate)\n",
      "       Beta: 591 cells (fate)\n",
      "       Delta: 70 cells (fate)\n",
      "       Ductal: 916 cells (progenitor)\n",
      "       Epsilon: 142 cells (fate)\n",
      "\n",
      "[scCS] Star embedding built → adata_sub.obsm[\"X_sccs\"] shape: (2200, 2)\n",
      "       Arm angles: {'Alpha': 0.0, 'Beta': 90.0, 'Delta': 180.0, 'Epsilon': 270.0}\n",
      "[scCS] Star embedding stored in scorer.adata_sub.obsm['X_sccs']. (2200 cells)\n",
      "[scCS] Root cluster 'Ductal': 916 cells, centroid=(-0.01, -0.00)\n",
      "[scCS]   Fate 'Alpha': 481 cells, centroid=(3.88, -0.02)\n",
      "[scCS]   Fate 'Beta': 591 cells, centroid=(-0.01, 4.98)\n",
      "[scCS]   Fate 'Delta': 70 cells, centroid=(-4.54, -0.01)\n",
      "[scCS]   Fate 'Epsilon': 142 cells, centroid=(0.04, -4.35)\n",
      "[scCS] FateMap built: k=4 fates\n",
      "[scCS] Projecting velocity via scVelo on full adata → slicing to subset...\n",
      "computing velocity embedding\n",
      "    finished (0:00:00) --> added\n",
      "    'velocity_sccs_tmp', embedded velocity vectors (adata.obsm)\n",
      "[scCS] Velocity projected. Shape: (2200, 2)\n",
      "FateMap  (root='Ductal', k=4)\n",
      "  Cluster key : 'clusters'\n",
      "  Root cells  : 916\n",
      "  Root centroid: (-0.008, -0.004)\n",
      "  Fate 0: 'Alpha'  n_cells=481  centroid=(3.88, -0.02)  arm_angle=0.0°\n",
      "  Fate 1: 'Beta'  n_cells=591  centroid=(-0.01, 4.98)  arm_angle=90.0°\n",
      "  Fate 2: 'Delta'  n_cells=70  centroid=(-4.54, -0.01)  arm_angle=180.0°\n",
      "  Fate 3: 'Epsilon'  n_cells=142  centroid=(0.04, -4.35)  arm_angle=270.0°\n",
      "Shared embedding + FateMap fit complete.\n"
     ]
    }
   ],
   "source": [
    "# Use velocity_pseudotime as the ordering metric — it was computed by\n",
    "# scv.tl.velocity_pseudotime above. We deliberately skip refit_pseudotime()\n",
    "# here: that routine calls scanpy DPT which, without prior diffmap on the\n",
    "# subset, falls back to default-parameter diffmap and can produce inf\n",
    "# pseudotime for terminal fate cells (which yields NaN star coordinates).\n",
    "mscorer.build_embedding(ordering_metric=\"velocity_pseudotime\")\n",
    "mscorer.fit()\n",
    "print(\"Shared embedding + FateMap fit complete.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55472d6e",
   "metadata": {},
   "source": [
    "## 4. Tier 1 — Score all conditions\n",
    "\n",
    "Compute per-condition CS / nCS scores plus cell-level affinities and bootstrap CIs.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "25b12cd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[scCS] Scoring condition: 'high_velocity' (633 cells)...\n",
      "[scCS] Computing bootstrap CI (n=200)...\n",
      "=== CommitmentScoreResult ===\n",
      "  Fates (4): Alpha, Beta, Delta, Epsilon\n",
      "  Dominant fate: Beta\n",
      "\n",
      "  Entropy metrics:\n",
      "    Population entropy:           0.7565  [aggregate velocity-mass balance]\n",
      "    Mean cell entropy:            0.9892  [per-cell average, k-way]\n",
      "    Per-fate cell entropy:\n",
      "      Alpha: 0.8224\n",
      "      Beta: 0.8109\n",
      "      Delta: 0.7921\n",
      "      Epsilon: 0.7915\n",
      "\n",
      "  Commitment vector (normalized):\n",
      "    Alpha: 0.0587\n",
      "    Beta: 0.5826\n",
      "    Delta: 0.0879\n",
      "    Epsilon: 0.2708\n",
      "\n",
      "  Pairwise nCS matrix:\n",
      "            Alpha      Beta     Delta   Epsilon\n",
      "Alpha    1.000000  1.880778  0.222604  1.336829\n",
      "Beta     0.531695  1.000000  0.118357  0.710785\n",
      "Delta    4.492291  8.449001  1.000000  6.005426\n",
      "Epsilon  0.748039  1.406895  0.166516  1.000000\n",
      "\n",
      "  Bootstrap 95% CI on nCS (n=200):\n",
      "    CI low:\n",
      "         Alpha   Beta  Delta  Epsilon\n",
      "Alpha    1.000  1.075  0.131    0.789\n",
      "Beta     0.307  1.000  0.078    0.437\n",
      "Delta    2.378  5.225  1.000    3.403\n",
      "Epsilon  0.399  0.918  0.109    1.000\n",
      "    CI high:\n",
      "         Alpha    Beta  Delta  Epsilon\n",
      "Alpha    1.000   3.261  0.421    2.508\n",
      "Beta     0.930   1.000  0.191    1.090\n",
      "Delta    7.653  12.820  1.000    9.162\n",
      "Epsilon  1.268   2.286  0.294    1.000\n",
      "\n",
      "[scCS] Scoring condition: 'low_velocity' (957 cells)...\n",
      "[scCS] Computing bootstrap CI (n=200)...\n",
      "=== CommitmentScoreResult ===\n",
      "  Fates (4): Alpha, Beta, Delta, Epsilon\n",
      "  Dominant fate: Alpha\n",
      "\n",
      "  Entropy metrics:\n",
      "    Population entropy:           0.8330  [aggregate velocity-mass balance]\n",
      "    Mean cell entropy:            0.9892  [per-cell average, k-way]\n",
      "    Per-fate cell entropy:\n",
      "      Alpha: 0.8224\n",
      "      Beta: 0.8109\n",
      "      Delta: 0.7921\n",
      "      Epsilon: 0.7915\n",
      "\n",
      "  Commitment vector (normalized):\n",
      "    Alpha: 0.4174\n",
      "    Beta: 0.2902\n",
      "    Delta: 0.0195\n",
      "    Epsilon: 0.2729\n",
      "\n",
      "  Pairwise nCS matrix:\n",
      "            Alpha      Beta      Delta   Epsilon\n",
      "Alpha    1.000000  1.315927   2.947273  0.273116\n",
      "Beta     0.759921  1.000000   2.239694  0.207547\n",
      "Delta    0.339297  0.446490   1.000000  0.092667\n",
      "Epsilon  3.661447  4.818195  10.791283  1.000000\n",
      "\n",
      "  Bootstrap 95% CI on nCS (n=200):\n",
      "    CI low:\n",
      "         Alpha   Beta  Delta  Epsilon\n",
      "Alpha    1.000  1.072  1.955    0.208\n",
      "Beta     0.578  1.000  1.496    0.156\n",
      "Delta    0.198  0.277  1.000    0.056\n",
      "Epsilon  2.701  3.384  6.932    1.000\n",
      "    CI high:\n",
      "         Alpha   Beta   Delta  Epsilon\n",
      "Alpha    1.000  1.729   5.042    0.370\n",
      "Beta     0.933  1.000   3.605    0.295\n",
      "Delta    0.512  0.669   1.000    0.144\n",
      "Epsilon  4.812  6.418  17.811    1.000\n",
      "\n",
      "[scCS] Scoring condition: 'med_velocity' (610 cells)...\n",
      "[scCS] Computing bootstrap CI (n=200)...\n",
      "=== CommitmentScoreResult ===\n",
      "  Fates (4): Alpha, Beta, Delta, Epsilon\n",
      "  Dominant fate: Beta\n",
      "\n",
      "  Entropy metrics:\n",
      "    Population entropy:           0.8042  [aggregate velocity-mass balance]\n",
      "    Mean cell entropy:            0.9892  [per-cell average, k-way]\n",
      "    Per-fate cell entropy:\n",
      "      Alpha: 0.8224\n",
      "      Beta: 0.8109\n",
      "      Delta: 0.7921\n",
      "      Epsilon: 0.7915\n",
      "\n",
      "  Commitment vector (normalized):\n",
      "    Alpha: 0.2271\n",
      "    Beta: 0.5552\n",
      "    Delta: 0.0520\n",
      "    Epsilon: 0.1657\n",
      "\n",
      "  Pairwise nCS matrix:\n",
      "            Alpha      Beta     Delta   Epsilon\n",
      "Alpha    1.000000  0.538022  0.708432  0.493838\n",
      "Beta     1.858660  1.000000  1.316734  0.917876\n",
      "Delta    1.411568  0.759455  1.000000  0.697085\n",
      "Epsilon  2.024957  1.089471  1.434545  1.000000\n",
      "\n",
      "  Bootstrap 95% CI on nCS (n=200):\n",
      "    CI low:\n",
      "         Alpha   Beta  Delta  Epsilon\n",
      "Alpha    1.000  0.381  0.452    0.294\n",
      "Beta     1.280  1.000  0.838    0.586\n",
      "Delta    0.881  0.469  1.000    0.402\n",
      "Epsilon  1.273  0.689  0.795    1.000\n",
      "    CI high:\n",
      "         Alpha   Beta  Delta  Epsilon\n",
      "Alpha    1.000  0.781  1.136    0.785\n",
      "Beta     2.623  1.000  2.134    1.451\n",
      "Delta    2.213  1.194  1.000    1.259\n",
      "Epsilon  3.398  1.705  2.485    1.000\n"
     ]
    },
    {
     "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>condition</th>\n",
       "      <th>n_cells_in_fate</th>\n",
       "      <th>unCS_alpha_beta</th>\n",
       "      <th>nCS_alpha_beta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>157</td>\n",
       "      <td>0.100756</td>\n",
       "      <td>1.880778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>low_velocity</td>\n",
       "      <td>812</td>\n",
       "      <td>1.438430</td>\n",
       "      <td>1.315927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>med_velocity</td>\n",
       "      <td>315</td>\n",
       "      <td>0.409044</td>\n",
       "      <td>0.538022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       condition  n_cells_in_fate  unCS_alpha_beta  nCS_alpha_beta\n",
       "0  high_velocity              157         0.100756        1.880778\n",
       "1   low_velocity              812         1.438430        1.315927\n",
       "2   med_velocity              315         0.409044        0.538022"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = mscorer.score_all_conditions(cell_level=True, n_bootstrap=200)\n",
    "\n",
    "summary_rows = []\n",
    "for cond, res in results.items():\n",
    "    summary_rows.append({\n",
    "        \"condition\": cond,\n",
    "        \"n_cells_in_fate\": int(res.n_cells_per_fate.sum()),\n",
    "        \"unCS_alpha_beta\": float(res.pairwise_unCS[res.fate_names.index(\"Alpha\"), res.fate_names.index(\"Beta\")])\n",
    "                            if \"Alpha\" in res.fate_names and \"Beta\" in res.fate_names else np.nan,\n",
    "        \"nCS_alpha_beta\": float(res.pairwise_nCS[res.fate_names.index(\"Alpha\"), res.fate_names.index(\"Beta\")])\n",
    "                            if \"Alpha\" in res.fate_names and \"Beta\" in res.fate_names else np.nan,\n",
    "    })\n",
    "pd.DataFrame(summary_rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9292059b",
   "metadata": {},
   "source": [
    "## 5. Tier 2 — Omnibus tests\n",
    "\n",
    "Test whether per-cell affinity scores differ across **all** conditions simultaneously, fate by fate.\n",
    "\n",
    "- **Kruskal-Wallis** (default): non-parametric, doesn't assume normality.\n",
    "- **ANOVA**: parametric, faster.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1bbb9f63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Omnibus test (kruskal) across 3 conditions ===\n",
      "  Significant fates: 4 / 4\n",
      "   fate           test  statistic         pval     pval_adj  significant\n",
      "  Alpha kruskal-wallis 156.075014 1.284521e-34 5.138084e-34         True\n",
      "   Beta kruskal-wallis  16.778824 2.272609e-04 9.090435e-04         True\n",
      "  Delta kruskal-wallis 156.910776 8.457808e-35 3.383123e-34         True\n",
      "Epsilon kruskal-wallis  15.441959 4.434260e-04 1.773704e-03         True\n"
     ]
    },
    {
     "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>fate</th>\n",
       "      <th>test</th>\n",
       "      <th>statistic</th>\n",
       "      <th>pval</th>\n",
       "      <th>n_conditions</th>\n",
       "      <th>pval_adj</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>kruskal-wallis</td>\n",
       "      <td>156.075014</td>\n",
       "      <td>1.284521e-34</td>\n",
       "      <td>3</td>\n",
       "      <td>5.138084e-34</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beta</td>\n",
       "      <td>kruskal-wallis</td>\n",
       "      <td>16.778824</td>\n",
       "      <td>2.272609e-04</td>\n",
       "      <td>3</td>\n",
       "      <td>9.090435e-04</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Delta</td>\n",
       "      <td>kruskal-wallis</td>\n",
       "      <td>156.910776</td>\n",
       "      <td>8.457808e-35</td>\n",
       "      <td>3</td>\n",
       "      <td>3.383123e-34</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>kruskal-wallis</td>\n",
       "      <td>15.441959</td>\n",
       "      <td>4.434260e-04</td>\n",
       "      <td>3</td>\n",
       "      <td>1.773704e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fate            test   statistic          pval  n_conditions      pval_adj  significant\n",
       "0    Alpha  kruskal-wallis  156.075014  1.284521e-34             3  5.138084e-34         True\n",
       "1     Beta  kruskal-wallis   16.778824  2.272609e-04             3  9.090435e-04         True\n",
       "2    Delta  kruskal-wallis  156.910776  8.457808e-35             3  3.383123e-34         True\n",
       "3  Epsilon  kruskal-wallis   15.441959  4.434260e-04             3  1.773704e-03         True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "omnibus_df = mscorer.compare_omnibus(results, test=\"kruskal\")\n",
    "omnibus_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7ef7ac7",
   "metadata": {},
   "source": [
    "## 6. Tier 2 — Post-hoc pairwise comparisons\n",
    "\n",
    "When the omnibus rejects H0, follow up with pairwise post-hoc tests.\n",
    "\n",
    "- `dunn`: standard non-parametric post-hoc for Kruskal-Wallis (Bonferroni-corrected).\n",
    "- `tukey`: standard parametric post-hoc for ANOVA.\n",
    "- `mwu`: Mann-Whitney U for each pair, Bonferroni-corrected.\n",
    "\n",
    "Pass `omnibus_results=` to restrict post-hoc to fates that passed omnibus.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2851ad57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Post-hoc comparison (dunn, correction=bonferroni) ===\n",
      "  Significant pairs: 6 / 12\n",
      "   fate                    comparison method         pval     pval_adj  delta_mean\n",
      "  Alpha high_velocity vs low_velocity   dunn 1.158355e-29 1.390026e-28   -0.018806\n",
      "  Alpha  low_velocity vs med_velocity   dunn 1.069279e-20 1.283135e-19    0.014005\n",
      "   Beta high_velocity vs low_velocity   dunn 5.344060e-05 6.412872e-04    0.001263\n",
      "  Delta high_velocity vs low_velocity   dunn 6.652264e-30 7.982717e-29    0.018908\n",
      "  Delta  low_velocity vs med_velocity   dunn 1.109400e-20 1.331281e-19   -0.014062\n",
      "Epsilon high_velocity vs low_velocity   dunn 1.048553e-04 1.258264e-03   -0.001364\n"
     ]
    },
    {
     "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>fate</th>\n",
       "      <th>comparison</th>\n",
       "      <th>method</th>\n",
       "      <th>statistic</th>\n",
       "      <th>pval</th>\n",
       "      <th>mean_A</th>\n",
       "      <th>mean_B</th>\n",
       "      <th>delta_mean</th>\n",
       "      <th>pval_adj</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>high_velocity vs low_velocity</td>\n",
       "      <td>dunn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.158355e-29</td>\n",
       "      <td>0.249787</td>\n",
       "      <td>0.268593</td>\n",
       "      <td>-0.018806</td>\n",
       "      <td>1.390026e-28</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>high_velocity vs med_velocity</td>\n",
       "      <td>dunn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.014552e-02</td>\n",
       "      <td>0.249787</td>\n",
       "      <td>0.254588</td>\n",
       "      <td>-0.004801</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>low_velocity vs med_velocity</td>\n",
       "      <td>dunn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.069279e-20</td>\n",
       "      <td>0.268593</td>\n",
       "      <td>0.254588</td>\n",
       "      <td>0.014005</td>\n",
       "      <td>1.283135e-19</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Beta</td>\n",
       "      <td>high_velocity vs low_velocity</td>\n",
       "      <td>dunn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.344060e-05</td>\n",
       "      <td>0.254747</td>\n",
       "      <td>0.253484</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>6.412872e-04</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Beta</td>\n",
       "      <td>high_velocity vs med_velocity</td>\n",
       "      <td>dunn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.034544e-01</td>\n",
       "      <td>0.254747</td>\n",
       "      <td>0.260941</td>\n",
       "      <td>-0.006195</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Beta</td>\n",
       "      <td>low_velocity vs med_velocity</td>\n",
       "      <td>dunn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.698207e-02</td>\n",
       "      <td>0.253484</td>\n",
       "      <td>0.260941</td>\n",
       "      <td>-0.007458</td>\n",
       "      <td>3.237848e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Delta</td>\n",
       "      <td>high_velocity vs low_velocity</td>\n",
       "      <td>dunn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.652264e-30</td>\n",
       "      <td>0.250201</td>\n",
       "      <td>0.231294</td>\n",
       "      <td>0.018908</td>\n",
       "      <td>7.982717e-29</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delta</td>\n",
       "      <td>high_velocity vs med_velocity</td>\n",
       "      <td>dunn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.150100e-02</td>\n",
       "      <td>0.250201</td>\n",
       "      <td>0.245356</td>\n",
       "      <td>0.004845</td>\n",
       "      <td>9.780120e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Delta</td>\n",
       "      <td>low_velocity vs med_velocity</td>\n",
       "      <td>dunn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.109400e-20</td>\n",
       "      <td>0.231294</td>\n",
       "      <td>0.245356</td>\n",
       "      <td>-0.014062</td>\n",
       "      <td>1.331281e-19</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>high_velocity vs low_velocity</td>\n",
       "      <td>dunn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.048553e-04</td>\n",
       "      <td>0.245265</td>\n",
       "      <td>0.246630</td>\n",
       "      <td>-0.001364</td>\n",
       "      <td>1.258264e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>high_velocity vs med_velocity</td>\n",
       "      <td>dunn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.139209e-01</td>\n",
       "      <td>0.245265</td>\n",
       "      <td>0.239115</td>\n",
       "      <td>0.006151</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>low_velocity vs med_velocity</td>\n",
       "      <td>dunn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.532275e-02</td>\n",
       "      <td>0.246630</td>\n",
       "      <td>0.239115</td>\n",
       "      <td>0.007515</td>\n",
       "      <td>4.238730e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fate                     comparison method  statistic          pval    mean_A    mean_B  delta_mean      pval_adj  significant\n",
       "0     Alpha  high_velocity vs low_velocity   dunn        NaN  1.158355e-29  0.249787  0.268593   -0.018806  1.390026e-28         True\n",
       "1     Alpha  high_velocity vs med_velocity   dunn        NaN  9.014552e-02  0.249787  0.254588   -0.004801  1.000000e+00        False\n",
       "2     Alpha   low_velocity vs med_velocity   dunn        NaN  1.069279e-20  0.268593  0.254588    0.014005  1.283135e-19         True\n",
       "3      Beta  high_velocity vs low_velocity   dunn        NaN  5.344060e-05  0.254747  0.253484    0.001263  6.412872e-04         True\n",
       "4      Beta  high_velocity vs med_velocity   dunn        NaN  1.034544e-01  0.254747  0.260941   -0.006195  1.000000e+00        False\n",
       "5      Beta   low_velocity vs med_velocity   dunn        NaN  2.698207e-02  0.253484  0.260941   -0.007458  3.237848e-01        False\n",
       "6     Delta  high_velocity vs low_velocity   dunn        NaN  6.652264e-30  0.250201  0.231294    0.018908  7.982717e-29         True\n",
       "7     Delta  high_velocity vs med_velocity   dunn        NaN  8.150100e-02  0.250201  0.245356    0.004845  9.780120e-01        False\n",
       "8     Delta   low_velocity vs med_velocity   dunn        NaN  1.109400e-20  0.231294  0.245356   -0.014062  1.331281e-19         True\n",
       "9   Epsilon  high_velocity vs low_velocity   dunn        NaN  1.048553e-04  0.245265  0.246630   -0.001364  1.258264e-03         True\n",
       "10  Epsilon  high_velocity vs med_velocity   dunn        NaN  1.139209e-01  0.245265  0.239115    0.006151  1.000000e+00        False\n",
       "11  Epsilon   low_velocity vs med_velocity   dunn        NaN  3.532275e-02  0.246630  0.239115    0.007515  4.238730e-01        False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posthoc_df = mscorer.compare_posthoc(\n",
    "    results,\n",
    "    omnibus_results=omnibus_df,\n",
    "    method=\"dunn\",\n",
    "    pval_correction=\"bonferroni\",\n",
    "    pval_threshold=0.05,\n",
    ")\n",
    "posthoc_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "322ed091",
   "metadata": {},
   "source": [
    "## 7. Compute pairwise deltas (ΔnCS) for all condition pairs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a39e2081",
   "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>cond_a</th>\n",
       "      <th>cond_b</th>\n",
       "      <th>fate_pair</th>\n",
       "      <th>delta_nCS</th>\n",
       "      <th>CI_low</th>\n",
       "      <th>CI_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>low_velocity</td>\n",
       "      <td>Alpha vs Beta</td>\n",
       "      <td>0.564851</td>\n",
       "      <td>-0.051601</td>\n",
       "      <td>1.366215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>low_velocity</td>\n",
       "      <td>Alpha vs Delta</td>\n",
       "      <td>-2.724670</td>\n",
       "      <td>-4.147149</td>\n",
       "      <td>-2.035804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>low_velocity</td>\n",
       "      <td>Alpha vs Epsilon</td>\n",
       "      <td>1.063713</td>\n",
       "      <td>0.607047</td>\n",
       "      <td>1.774658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>low_velocity</td>\n",
       "      <td>Beta vs Delta</td>\n",
       "      <td>-2.121337</td>\n",
       "      <td>-3.143616</td>\n",
       "      <td>-1.511814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>low_velocity</td>\n",
       "      <td>Beta vs Epsilon</td>\n",
       "      <td>0.503239</td>\n",
       "      <td>0.288870</td>\n",
       "      <td>0.789858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>low_velocity</td>\n",
       "      <td>Delta vs Epsilon</td>\n",
       "      <td>5.912758</td>\n",
       "      <td>3.766956</td>\n",
       "      <td>8.101832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Alpha vs Beta</td>\n",
       "      <td>1.342756</td>\n",
       "      <td>0.683284</td>\n",
       "      <td>2.208189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Alpha vs Delta</td>\n",
       "      <td>-0.485829</td>\n",
       "      <td>-0.736719</td>\n",
       "      <td>-0.216908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Alpha vs Epsilon</td>\n",
       "      <td>0.842992</td>\n",
       "      <td>0.318774</td>\n",
       "      <td>1.503781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Beta vs Delta</td>\n",
       "      <td>-1.198377</td>\n",
       "      <td>-1.632024</td>\n",
       "      <td>-0.819982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Beta vs Epsilon</td>\n",
       "      <td>-0.207091</td>\n",
       "      <td>-0.743554</td>\n",
       "      <td>0.246893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Delta vs Epsilon</td>\n",
       "      <td>5.308340</td>\n",
       "      <td>3.154044</td>\n",
       "      <td>7.473284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>low_velocity</td>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Alpha vs Beta</td>\n",
       "      <td>0.777905</td>\n",
       "      <td>0.590668</td>\n",
       "      <td>1.079409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>low_velocity</td>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Alpha vs Delta</td>\n",
       "      <td>2.238841</td>\n",
       "      <td>1.541933</td>\n",
       "      <td>3.886388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>low_velocity</td>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Alpha vs Epsilon</td>\n",
       "      <td>-0.220722</td>\n",
       "      <td>-0.505912</td>\n",
       "      <td>-0.038625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>low_velocity</td>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Beta vs Delta</td>\n",
       "      <td>0.922960</td>\n",
       "      <td>0.221573</td>\n",
       "      <td>2.134772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>low_velocity</td>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Beta vs Epsilon</td>\n",
       "      <td>-0.710330</td>\n",
       "      <td>-1.164860</td>\n",
       "      <td>-0.390430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>low_velocity</td>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Delta vs Epsilon</td>\n",
       "      <td>-0.604418</td>\n",
       "      <td>-1.061001</td>\n",
       "      <td>-0.396445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cond_a        cond_b         fate_pair  delta_nCS    CI_low   CI_high\n",
       "0   high_velocity  low_velocity     Alpha vs Beta   0.564851 -0.051601  1.366215\n",
       "1   high_velocity  low_velocity    Alpha vs Delta  -2.724670 -4.147149 -2.035804\n",
       "2   high_velocity  low_velocity  Alpha vs Epsilon   1.063713  0.607047  1.774658\n",
       "3   high_velocity  low_velocity     Beta vs Delta  -2.121337 -3.143616 -1.511814\n",
       "4   high_velocity  low_velocity   Beta vs Epsilon   0.503239  0.288870  0.789858\n",
       "5   high_velocity  low_velocity  Delta vs Epsilon   5.912758  3.766956  8.101832\n",
       "6   high_velocity  med_velocity     Alpha vs Beta   1.342756  0.683284  2.208189\n",
       "7   high_velocity  med_velocity    Alpha vs Delta  -0.485829 -0.736719 -0.216908\n",
       "8   high_velocity  med_velocity  Alpha vs Epsilon   0.842992  0.318774  1.503781\n",
       "9   high_velocity  med_velocity     Beta vs Delta  -1.198377 -1.632024 -0.819982\n",
       "10  high_velocity  med_velocity   Beta vs Epsilon  -0.207091 -0.743554  0.246893\n",
       "11  high_velocity  med_velocity  Delta vs Epsilon   5.308340  3.154044  7.473284\n",
       "12   low_velocity  med_velocity     Alpha vs Beta   0.777905  0.590668  1.079409\n",
       "13   low_velocity  med_velocity    Alpha vs Delta   2.238841  1.541933  3.886388\n",
       "14   low_velocity  med_velocity  Alpha vs Epsilon  -0.220722 -0.505912 -0.038625\n",
       "15   low_velocity  med_velocity     Beta vs Delta   0.922960  0.221573  2.134772\n",
       "16   low_velocity  med_velocity   Beta vs Epsilon  -0.710330 -1.164860 -0.390430\n",
       "17   low_velocity  med_velocity  Delta vs Epsilon  -0.604418 -1.061001 -0.396445"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltas = mscorer.compute_pairwise_deltas(n_bootstrap=100, verbose=False)\n",
    "\n",
    "delta_rows = []\n",
    "for (cond_a, cond_b), delta in deltas.items():\n",
    "    fate_names_d = delta[\"fate_names\"]\n",
    "    k = len(fate_names_d)\n",
    "    for i in range(k):\n",
    "        for j in range(i + 1, k):\n",
    "            delta_rows.append({\n",
    "                \"cond_a\": cond_a,\n",
    "                \"cond_b\": cond_b,\n",
    "                \"fate_pair\": f\"{fate_names_d[i]} vs {fate_names_d[j]}\",\n",
    "                \"delta_nCS\": float(delta[\"delta_nCS\"][i, j]),\n",
    "                \"CI_low\":    float(delta[\"ci_low\"][i, j]),\n",
    "                \"CI_high\":   float(delta[\"ci_high\"][i, j]),\n",
    "            })\n",
    "pd.DataFrame(delta_rows)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d790274d",
   "metadata": {},
   "source": [
    "## 8. Tier 3 — Mixed-effects model with reference contrasts\n",
    "\n",
    "Fits per-cell affinity ~ condition + (1 | replicate), with one of the conditions as the reference. Returns coefficient estimates for each non-reference condition.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c53dffb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Mixed-effects model contrasts ===\n",
      "  Significant contrasts: 0 / 8\n"
     ]
    },
    {
     "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>fate</th>\n",
       "      <th>contrast</th>\n",
       "      <th>condition_a</th>\n",
       "      <th>condition_b</th>\n",
       "      <th>coef</th>\n",
       "      <th>std_err</th>\n",
       "      <th>z_score</th>\n",
       "      <th>pval</th>\n",
       "      <th>pval_adj</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>high_velocity vs low_velocity</td>\n",
       "      <td>high_velocity</td>\n",
       "      <td>low_velocity</td>\n",
       "      <td>0.487261</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>med_velocity vs low_velocity</td>\n",
       "      <td>med_velocity</td>\n",
       "      <td>low_velocity</td>\n",
       "      <td>0.370473</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beta</td>\n",
       "      <td>high_velocity vs low_velocity</td>\n",
       "      <td>high_velocity</td>\n",
       "      <td>low_velocity</td>\n",
       "      <td>1.327102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Beta</td>\n",
       "      <td>med_velocity vs low_velocity</td>\n",
       "      <td>med_velocity</td>\n",
       "      <td>low_velocity</td>\n",
       "      <td>-0.213693</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Delta</td>\n",
       "      <td>high_velocity vs low_velocity</td>\n",
       "      <td>high_velocity</td>\n",
       "      <td>low_velocity</td>\n",
       "      <td>-0.042396</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Delta</td>\n",
       "      <td>med_velocity vs low_velocity</td>\n",
       "      <td>med_velocity</td>\n",
       "      <td>low_velocity</td>\n",
       "      <td>0.249594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>high_velocity vs low_velocity</td>\n",
       "      <td>high_velocity</td>\n",
       "      <td>low_velocity</td>\n",
       "      <td>-2.542953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>med_velocity vs low_velocity</td>\n",
       "      <td>med_velocity</td>\n",
       "      <td>low_velocity</td>\n",
       "      <td>0.172580</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fate                       contrast    condition_a   condition_b      coef  std_err  z_score  pval  pval_adj  significant\n",
       "0    Alpha  high_velocity vs low_velocity  high_velocity  low_velocity  0.487261      NaN      0.0   1.0       1.0        False\n",
       "1    Alpha   med_velocity vs low_velocity   med_velocity  low_velocity  0.370473      NaN      0.0   1.0       1.0        False\n",
       "2     Beta  high_velocity vs low_velocity  high_velocity  low_velocity  1.327102      NaN      0.0   1.0       1.0        False\n",
       "3     Beta   med_velocity vs low_velocity   med_velocity  low_velocity -0.213693      NaN      0.0   1.0       1.0        False\n",
       "4    Delta  high_velocity vs low_velocity  high_velocity  low_velocity -0.042396      NaN      0.0   1.0       1.0        False\n",
       "5    Delta   med_velocity vs low_velocity   med_velocity  low_velocity  0.249594      NaN      0.0   1.0       1.0        False\n",
       "6  Epsilon  high_velocity vs low_velocity  high_velocity  low_velocity -2.542953      NaN      0.0   1.0       1.0        False\n",
       "7  Epsilon   med_velocity vs low_velocity   med_velocity  low_velocity  0.172580      NaN      0.0   1.0       1.0        False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contrasts_df = mscorer.fit_mixed_model_contrasts(\n",
    "    results,\n",
    "    ref_condition=\"low_velocity\",\n",
    "    replicate_key=\"sample\",\n",
    ")\n",
    "contrasts_df.head(20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6939184",
   "metadata": {},
   "source": [
    "## 9. Visualizations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "90568575",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x420 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Omnibus summary: fates x conditions heatmap (also marks post-hoc significance)\n",
    "fig = mscorer.plot_omnibus_summary(omnibus_df, results, posthoc_df=posthoc_df)\n",
    "fig.savefig(\"multi_omnibus_summary.png\", dpi=120, bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "32db417e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Per-fate post-hoc heatmap (shows which condition pairs differ)\n",
    "fig = mscorer.plot_posthoc_heatmap(posthoc_df)\n",
    "fig.savefig(\"multi_posthoc_heatmap.png\", dpi=120, bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "230bc313",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pairwise delta grid: ΔCS heatmaps for all condition pairs\n",
    "fig = mscorer.plot_pairwise_delta_grid(deltas)\n",
    "fig.savefig(\"multi_pairwise_deltas.png\", dpi=120, bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0db9ee4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Standard multi-condition visualizations (shared with PairScorer)\n",
    "fig = mscorer.plot_star_grid(results)\n",
    "plt.show()\n",
    "\n",
    "fig = mscorer.plot_rose_grid(results)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c67429a",
   "metadata": {},
   "source": [
    "## 10. Driver genes per condition\n",
    "\n",
    "For each condition we mask the shared embedding's `adata_sub` to that condition, then run the same drivers used in `SingleScorer` — `get_velocity_drivers` and `get_deg_drivers`. The compact `_stack_drivers_by_condition` helper concatenates the per-fate results into one tidy `(condition, fate, gene, ...)` table.\n",
    "\n",
    "This pattern lets you ask:\n",
    "- *\"Which genes drive each fate decision **within** each condition?\"*\n",
    "- *\"Which driver genes are **shared** across all conditions vs **condition-specific**?\"*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e499d579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "── Velocity drivers: Alpha (top 20, sorted by delta_velocity) ──\n",
      " rank     gene  delta_velocity  mean_velocity\n",
      "    1   Ptprn2       10.138916      10.179033\n",
      "    2   Hs6st3        3.034873       3.104659\n",
      "    3  Fam155a        2.228252       2.250724\n",
      "    4     Rora        1.744673       1.288695\n",
      "    5   Kcnma1        1.205779       1.199826\n",
      "    6    Ndst4        1.142244       1.151202\n",
      "    7  Stxbp5l        1.115459       1.149110\n",
      "    8    Glud1        1.078897       0.986085\n",
      "    9   Dock11        1.037627       1.048391\n",
      "   10    Rims2        0.969603       1.035052\n",
      "   11    Kcnb2        0.855988       0.901875\n",
      "   12      Tnr        0.838274       0.840978\n",
      "   13    Nlgn1        0.813350       0.828963\n",
      "   14  Cacna1d        0.786799       0.842683\n",
      "   15  Cacna1a        0.772174       0.479498\n",
      "   16 Tmem178b        0.724432       0.726721\n",
      "   17    Meis2        0.640396       1.320285\n",
      "   18     Rgs7        0.633256       0.633126\n",
      "   19    Unc79        0.553534       0.561987\n",
      "   20    Gnao1        0.547504       0.557490\n",
      "\n",
      "── Velocity drivers: Beta (top 20, sorted by delta_velocity) ──\n",
      " rank    gene  delta_velocity  mean_velocity\n",
      "    1  Ptprn2        3.382511       3.422628\n",
      "    2    Rora        2.651336       2.195358\n",
      "    3    Ins2        1.267590       1.869774\n",
      "    4  Hs6st3        1.112840       1.182626\n",
      "    5 Fam155a        0.823156       0.845628\n",
      "    6   Sntg1        0.803178       0.959989\n",
      "    7  Kcnmb2        0.722441       0.731022\n",
      "    8   Rims2        0.625068       0.690516\n",
      "    9    Gnas        0.566056       0.370796\n",
      "   10   Gnao1        0.533264       0.543250\n",
      "   11 Tmem108        0.484175       0.502514\n",
      "   12     Cpe        0.482112       0.480504\n",
      "   13 Phactr1        0.474257       0.432363\n",
      "   14    Pax6        0.409636       0.406289\n",
      "   15   Unc5c        0.385509       0.392267\n",
      "   16   Map1b        0.382621       0.384673\n",
      "   17  Rnf220        0.329072      -0.704068\n",
      "   18    Rfx6        0.316811       0.337776\n",
      "   19    Pim2        0.312921       0.318306\n",
      "   20 Macrod2        0.312186       0.852050\n",
      "\n",
      "── Velocity drivers: Delta (top 20, sorted by delta_velocity) ──\n",
      " rank     gene  delta_velocity  mean_velocity\n",
      "    1   Hs6st3        2.518699       2.588486\n",
      "    2  Fam155a        2.233560       2.256033\n",
      "    3  Macrod2        1.641149       2.181014\n",
      "    4  Stxbp5l        1.554508       1.588159\n",
      "    5  Pip5k1b        1.302159       1.617899\n",
      "    6    Pcsk2        1.195035       1.186527\n",
      "    7      Ghr        1.095726       1.105962\n",
      "    8    Prkca        1.086828       1.211758\n",
      "    9 Slc16a12        0.868062       0.982354\n",
      "   10     Sdk1        0.794813       0.840292\n",
      "   11    Abcc8        0.771486       0.795749\n",
      "   12    Unc79        0.697671       0.706124\n",
      "   13   Ctnna3        0.692108       0.699166\n",
      "   14    Gnao1        0.668201       0.678187\n",
      "   15   Fbxl17        0.657306       1.152713\n",
      "   16     Nol4        0.630128       0.639353\n",
      "   17    Ptprt        0.620964       0.676163\n",
      "   18  Cacna1d        0.606272       0.662156\n",
      "   19  Cacna1a        0.588065       0.295389\n",
      "   20    Tshz3        0.571219       0.568317\n",
      "\n",
      "── Velocity drivers: Epsilon (top 20, sorted by delta_velocity) ──\n",
      " rank          gene  delta_velocity  mean_velocity\n",
      "    1        Ptprn2        7.266768       7.306885\n",
      "    2       Cacna1c        4.574422       4.559257\n",
      "    3 9030622O22Rik        3.141153       3.523523\n",
      "    4       Cacna1a        2.290157       1.997480\n",
      "    5         Maml3        2.246315       2.489925\n",
      "    6         Cadps        2.023596       2.027040\n",
      "    7          Ghrl        1.524408       1.524595\n",
      "    8       Rapgef4        1.362765       1.443573\n",
      "    9           Pam        1.338101       1.679233\n",
      "   10           Ghr        1.154232       1.164467\n",
      "   11        Sorcs1        1.091565       1.137015\n",
      "   12       Runx1t1        0.970272       0.964474\n",
      "   13        Rimbp2        0.893447       0.889542\n",
      "   14         Pard3        0.851990       0.369684\n",
      "   15        Kcnma1        0.826091       0.820138\n",
      "   16        Setbp1        0.775604       0.874517\n",
      "   17       Slc7a14        0.755812       0.751685\n",
      "   18         Gstz1        0.730673       0.386536\n",
      "   19         Gnao1        0.694398       0.704384\n",
      "   20         Kcnd2        0.670332       0.675399\n",
      "\n",
      "── Velocity drivers: Alpha (top 20, sorted by delta_velocity) ──\n",
      " rank     gene  delta_velocity  mean_velocity\n",
      "    1   Ptprn2        4.946443       5.012487\n",
      "    2   Hs6st3        1.900544       1.959349\n",
      "    3    Auts2        1.824287       2.177465\n",
      "    4    Park2        1.416998       1.663597\n",
      "    5  Fam155a        0.830096       0.837425\n",
      "    6  Cacna1a        0.823989       0.444519\n",
      "    7  Zcchc16        0.746371       0.767187\n",
      "    8    Gnao1        0.719144       0.711517\n",
      "    9    Rims2        0.706261       0.827242\n",
      "   10  Macrod2        0.656080       0.851456\n",
      "   11    Kctd8        0.563886       0.565218\n",
      "   12    Chrm3        0.547148       0.611220\n",
      "   13   Kcnma1        0.544609       0.545178\n",
      "   14     Sdk1        0.512390       0.524567\n",
      "   15    Unc79        0.459880       0.419348\n",
      "   16 Tmem178b        0.441969       0.445359\n",
      "   17  Cacna1c        0.429449       0.458596\n",
      "   18    Nlgn1        0.424375       0.445873\n",
      "   19   Sorcs1        0.412837       0.428996\n",
      "   20    Pcsk2        0.385351       0.388765\n",
      "\n",
      "── Velocity drivers: Beta (top 20, sorted by delta_velocity) ──\n",
      " rank    gene  delta_velocity  mean_velocity\n",
      "    1  Hs6st3        2.056397       2.115201\n",
      "    2  Ptprn2        1.646501       1.712545\n",
      "    3    Ins2        1.640553       2.172913\n",
      "    4    Nnat        1.464386       1.480304\n",
      "    5   Park2        1.388878       1.635477\n",
      "    6   Abcc8        1.196749       1.221476\n",
      "    7    Rora        1.107728       0.719490\n",
      "    8   Gnao1        1.025234       1.017608\n",
      "    9   Cadps        0.882353       0.916004\n",
      "   10    Sdk1        0.736400       0.748576\n",
      "   11 Fam155a        0.674569       0.681898\n",
      "   12   Gng12        0.656559       0.699961\n",
      "   13   Maml3        0.639099       0.987928\n",
      "   14 Cacna1a        0.638900       0.259430\n",
      "   15 Tmem108        0.574204       0.578854\n",
      "   16 Macrod2        0.551186       0.746562\n",
      "   17   Rims2        0.535604       0.656585\n",
      "   18   Unc5c        0.514555       0.520736\n",
      "   19  Kcnmb2        0.464552       0.466642\n",
      "   20   Unc79        0.440983       0.400451\n",
      "\n",
      "── Velocity drivers: Delta (top 20, sorted by delta_velocity) ──\n",
      " rank    gene  delta_velocity  mean_velocity\n",
      "    1    Rora        4.120846       3.732608\n",
      "    2 Cacna1a        1.505980       1.126510\n",
      "    3  Hs6st3        1.480293       1.539098\n",
      "    4 Fam155a        1.426345       1.433674\n",
      "    5   Kcnd2        1.362409       1.371242\n",
      "    6   Asic2        1.297021       1.297021\n",
      "    7   Park2        1.221469       1.468067\n",
      "    8 Pip5k1b        1.090178       1.402752\n",
      "    9  Ptprn2        1.041894       1.107937\n",
      "   10   Abcc8        0.939510       0.964237\n",
      "   11 Cacna1c        0.921160       0.950308\n",
      "   12 Macrod2        0.901842       1.097218\n",
      "   13    Ank2        0.719375       0.670792\n",
      "   14   Kcnh8        0.636324       0.589051\n",
      "   15  Kcnma1        0.628848       0.629416\n",
      "   16 Phactr1        0.575674       0.518170\n",
      "   17 Zcchc16        0.561209       0.582024\n",
      "   18   Map1b        0.560959       0.566319\n",
      "   19   Rims2        0.545161       0.666142\n",
      "   20 Slc7a14        0.539349       0.539058\n",
      "\n",
      "── Velocity drivers: Epsilon (top 20, sorted by delta_velocity) ──\n",
      " rank          gene  delta_velocity  mean_velocity\n",
      "    1         Auts2        2.888110       3.241288\n",
      "    2         Park2        1.922998       2.169597\n",
      "    3       Fam155a        1.501959       1.509288\n",
      "    4        Kcnma1        1.364251       1.364820\n",
      "    5        Sphkap        1.008352       1.014405\n",
      "    6         Mast4        0.923207       1.209805\n",
      "    7       Zcchc16        0.891418       0.912233\n",
      "    8         Rims2        0.864141       0.985122\n",
      "    9 9030622O22Rik        0.845331       1.251969\n",
      "   10         Cadps        0.767456       0.801107\n",
      "   11        Hs6st3        0.742775       0.801580\n",
      "   12         Gnao1        0.671777       0.664150\n",
      "   13       Cacna1c        0.653965       0.683112\n",
      "   14       Cacna1a        0.625256       0.245785\n",
      "   15        Sorcs1        0.610847       0.627006\n",
      "   16         Kcnb2        0.568632       0.640924\n",
      "   17         Kcnd2        0.541085       0.549917\n",
      "   18         Unc79        0.525438       0.484906\n",
      "   19         Map1b        0.504821       0.510181\n",
      "   20          Rfx6        0.480548       0.499856\n",
      "\n",
      "── Velocity drivers: Alpha (top 20, sorted by delta_velocity) ──\n",
      " rank     gene  delta_velocity  mean_velocity\n",
      "    1   Ptprn2        5.065232       4.972903\n",
      "    2   Hs6st3        2.101268       2.160700\n",
      "    3    Park2        1.049840       1.606843\n",
      "    4  Fam155a        1.024209       1.038476\n",
      "    5    Auts2        0.979488       1.735276\n",
      "    6    Rims2        0.787453       0.852751\n",
      "    7   Kcnma1        0.763136       0.760720\n",
      "    8  Zcchc16        0.675566       0.695090\n",
      "    9  Macrod2        0.602498       0.864472\n",
      "   10    Kctd8        0.562066       0.548849\n",
      "   11    Nlgn1        0.532786       0.552278\n",
      "   12 Tmem178b        0.528826       0.529526\n",
      "   13  Cacna1a        0.496395       0.321379\n",
      "   14   Sorcs1        0.479955       0.494385\n",
      "   15    Gnao1        0.449957       0.452301\n",
      "   16    Unc79        0.443202       0.445140\n",
      "   17    Glud1        0.359886       0.334689\n",
      "   18    Ndst4        0.351433       0.358264\n",
      "   19    Chrm3        0.326816       0.462213\n",
      "   20   Kcnip4        0.319257       0.370047\n",
      "\n",
      "── Velocity drivers: Beta (top 20, sorted by delta_velocity) ──\n",
      " rank    gene  delta_velocity  mean_velocity\n",
      "    1    Ins2        9.558885      10.156736\n",
      "    2  Ptprn2        3.255148       3.162819\n",
      "    3    Rora        1.666505       1.843346\n",
      "    4  Hs6st3        1.391292       1.450724\n",
      "    5 Phactr1        0.967009       0.905735\n",
      "    6   Sntg1        0.918031       1.298722\n",
      "    7   Park2        0.891937       1.448940\n",
      "    8    Nnat        0.832265       0.847311\n",
      "    9 Fam155a        0.729469       0.743736\n",
      "   10   Gnao1        0.685975       0.688319\n",
      "   11 Macrod2        0.644620       0.906594\n",
      "   12   Rims2        0.643239       0.708537\n",
      "   13  Kcnmb2        0.473439       0.478471\n",
      "   14   Unc5c        0.449677       0.458875\n",
      "   15   Maml3        0.449581       0.691347\n",
      "   16 Rapgef4        0.448530       0.488526\n",
      "   17    Pax6        0.414843       0.411885\n",
      "   18 Tmem108        0.408576       0.445672\n",
      "   19   Abcc8        0.400374       0.416758\n",
      "   20 Fam219a        0.323444       0.333184\n",
      "\n",
      "── Velocity drivers: Delta (top 20, sorted by delta_velocity) ──\n",
      " rank     gene  delta_velocity  mean_velocity\n",
      "    1   Hs6st3        2.254268       2.313700\n",
      "    2     Rora        2.149730       2.326570\n",
      "    3  Fam155a        1.761150       1.775418\n",
      "    4  Macrod2        1.227703       1.489677\n",
      "    5  Cacna1a        1.133342       0.958327\n",
      "    6  Pip5k1b        0.948238       1.236933\n",
      "    7    Abcc8        0.851071       0.867455\n",
      "    8 Slc16a12        0.839184       0.930028\n",
      "    9  Cacna1c        0.798074       0.800853\n",
      "   10    Kcnd2        0.777342       0.791671\n",
      "   11    Asic2        0.730172       0.732178\n",
      "   12    Ptprt        0.713642       0.751418\n",
      "   13    Map1b        0.630941       0.632863\n",
      "   14   Gatsl2        0.561950       0.596788\n",
      "   15    Tshz3        0.535825       0.543022\n",
      "   16    Prkca        0.531170       0.610961\n",
      "   17  Slc7a14        0.526877       0.528869\n",
      "   18  Stxbp5l        0.511966       0.541099\n",
      "   19     Ank2        0.473522       0.462186\n",
      "   20    Kcnh8        0.466996       0.511684\n",
      "\n",
      "── Velocity drivers: Epsilon (top 20, sorted by delta_velocity) ──\n",
      " rank          gene  delta_velocity  mean_velocity\n",
      "    1        Ptprn2        2.387180       2.294851\n",
      "    2 9030622O22Rik        1.907413       2.330741\n",
      "    3       Cacna1c        1.846774       1.849553\n",
      "    4        Sorcs1        1.739625       1.754055\n",
      "    5       Fam155a        1.015867       1.030134\n",
      "    6         Maml3        0.973382       1.215148\n",
      "    7       Rapgef4        0.943933       0.983929\n",
      "    8        Sphkap        0.866295       0.867441\n",
      "    9        Hs6st3        0.820497       0.879930\n",
      "   10         Park2        0.810861       1.367864\n",
      "   11       Zcchc16        0.794487       0.814011\n",
      "   12         Auts2        0.778251       1.534039\n",
      "   13         Unc79        0.724618       0.726556\n",
      "   14        Ctnna3        0.648964       0.663372\n",
      "   15         Cadps        0.637715       0.652266\n",
      "   16       Cacna1a        0.596067       0.421051\n",
      "   17        Kcnma1        0.588465       0.586048\n",
      "   18        Mamld1        0.576373       0.644715\n",
      "   19         Rims2        0.551724       0.617022\n",
      "   20        Rimbp2        0.526820       0.533911\n",
      "Top velocity drivers (top 5 per fate, per condition):\n"
     ]
    },
    {
     "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>condition</th>\n",
       "      <th>fate</th>\n",
       "      <th>gene</th>\n",
       "      <th>delta_velocity</th>\n",
       "      <th>mean_velocity</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>Ptprn2</td>\n",
       "      <td>10.138916</td>\n",
       "      <td>10.179033</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>Hs6st3</td>\n",
       "      <td>3.034873</td>\n",
       "      <td>3.104659</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>Fam155a</td>\n",
       "      <td>2.228252</td>\n",
       "      <td>2.250724</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>Rora</td>\n",
       "      <td>1.744673</td>\n",
       "      <td>1.288695</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>Kcnma1</td>\n",
       "      <td>1.205779</td>\n",
       "      <td>1.199826</td>\n",
       "      <td>5</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Ptprn2</td>\n",
       "      <td>2.387180</td>\n",
       "      <td>2.294851</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Epsilon</td>\n",
       "      <td>9030622O22Rik</td>\n",
       "      <td>1.907413</td>\n",
       "      <td>2.330741</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Cacna1c</td>\n",
       "      <td>1.846774</td>\n",
       "      <td>1.849553</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Sorcs1</td>\n",
       "      <td>1.739625</td>\n",
       "      <td>1.754055</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Fam155a</td>\n",
       "      <td>1.015867</td>\n",
       "      <td>1.030134</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        condition     fate           gene  delta_velocity  mean_velocity  rank\n",
       "0   high_velocity    Alpha         Ptprn2       10.138916      10.179033     1\n",
       "1   high_velocity    Alpha         Hs6st3        3.034873       3.104659     2\n",
       "2   high_velocity    Alpha        Fam155a        2.228252       2.250724     3\n",
       "3   high_velocity    Alpha           Rora        1.744673       1.288695     4\n",
       "4   high_velocity    Alpha         Kcnma1        1.205779       1.199826     5\n",
       "..            ...      ...            ...             ...            ...   ...\n",
       "55   med_velocity  Epsilon         Ptprn2        2.387180       2.294851     1\n",
       "56   med_velocity  Epsilon  9030622O22Rik        1.907413       2.330741     2\n",
       "57   med_velocity  Epsilon        Cacna1c        1.846774       1.849553     3\n",
       "58   med_velocity  Epsilon         Sorcs1        1.739625       1.754055     4\n",
       "59   med_velocity  Epsilon        Fam155a        1.015867       1.030134     5\n",
       "\n",
       "[60 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_sub_shared = mscorer._scorer.adata_sub\n",
    "fate_names = mscorer._scorer._fate_map.fate_names\n",
    "root_cluster = mscorer._scorer.root\n",
    "obs_key_used = mscorer._scorer.obs_key\n",
    "\n",
    "vel_drivers_per_cond = {}\n",
    "for cond in mscorer.conditions:\n",
    "    mask = (adata_sub_shared.obs[mscorer.condition_obs_key].astype(str) == cond).values\n",
    "    sub = adata_sub_shared[mask].copy()\n",
    "    vel_drivers_per_cond[cond] = get_velocity_drivers(\n",
    "        sub,\n",
    "        fate_names=fate_names,\n",
    "        obs_key=obs_key_used,\n",
    "        root=root_cluster,\n",
    "        n_top_genes=20,\n",
    "    )\n",
    "\n",
    "# One compact table: (condition, fate, gene, delta_velocity, mean_velocity, rank)\n",
    "print(\"Top velocity drivers (top 5 per fate, per condition):\")\n",
    "_stack_drivers_by_condition(\n",
    "    vel_drivers_per_cond,\n",
    "    n_per_fate=5,\n",
    "    cols=[\"gene\", \"delta_velocity\", \"mean_velocity\", \"rank\"],\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "01c91a3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "── DEG drivers: Alpha vs progenitor ──\n",
      "   Significant: 170  (up: 80, down: 90)\n",
      "   gene  logfoldchange  pval_adj  pct_fate  pct_progenitor\n",
      "   Iapp     165.372681  0.001959  1.000000             NaN\n",
      "    Ttr     143.253738  0.001959  1.000000             NaN\n",
      "    Pyy      99.835434  0.001959  1.000000             NaN\n",
      "   Chgb      69.993912  0.001959  1.000000             NaN\n",
      "   Gnas      59.068867  0.001959  1.000000             NaN\n",
      " Pcsk1n      47.226532  0.001959  1.000000             NaN\n",
      " Malat1      42.059872  0.007366  1.000000             NaN\n",
      " Tmem27      35.617493  0.001959  1.000000             NaN\n",
      " Sphkap      34.646255  0.009978  0.833333             NaN\n",
      "  Rab37      31.131083  0.009978  0.833333             NaN\n",
      "    Cpe      26.859491  0.001959  1.000000             NaN\n",
      "   Chga      24.825945  0.001959  1.000000             NaN\n",
      "   Rbp4      23.749779  0.001959  1.000000             NaN\n",
      "   Bex2      17.820747  0.001959  1.000000             NaN\n",
      "   Scg3      16.006983  0.009978  0.833333             NaN\n",
      "Slc25a5      15.879355  0.001959  1.000000             NaN\n",
      "  Pcsk2      15.673647  0.009978  0.833333             NaN\n",
      "Fam183b      15.664439  0.001959  1.000000             NaN\n",
      "  Aplp1      14.979936  0.001959  1.000000             NaN\n",
      "Slc38a5      14.898108  0.009978  0.833333             NaN\n",
      "\n",
      "── DEG drivers: Beta vs progenitor ──\n",
      "   Significant: 1014  (up: 590, down: 424)\n",
      "    gene  logfoldchange     pval_adj  pct_fate  pct_progenitor\n",
      "    Iapp     319.308167 2.691527e-58  1.000000             NaN\n",
      "    Ins1     231.821686 5.935833e-49  0.919643             NaN\n",
      "     Pyy     176.222336 8.209008e-49  0.946429             NaN\n",
      "    Ins2     114.226288 5.625770e-55  0.964286             NaN\n",
      "    Nnat      80.990791 9.460143e-56  0.973214             NaN\n",
      "    Gnas      60.395771 2.056687e-58  1.000000             NaN\n",
      "    Rbp4      47.380043 3.605727e-57  0.991071             NaN\n",
      "  Malat1      39.235798 2.014396e-35  1.000000             NaN\n",
      "    Chgb      33.063000 2.056687e-58  1.000000             NaN\n",
      "    Gng4      30.302731 9.661754e-17  0.517857             NaN\n",
      " Slc30a8      30.090532 3.339000e-13  0.455357             NaN\n",
      "    Lrp8      29.957945 3.619816e-14  0.473214             NaN\n",
      "Arhgap36      29.909950 3.719994e-08  0.348214             NaN\n",
      "   Gnao1      29.817169 3.620658e-15  0.491071             NaN\n",
      "  Sphkap      29.655596 9.837795e-13  0.446429             NaN\n",
      "  Snap25      29.162764 3.719994e-08  0.348214             NaN\n",
      "   Cntfr      28.942442 4.068914e-06  0.294643             NaN\n",
      "     Ttr      28.782820 2.847681e-24  1.000000             NaN\n",
      "   Nfasc      28.639511 5.877230e-05  0.258929             NaN\n",
      "   Rab37      28.634060 4.068914e-06  0.294643             NaN\n",
      "\n",
      "── DEG drivers: Epsilon vs progenitor ──\n",
      "   Significant: 687  (up: 373, down: 314)\n",
      "   gene  logfoldchange     pval_adj  pct_fate  pct_progenitor\n",
      "   Ghrl     517.756226 1.204601e-21  1.000000             NaN\n",
      "    Pyy      91.554626 2.922905e-07  0.702703             NaN\n",
      " Cdkn1a      73.212456 1.204601e-21  1.000000             NaN\n",
      "    Cck      39.323284 5.578819e-21  0.972973             NaN\n",
      "   Isl1      39.186619 1.204601e-21  1.000000             NaN\n",
      "   Rbp4      38.788918 5.909970e-20  0.972973             NaN\n",
      " Lrpprc      35.442978 2.316388e-14  0.810811             NaN\n",
      "   Gng4      32.237843 2.168506e-12  0.729730             NaN\n",
      "   Dll3      30.021725 8.853757e-03  0.297297             NaN\n",
      "  Gnao1      29.968044 1.761210e-05  0.459459             NaN\n",
      " Maged2      29.401833 5.480681e-18  0.972973             NaN\n",
      " Lrrtm3      29.124493 3.790129e-03  0.324324             NaN\n",
      "   Sv2c      29.075603 8.853757e-03  0.297297             NaN\n",
      "  Pex5l      28.918749 1.968029e-02  0.270270             NaN\n",
      "  Ap1s2      28.487804 1.968029e-02  0.270270             NaN\n",
      "Neurod1      28.314774 4.144581e-02  0.243243             NaN\n",
      " Malat1      27.702450 5.050441e-14  1.000000             NaN\n",
      "Fam183b      19.508162 1.204601e-21  1.000000             NaN\n",
      "    Ubb      17.677732 1.630272e-16  1.000000             NaN\n",
      "Neurog3      17.597889 2.133376e-16  0.864865             NaN\n",
      "\n",
      "── DEG drivers: Alpha vs progenitor ──\n",
      "   Significant: 1038  (up: 622, down: 416)\n",
      "    gene  logfoldchange     pval_adj  pct_fate  pct_progenitor\n",
      "     Pyy     270.907867 1.095485e-61  0.972527             NaN\n",
      "     Gcg     215.466476 5.573736e-28  0.681319             NaN\n",
      "    Iapp      99.668839 7.016556e-44  0.846154             NaN\n",
      "     Ttr      80.658424 1.391774e-61  1.000000             NaN\n",
      "    Gnas      76.414871 5.342205e-66  1.000000             NaN\n",
      "    Rbp4      55.058666 1.653055e-57  0.967033             NaN\n",
      "  Malat1      50.032120 2.515452e-60  1.000000             NaN\n",
      "    Isl1      42.396778 3.263752e-57  0.917582             NaN\n",
      "   Pcsk2      39.577908 1.611496e-49  0.851648             NaN\n",
      "    Irx2      32.601612 1.794797e-28  0.640110             NaN\n",
      "    Chgb      32.077511 2.788463e-57  0.928571             NaN\n",
      "  Sphkap      31.570948 3.814446e-34  0.703297             NaN\n",
      " Tmem163      30.995466 1.676530e-25  0.604396             NaN\n",
      "   Ncam1      30.466309 4.006713e-19  0.519231             NaN\n",
      "   Celf3      30.442444 1.090874e-21  0.554945             NaN\n",
      "  Vwa5b2      30.106846 1.664818e-14  0.447802             NaN\n",
      "  Cldn11      30.073782 1.500844e-10  0.376374             NaN\n",
      " Slc7a14      29.913431 7.425416e-14  0.436813             NaN\n",
      "Pcsk2os1      29.691513 2.246793e-13  0.428571             NaN\n",
      "   Pcsk1      29.630861 2.426720e-09  0.351648             NaN\n",
      "\n",
      "── DEG drivers: Beta vs progenitor ──\n",
      "   Significant: 1086  (up: 683, down: 403)\n",
      "   gene  logfoldchange     pval_adj  pct_fate  pct_progenitor\n",
      "    Pyy     184.695587 9.281464e-56  0.951952             NaN\n",
      "   Iapp      97.912140 5.577822e-51  0.906907             NaN\n",
      "   Gnas      71.186623 1.157115e-64  1.000000             NaN\n",
      "   Rbp4      64.360428 5.196736e-64  0.996997             NaN\n",
      " Malat1      54.942646 5.491107e-61  1.000000             NaN\n",
      "   Chgb      50.319710 1.168838e-63  0.987988             NaN\n",
      "   Isl1      41.455986 9.597657e-60  0.951952             NaN\n",
      "  Pcsk2      40.564831 6.367650e-63  0.978979             NaN\n",
      "    Ttr      36.196297 3.638217e-49  0.993994             NaN\n",
      "   Chga      34.888813 1.349103e-64  0.996997             NaN\n",
      "Tmem163      31.561834 3.178231e-33  0.702703             NaN\n",
      "  Pcsk1      31.447981 8.408603e-31  0.675676             NaN\n",
      "    Gip      30.551739 3.254021e-03  0.183183             NaN\n",
      "Gdap1l1      30.395351 4.760353e-22  0.567568             NaN\n",
      "  Celf3      30.349344 3.622626e-23  0.582583             NaN\n",
      " Vwa5b2      30.107122 2.787453e-19  0.528529             NaN\n",
      " Cldn11      29.974279 6.003679e-12  0.408408             NaN\n",
      " Plcxd3      29.862917 5.027443e-15  0.462462             NaN\n",
      "  Rab3b      29.845289 1.338192e-12  0.420420             NaN\n",
      "  Ncam1      29.696566 8.602412e-12  0.405405             NaN\n",
      "\n",
      "── DEG drivers: Delta vs progenitor ──\n",
      "   Significant: 723  (up: 387, down: 336)\n",
      "    gene  logfoldchange     pval_adj  pct_fate  pct_progenitor\n",
      "     Pyy     560.111816 1.648064e-23      1.00             NaN\n",
      "     Sst     337.905396 1.058007e-18      0.88             NaN\n",
      "    Rbp4     216.754547 1.648064e-23      1.00             NaN\n",
      "    Iapp     119.899414 1.788893e-23      1.00             NaN\n",
      "  Malat1      65.846184 3.063520e-23      1.00             NaN\n",
      "    Isl1      47.423279 1.648064e-23      1.00             NaN\n",
      "    Gnas      41.713760 1.788893e-23      1.00             NaN\n",
      "   Pcsk2      41.403732 3.230823e-22      0.96             NaN\n",
      "    Frzb      30.745613 6.427865e-10      0.62             NaN\n",
      "Ppp1r14c      30.685139 8.884320e-07      0.50             NaN\n",
      "   Celf3      30.497181 2.939825e-07      0.52             NaN\n",
      "   Rab3b      30.486515 9.601781e-08      0.54             NaN\n",
      "   Kctd8      30.408361 2.597009e-06      0.48             NaN\n",
      "     Ppy      30.359205 5.835742e-08      0.56             NaN\n",
      "  Sez6l2      30.272020 8.884320e-07      0.50             NaN\n",
      "  Fam92b      30.234203 3.057519e-08      0.56             NaN\n",
      " Gdap1l1      30.204899 9.601781e-08      0.54             NaN\n",
      "   Pcsk1      30.201130 2.939825e-07      0.52             NaN\n",
      "   Ncam1      30.071222 7.208312e-06      0.46             NaN\n",
      "    St18      29.514408 2.811024e-04      0.38             NaN\n",
      "\n",
      "── DEG drivers: Epsilon vs progenitor ──\n",
      "   Significant: 848  (up: 493, down: 355)\n",
      "    gene  logfoldchange     pval_adj  pct_fate  pct_progenitor\n",
      "    Ghrl     498.352386 2.375788e-28  1.000000             NaN\n",
      "     Pyy     128.112381 1.626203e-21  0.923077             NaN\n",
      "    Rbp4     120.034706 2.375788e-28  1.000000             NaN\n",
      "     Ttr      51.104588 6.265073e-21  1.000000             NaN\n",
      "    Isl1      50.955517 8.561742e-28  0.984615             NaN\n",
      "    Gnas      50.034081 5.945616e-28  1.000000             NaN\n",
      "  Malat1      42.224209 5.564506e-25  1.000000             NaN\n",
      "     Gcg      38.234589 2.171203e-07  0.553846             NaN\n",
      "    Iapp      36.981209 2.233751e-23  0.923077             NaN\n",
      "   Pcsk2      32.857044 8.084460e-13  0.646154             NaN\n",
      "    Irx2      32.751572 5.533260e-14  0.676923             NaN\n",
      "  Nap1l5      31.045471 3.560414e-10  0.569231             NaN\n",
      "  Sphkap      30.900093 3.560414e-10  0.569231             NaN\n",
      " Gm43194      30.121216 5.042841e-07  0.461538             NaN\n",
      "   Ncam1      30.119257 3.560414e-10  0.569231             NaN\n",
      "Pcsk2os1      30.098072 7.509680e-08  0.492308             NaN\n",
      "   Celf3      29.958424 3.560414e-10  0.569231             NaN\n",
      "  Pou6f2      29.860731 7.749427e-05  0.369231             NaN\n",
      " Tmem163      29.805254 1.251068e-06  0.446154             NaN\n",
      " Gdap1l1      29.723566 7.122236e-06  0.415385             NaN\n",
      "\n",
      "── DEG drivers: Alpha vs progenitor ──\n",
      "   Significant: 934  (up: 497, down: 437)\n",
      "    gene  logfoldchange     pval_adj  pct_fate  pct_progenitor\n",
      "     Pyy     219.155823 2.833656e-45  0.954955             NaN\n",
      "     Gcg     203.555252 7.818328e-14  0.576577             NaN\n",
      "    Iapp     104.576691 4.358536e-37  0.873874             NaN\n",
      "     Ttr      77.493851 8.624462e-45  1.000000             NaN\n",
      "    Gnas      75.978882 1.650709e-51  1.000000             NaN\n",
      "    Chgb      46.177563 7.129069e-43  0.909910             NaN\n",
      "  Malat1      43.927719 4.928366e-39  1.000000             NaN\n",
      "    Rbp4      42.754589 2.532834e-41  0.945946             NaN\n",
      "BC048546      32.823662 8.888518e-33  0.783784             NaN\n",
      " Slc38a5      31.937300 2.120742e-43  0.909910             NaN\n",
      "    Etv1      31.794817 8.486850e-23  0.648649             NaN\n",
      "  Sphkap      31.697752 3.348922e-27  0.711712             NaN\n",
      "   Gnao1      31.172338 9.190003e-19  0.585586             NaN\n",
      " Tmem163      31.162647 3.621997e-17  0.558559             NaN\n",
      " Slc30a8      30.911142 1.587695e-09  0.405405             NaN\n",
      "    Rai2      30.712553 7.951286e-13  0.477477             NaN\n",
      "   Cadps      30.268208 3.608526e-15  0.522523             NaN\n",
      " Gm43861      29.907495 1.958137e-08  0.378378             NaN\n",
      " Slc7a14      29.898027 3.749925e-09  0.396396             NaN\n",
      " Gdap1l1      29.892195 2.704527e-10  0.423423             NaN\n",
      "\n",
      "── DEG drivers: Beta vs progenitor ──\n",
      "   Significant: 1088  (up: 657, down: 431)\n",
      "    gene  logfoldchange     pval_adj  pct_fate  pct_progenitor\n",
      "    Iapp     291.223389 2.768662e-61  0.986301             NaN\n",
      "     Pyy     196.978867 2.015121e-62  1.000000             NaN\n",
      "    Ins1     185.555130 2.981718e-27  0.705479             NaN\n",
      "    Ins2      77.237167 2.139489e-40  0.794521             NaN\n",
      "    Nnat      70.255356 3.086069e-50  0.904110             NaN\n",
      "    Gnas      64.764244 6.202128e-63  1.000000             NaN\n",
      "    Rbp4      56.959015 6.202128e-63  1.000000             NaN\n",
      "  Malat1      37.846973 4.326653e-44  1.000000             NaN\n",
      "    Chgb      32.391380 1.631449e-61  0.986301             NaN\n",
      "     Ttr      31.606613 9.026741e-38  0.993151             NaN\n",
      " Tmem163      31.063684 7.440471e-29  0.664384             NaN\n",
      "   G6pc2      30.708227 6.214345e-22  0.575342             NaN\n",
      "BC048546      30.439980 5.871768e-21  0.561644             NaN\n",
      " Slc30a8      30.429060 5.176797e-20  0.547945             NaN\n",
      "Arhgap36      30.284391 4.239945e-09  0.356164             NaN\n",
      "    Frzb      30.184422 5.057499e-16  0.486301             NaN\n",
      " Gdap1l1      30.173061 1.983171e-22  0.582192             NaN\n",
      "   Gnao1      30.065212 9.701277e-18  0.513699             NaN\n",
      " Slc7a14      29.919584 1.935221e-16  0.493151             NaN\n",
      "  Sphkap      29.781712 7.166407e-17  0.500000             NaN\n",
      "\n",
      "── DEG drivers: Delta vs progenitor ──\n",
      "   Significant: 479  (up: 210, down: 269)\n",
      "    gene  logfoldchange     pval_adj  pct_fate  pct_progenitor\n",
      "     Pyy     464.324585 1.610999e-10  1.000000             NaN\n",
      "     Sst     338.118744 1.073547e-07  0.833333             NaN\n",
      "    Rbp4     210.968674 1.610999e-10  1.000000             NaN\n",
      "    Iapp      72.105080 1.949895e-09  0.944444             NaN\n",
      "  Malat1      58.503399 1.071009e-09  1.000000             NaN\n",
      "    Gnas      40.954994 1.610999e-10  1.000000             NaN\n",
      "    Isl1      34.577591 1.610999e-10  1.000000             NaN\n",
      "Ppp1r14c      32.689064 6.655484e-08  0.833333             NaN\n",
      "    Frzb      31.795914 4.198904e-06  0.722222             NaN\n",
      "    Rai2      31.174133 1.386014e-04  0.611111             NaN\n",
      "   Gnao1      31.094294 1.386014e-04  0.611111             NaN\n",
      "    St18      30.554657 1.386014e-04  0.611111             NaN\n",
      "BC048546      30.449245 2.682706e-02  0.388889             NaN\n",
      " Gdap1l1      30.255129 8.792385e-03  0.444444             NaN\n",
      "  Kcnmb2      30.096973 8.792385e-03  0.444444             NaN\n",
      "   Scn3a      30.061802 6.245587e-04  0.555556             NaN\n",
      "    Etv1      29.126734 2.682706e-02  0.388889             NaN\n",
      "    Hhex      21.994329 9.207233e-10  1.000000             NaN\n",
      " Fam183b      19.523029 1.610999e-10  1.000000             NaN\n",
      "     Ubb      19.173141 2.719530e-07  1.000000             NaN\n",
      "\n",
      "── DEG drivers: Epsilon vs progenitor ──\n",
      "   Significant: 783  (up: 422, down: 361)\n",
      "    gene  logfoldchange     pval_adj  pct_fate  pct_progenitor\n",
      "    Ghrl     485.379456 2.998483e-22     1.000             NaN\n",
      "     Pyy     163.205170 3.450739e-19     0.950             NaN\n",
      "    Rbp4      99.282028 2.998483e-22     1.000             NaN\n",
      "  Lrpprc      56.673809 3.247204e-18     0.900             NaN\n",
      "    Isl1      47.636112 2.998483e-22     1.000             NaN\n",
      "  Malat1      46.982475 8.548388e-19     1.000             NaN\n",
      "  Mboat4      33.095497 1.517948e-12     0.725             NaN\n",
      "   Gnao1      32.037502 9.017539e-12     0.700             NaN\n",
      "    Etv1      30.398169 6.235255e-06     0.475             NaN\n",
      "  Nap1l5      30.321486 4.682076e-07     0.525             NaN\n",
      "    St18      30.176975 4.682076e-07     0.525             NaN\n",
      "Ppp1r14c      30.062613 2.013902e-05     0.450             NaN\n",
      " Tmem163      29.851349 4.848064e-04     0.375             NaN\n",
      " Gm43861      29.556721 2.013902e-05     0.450             NaN\n",
      "   Cadps      29.550922 2.013902e-05     0.450             NaN\n",
      "BC048546      29.533049 2.946907e-03     0.325             NaN\n",
      "  Pou6f2      29.491068 1.392431e-02     0.275             NaN\n",
      "Serping1      29.471628 2.013902e-05     0.450             NaN\n",
      "    Rai2      29.452677 1.392431e-02     0.275             NaN\n",
      "   Gria2      29.375956 4.848064e-04     0.375             NaN\n",
      "Top DEG drivers (top 5 per fate, per condition):\n"
     ]
    },
    {
     "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>condition</th>\n",
       "      <th>fate</th>\n",
       "      <th>gene</th>\n",
       "      <th>logfoldchange</th>\n",
       "      <th>pval_adj</th>\n",
       "      <th>pct_fate</th>\n",
       "      <th>pct_progenitor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>Iapp</td>\n",
       "      <td>165.372681</td>\n",
       "      <td>1.959313e-03</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>Ttr</td>\n",
       "      <td>143.253738</td>\n",
       "      <td>1.959313e-03</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>Pyy</td>\n",
       "      <td>99.835434</td>\n",
       "      <td>1.959313e-03</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>Chgb</td>\n",
       "      <td>69.993912</td>\n",
       "      <td>1.959313e-03</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>Gnas</td>\n",
       "      <td>59.068867</td>\n",
       "      <td>1.959313e-03</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</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>50</th>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Ghrl</td>\n",
       "      <td>485.379456</td>\n",
       "      <td>2.998483e-22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Pyy</td>\n",
       "      <td>163.205170</td>\n",
       "      <td>3.450739e-19</td>\n",
       "      <td>0.95</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Rbp4</td>\n",
       "      <td>99.282028</td>\n",
       "      <td>2.998483e-22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Lrpprc</td>\n",
       "      <td>56.673809</td>\n",
       "      <td>3.247204e-18</td>\n",
       "      <td>0.90</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Isl1</td>\n",
       "      <td>47.636112</td>\n",
       "      <td>2.998483e-22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        condition     fate    gene  logfoldchange      pval_adj  pct_fate  pct_progenitor\n",
       "0   high_velocity    Alpha    Iapp     165.372681  1.959313e-03      1.00             NaN\n",
       "1   high_velocity    Alpha     Ttr     143.253738  1.959313e-03      1.00             NaN\n",
       "2   high_velocity    Alpha     Pyy      99.835434  1.959313e-03      1.00             NaN\n",
       "3   high_velocity    Alpha    Chgb      69.993912  1.959313e-03      1.00             NaN\n",
       "4   high_velocity    Alpha    Gnas      59.068867  1.959313e-03      1.00             NaN\n",
       "..            ...      ...     ...            ...           ...       ...             ...\n",
       "50   med_velocity  Epsilon    Ghrl     485.379456  2.998483e-22      1.00             NaN\n",
       "51   med_velocity  Epsilon     Pyy     163.205170  3.450739e-19      0.95             NaN\n",
       "52   med_velocity  Epsilon    Rbp4      99.282028  2.998483e-22      1.00             NaN\n",
       "53   med_velocity  Epsilon  Lrpprc      56.673809  3.247204e-18      0.90             NaN\n",
       "54   med_velocity  Epsilon    Isl1      47.636112  2.998483e-22      1.00             NaN\n",
       "\n",
       "[55 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deg_drivers_per_cond = {}\n",
    "for cond in mscorer.conditions:\n",
    "    mask = (adata_sub_shared.obs[mscorer.condition_obs_key].astype(str) == cond).values\n",
    "    sub = adata_sub_shared[mask].copy()\n",
    "    deg_drivers_per_cond[cond] = get_deg_drivers(\n",
    "        sub,\n",
    "        fate_names=fate_names,\n",
    "        obs_key=obs_key_used,\n",
    "        root=root_cluster,\n",
    "        n_top_genes=20,\n",
    "        pval_threshold=0.05,\n",
    "        logfc_threshold=0.25,\n",
    "    )\n",
    "\n",
    "# One compact table: (condition, fate, gene, logfoldchange, pval_adj, pct_*)\n",
    "print(\"Top DEG drivers (top 5 per fate, per condition):\")\n",
    "_stack_drivers_by_condition(\n",
    "    deg_drivers_per_cond,\n",
    "    n_per_fate=5,\n",
    "    cols=[\"gene\", \"logfoldchange\", \"pval_adj\", \"pct_fate\", \"pct_progenitor\"],\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "faed744c",
   "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>n_conds</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alpha</th>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epsilon</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "n_conds   1  2  3\n",
       "fate             \n",
       "Alpha     9  3  5\n",
       "Beta      4  4  6\n",
       "Epsilon  12  3  4"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Driver overlap across all 3 conditions:\n",
    "# rows = (fate, gene); columns = condition presence + n_conds + shared_all flag.\n",
    "overlap = _driver_overlap(deg_drivers_per_cond, n_top=10, gene_col=\"gene\")\n",
    "\n",
    "# Cross-tab: how many genes are shared by exactly k conditions, per fate?\n",
    "overlap.groupby([\"fate\", \"n_conds\"]).size().unstack(fill_value=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "41d43edf",
   "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>fate</th>\n",
       "      <th>gene</th>\n",
       "      <th>high_velocity</th>\n",
       "      <th>low_velocity</th>\n",
       "      <th>med_velocity</th>\n",
       "      <th>n_conds</th>\n",
       "      <th>shared_all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>BC048546</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>Gnao1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>Irx2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>Isl1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>Pcsk1n</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>Pcsk2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>Slc38a5</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>Sphkap</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>Tmem27</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Beta</td>\n",
       "      <td>Chga</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Beta</td>\n",
       "      <td>Gng4</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Beta</td>\n",
       "      <td>Isl1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Beta</td>\n",
       "      <td>Pcsk2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Cck</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Cdkn1a</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Dll3</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Etv1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Gcg</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Gnas</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Gng4</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Iapp</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Mboat4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Nap1l5</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Pcsk2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Epsilon</td>\n",
       "      <td>Ttr</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fate      gene  high_velocity  low_velocity  med_velocity  n_conds  shared_all\n",
       "0     Alpha  BC048546          False         False          True        1       False\n",
       "3     Alpha     Gnao1           True         False         False        1       False\n",
       "6     Alpha      Irx2          False          True         False        1       False\n",
       "7     Alpha      Isl1          False          True         False        1       False\n",
       "9     Alpha    Pcsk1n           True         False         False        1       False\n",
       "10    Alpha     Pcsk2          False          True         False        1       False\n",
       "13    Alpha   Slc38a5          False         False          True        1       False\n",
       "14    Alpha    Sphkap           True         False         False        1       False\n",
       "15    Alpha    Tmem27           True         False         False        1       False\n",
       "17     Beta      Chga          False          True         False        1       False\n",
       "20     Beta      Gng4           True         False         False        1       False\n",
       "24     Beta      Isl1          False          True         False        1       False\n",
       "27     Beta     Pcsk2          False          True         False        1       False\n",
       "31  Epsilon       Cck           True         False         False        1       False\n",
       "32  Epsilon    Cdkn1a           True         False         False        1       False\n",
       "33  Epsilon      Dll3           True         False         False        1       False\n",
       "34  Epsilon      Etv1          False         False          True        1       False\n",
       "35  Epsilon       Gcg          False          True         False        1       False\n",
       "38  Epsilon      Gnas          False          True         False        1       False\n",
       "39  Epsilon      Gng4           True         False         False        1       False\n",
       "40  Epsilon      Iapp          False          True         False        1       False\n",
       "44  Epsilon    Mboat4          False         False          True        1       False\n",
       "45  Epsilon    Nap1l5          False         False          True        1       False\n",
       "46  Epsilon     Pcsk2          False          True         False        1       False\n",
       "49  Epsilon       Ttr          False          True         False        1       False"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the genes that are condition-specific (n_conds == 1) for each fate.\n",
    "specific = overlap[overlap[\"n_conds\"] == 1]\n",
    "specific.head(30)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77db2976",
   "metadata": {},
   "source": [
    "## 11. Pathway enrichment per condition\n",
    "\n",
    "We run `run_enrichment_per_fate` on the per-condition DEG drivers (offline-safe: wrapped in try/except). The compact `_stack_enrichment_by_condition` helper builds one tidy table with columns `(condition, fate, direction, Term, Adjusted P-value, Overlap, ...)`.\n",
    "\n",
    "> The cell will be empty in published HTML if `gseapy` cannot reach Enrichr (no internet during doc build). Run interactively for live results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0d0ddd45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "  Pathway enrichment: Alpha\n",
      "  Gene sets: ['KEGG_2019_Mouse', 'GO_Biological_Process_2021', 'Reactome_2022']\n",
      "  Up-regulated genes  : 80\n",
      "  Down-regulated genes: 90\n",
      "============================================================\n",
      "\n",
      "  [up] Significant terms: 1\n",
      "       Gene_set                      Term Overlap  Adjusted P-value\n",
      "KEGG_2019_Mouse Thyroid hormone synthesis    4/73          0.026393\n",
      "\n",
      "  [down] Significant terms: 107\n",
      "                  Gene_set                                                                             Term Overlap  Adjusted P-value\n",
      "GO_Biological_Process_2021         SRP-dependent cotranslational protein targeting to membrane (GO:0006614)   25/90      4.833486e-36\n",
      "GO_Biological_Process_2021                                             cytoplasmic translation (GO:0002181)   25/93      5.612907e-36\n",
      "GO_Biological_Process_2021                       cotranslational protein targeting to membrane (GO:0006613)   25/94      5.612907e-36\n",
      "GO_Biological_Process_2021                                             protein targeting to ER (GO:0045047)  25/103      5.632949e-35\n",
      "GO_Biological_Process_2021 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay (GO:0000184)  25/113      6.008108e-34\n",
      "             Reactome_2022                                   Eukaryotic Translation Elongation R-HSA-156842   23/90      6.362739e-33\n",
      "             Reactome_2022                                           Selenocysteine Synthesis R-HSA-2408557   23/90      6.362739e-33\n",
      "             Reactome_2022                                              Viral mRNA Translation R-HSA-192823   23/90      6.362739e-33\n",
      "             Reactome_2022                                            Peptide Chain Elongation R-HSA-156902   23/86      6.362739e-33\n",
      "             Reactome_2022                                   Eukaryotic Translation Termination R-HSA-72764   23/90      6.362739e-33\n",
      "\n",
      "============================================================\n",
      "  Pathway enrichment: Beta\n",
      "  Gene sets: ['KEGG_2019_Mouse', 'GO_Biological_Process_2021', 'Reactome_2022']\n",
      "  Up-regulated genes  : 590\n",
      "  Down-regulated genes: 424\n",
      "============================================================\n",
      "\n",
      "  [up] Significant terms: 208\n",
      "                  Gene_set                                                 Term Overlap  Adjusted P-value\n",
      "           KEGG_2019_Mouse          Protein processing in endoplasmic reticulum  27/163      7.997215e-11\n",
      "GO_Biological_Process_2021         regulation of insulin secretion (GO:0050796)  20/104      6.403333e-08\n",
      "           KEGG_2019_Mouse                 Maturity onset diabetes of the young   10/27      3.115636e-07\n",
      "           KEGG_2019_Mouse                                    Insulin secretion   16/86      3.212812e-07\n",
      "GO_Biological_Process_2021 IRE1-mediated unfolded protein response (GO:0036498)   14/53      3.765361e-07\n",
      "             Reactome_2022         Regulation Of Insulin Secretion R-HSA-422356   16/77      7.171901e-07\n",
      "             Reactome_2022        Integration Of Energy Metabolism R-HSA-163685  18/105      8.433568e-07\n",
      "             Reactome_2022             Peptide Hormone Metabolism R-HSA-2980736   16/89      2.224553e-06\n",
      "             Reactome_2022          IRE1alpha Activates Chaperones R-HSA-381070   12/48      2.650485e-06\n",
      "GO_Biological_Process_2021 regulation of peptide hormone secretion (GO:0090276)   15/74      3.172567e-06\n",
      "\n",
      "  [down] Significant terms: 397\n",
      "                  Gene_set                                                                             Term Overlap  Adjusted P-value\n",
      "GO_Biological_Process_2021                       cotranslational protein targeting to membrane (GO:0006613)   31/94      2.277097e-25\n",
      "GO_Biological_Process_2021         SRP-dependent cotranslational protein targeting to membrane (GO:0006614)   30/90      6.161288e-25\n",
      "GO_Biological_Process_2021                                             cytoplasmic translation (GO:0002181)   30/93      1.266777e-24\n",
      "GO_Biological_Process_2021                         cellular macromolecule biosynthetic process (GO:0034645)  48/314      1.805540e-24\n",
      "GO_Biological_Process_2021                                             protein targeting to ER (GO:0045047)  30/103      2.379529e-23\n",
      "             Reactome_2022                                            Peptide Chain Elongation R-HSA-156902   28/86      5.980082e-23\n",
      "             Reactome_2022                                   Eukaryotic Translation Elongation R-HSA-156842   28/90      1.267632e-22\n",
      "GO_Biological_Process_2021 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay (GO:0000184)  30/113      4.173200e-22\n",
      "             Reactome_2022                                              Viral mRNA Translation R-HSA-192823   27/90      9.255427e-22\n",
      "             Reactome_2022                                   Eukaryotic Translation Termination R-HSA-72764   27/90      9.255427e-22\n",
      "\n",
      "============================================================\n",
      "  Pathway enrichment: Epsilon\n",
      "  Gene sets: ['KEGG_2019_Mouse', 'GO_Biological_Process_2021', 'Reactome_2022']\n",
      "  Up-regulated genes  : 373\n",
      "  Down-regulated genes: 314\n",
      "============================================================\n",
      "\n",
      "  [up] Significant terms: 86\n",
      "                  Gene_set                                                          Term Overlap  Adjusted P-value\n",
      "GO_Biological_Process_2021         positive regulation of insulin secretion (GO:0032024)   10/38          0.000003\n",
      "           KEGG_2019_Mouse                          Maturity onset diabetes of the young    8/27          0.000005\n",
      "           KEGG_2019_Mouse                   Protein processing in endoplasmic reticulum  15/163          0.000050\n",
      "GO_Biological_Process_2021                  regulation of insulin secretion (GO:0050796)  13/104          0.000085\n",
      "GO_Biological_Process_2021            cellular response to insulin stimulus (GO:0032869)  14/129          0.000108\n",
      "           KEGG_2019_Mouse                                                      Lysosome  12/124          0.000285\n",
      "             Reactome_2022                      Peptide Hormone Metabolism R-HSA-2980736   11/89          0.000615\n",
      "             Reactome_2022      Regulation Of Gene Expression In Beta Cells R-HSA-210745    6/20          0.000615\n",
      "GO_Biological_Process_2021 positive regulation of peptide hormone secretion (GO:0090277)    8/43          0.000668\n",
      "           KEGG_2019_Mouse                                        Synaptic vesicle cycle    9/77          0.000772\n",
      "\n",
      "  [down] Significant terms: 336\n",
      "                  Gene_set                                                                             Term Overlap  Adjusted P-value\n",
      "GO_Biological_Process_2021                       cotranslational protein targeting to membrane (GO:0006613)   31/94      1.597058e-29\n",
      "GO_Biological_Process_2021         SRP-dependent cotranslational protein targeting to membrane (GO:0006614)   30/90      5.896162e-29\n",
      "GO_Biological_Process_2021                                             cytoplasmic translation (GO:0002181)   30/93      1.232166e-28\n",
      "GO_Biological_Process_2021                                             protein targeting to ER (GO:0045047)  30/103      3.054501e-27\n",
      "             Reactome_2022                                            Peptide Chain Elongation R-HSA-156902   28/86      1.170006e-26\n",
      "             Reactome_2022                                   Eukaryotic Translation Elongation R-HSA-156842   28/90      2.534405e-26\n",
      "GO_Biological_Process_2021                         cellular macromolecule biosynthetic process (GO:0034645)  44/314      2.536834e-26\n",
      "GO_Biological_Process_2021 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay (GO:0000184)  30/113      4.524592e-26\n",
      "             Reactome_2022                                   Eukaryotic Translation Termination R-HSA-72764   27/90      3.086957e-25\n",
      "             Reactome_2022                                           Selenocysteine Synthesis R-HSA-2408557   27/90      3.086957e-25\n",
      "\n",
      "============================================================\n",
      "  Pathway enrichment: Alpha\n",
      "  Gene sets: ['KEGG_2019_Mouse', 'GO_Biological_Process_2021', 'Reactome_2022']\n",
      "  Up-regulated genes  : 622\n",
      "  Down-regulated genes: 416\n",
      "============================================================\n",
      "\n",
      "  [up] Significant terms: 156\n",
      "                  Gene_set                                                  Term Overlap  Adjusted P-value\n",
      "GO_Biological_Process_2021          regulation of insulin secretion (GO:0050796)  22/104      2.445585e-09\n",
      "           KEGG_2019_Mouse                                              Lysosome  21/124      6.084043e-08\n",
      "             Reactome_2022          Regulation Of Insulin Secretion R-HSA-422356   17/77      1.756719e-07\n",
      "             Reactome_2022         Integration Of Energy Metabolism R-HSA-163685  19/105      2.811906e-07\n",
      "GO_Biological_Process_2021 positive regulation of insulin secretion (GO:0032024)   12/38      1.367715e-06\n",
      "             Reactome_2022              Peptide Hormone Metabolism R-HSA-2980736   16/89      4.795716e-06\n",
      "           KEGG_2019_Mouse                                  Dopaminergic synapse  19/135      5.062926e-06\n",
      "GO_Biological_Process_2021  regulation of peptide hormone secretion (GO:0090276)   15/74      6.661253e-06\n",
      "             Reactome_2022                G-protein Mediated Events R-HSA-112040   12/52      1.289888e-05\n",
      "             Reactome_2022                         Opioid Signaling R-HSA-111885   15/88      1.713559e-05\n",
      "\n",
      "  [down] Significant terms: 444\n",
      "                  Gene_set                                                                             Term Overlap  Adjusted P-value\n",
      "GO_Biological_Process_2021                         cellular macromolecule biosynthetic process (GO:0034645)  52/314      2.873895e-28\n",
      "GO_Biological_Process_2021                                             cytoplasmic translation (GO:0002181)   31/93      4.126134e-26\n",
      "GO_Biological_Process_2021                       cotranslational protein targeting to membrane (GO:0006613)   31/94      4.126134e-26\n",
      "             Reactome_2022                                                         Cell Cycle R-HSA-1640170  68/654      1.190819e-25\n",
      "             Reactome_2022                                                  Cell Cycle, Mitotic R-HSA-69278  61/523      1.190819e-25\n",
      "GO_Biological_Process_2021         SRP-dependent cotranslational protein targeting to membrane (GO:0006614)   30/90      1.707288e-25\n",
      "             Reactome_2022                                            Peptide Chain Elongation R-HSA-156902   28/86      1.181197e-23\n",
      "GO_Biological_Process_2021                                             protein targeting to ER (GO:0045047)  30/103      1.233512e-23\n",
      "GO_Biological_Process_2021 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay (GO:0000184)  31/113      1.233512e-23\n",
      "             Reactome_2022                                   Eukaryotic Translation Elongation R-HSA-156842   28/90      2.507804e-23\n",
      "\n",
      "============================================================\n",
      "  Pathway enrichment: Beta\n",
      "  Gene sets: ['KEGG_2019_Mouse', 'GO_Biological_Process_2021', 'Reactome_2022']\n",
      "  Up-regulated genes  : 683\n",
      "  Down-regulated genes: 403\n",
      "============================================================\n",
      "\n",
      "  [up] Significant terms: 129\n",
      "                  Gene_set                                                  Term Overlap  Adjusted P-value\n",
      "           KEGG_2019_Mouse                  Maturity onset diabetes of the young   13/27      2.565614e-10\n",
      "GO_Biological_Process_2021          regulation of insulin secretion (GO:0050796)  22/104      1.618268e-08\n",
      "GO_Biological_Process_2021  regulation of peptide hormone secretion (GO:0090276)   16/74      4.589452e-06\n",
      "           KEGG_2019_Mouse           Protein processing in endoplasmic reticulum  22/163      5.130451e-06\n",
      "GO_Biological_Process_2021          neuron projection morphogenesis (GO:0048812)  21/140      1.223028e-05\n",
      "           KEGG_2019_Mouse                                              Lysosome  18/124      1.923302e-05\n",
      "GO_Biological_Process_2021 positive regulation of insulin secretion (GO:0032024)   11/38      2.647650e-05\n",
      "             Reactome_2022          Regulation Of Insulin Secretion R-HSA-422356   15/77      4.590873e-05\n",
      "             Reactome_2022         Integration Of Energy Metabolism R-HSA-163685  17/105      5.146225e-05\n",
      "GO_Biological_Process_2021            neuron projection development (GO:0031175)  22/171      5.680492e-05\n",
      "\n",
      "  [down] Significant terms: 422\n",
      "                  Gene_set                                                                             Term Overlap  Adjusted P-value\n",
      "GO_Biological_Process_2021                         cellular macromolecule biosynthetic process (GO:0034645)  50/314      6.593606e-27\n",
      "GO_Biological_Process_2021                                             cytoplasmic translation (GO:0002181)   31/93      1.544213e-26\n",
      "GO_Biological_Process_2021         SRP-dependent cotranslational protein targeting to membrane (GO:0006614)   30/90      8.594398e-26\n",
      "GO_Biological_Process_2021                       cotranslational protein targeting to membrane (GO:0006613)   30/94      2.875876e-25\n",
      "             Reactome_2022                                                  Cell Cycle, Mitotic R-HSA-69278  60/523      2.890128e-25\n",
      "             Reactome_2022                                                         Cell Cycle R-HSA-1640170  65/654      2.708074e-24\n",
      "GO_Biological_Process_2021 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay (GO:0000184)  31/113      4.564548e-24\n",
      "GO_Biological_Process_2021                                             protein targeting to ER (GO:0045047)  30/103      4.564548e-24\n",
      "             Reactome_2022                                            Peptide Chain Elongation R-HSA-156902   28/86      4.816521e-24\n",
      "             Reactome_2022                                   Eukaryotic Translation Termination R-HSA-72764   28/90      1.230265e-23\n",
      "\n",
      "============================================================\n",
      "  Pathway enrichment: Delta\n",
      "  Gene sets: ['KEGG_2019_Mouse', 'GO_Biological_Process_2021', 'Reactome_2022']\n",
      "  Up-regulated genes  : 387\n",
      "  Down-regulated genes: 336\n",
      "============================================================\n",
      "\n",
      "  [up] Significant terms: 125\n",
      "                  Gene_set                                                          Term Overlap  Adjusted P-value\n",
      "GO_Biological_Process_2021 positive regulation of peptide hormone secretion (GO:0090277)    9/43          0.000122\n",
      "GO_Biological_Process_2021                  regulation of insulin secretion (GO:0050796)  13/104          0.000122\n",
      "           KEGG_2019_Mouse                                                      Lysosome  13/124          0.000200\n",
      "           KEGG_2019_Mouse                                          Dopaminergic synapse  13/135          0.000210\n",
      "           KEGG_2019_Mouse                                         Circadian entrainment   11/99          0.000210\n",
      "           KEGG_2019_Mouse                   Protein processing in endoplasmic reticulum  14/163          0.000210\n",
      "GO_Biological_Process_2021         positive regulation of insulin secretion (GO:0032024)    8/38          0.000400\n",
      "           KEGG_2019_Mouse                          Maturity onset diabetes of the young    6/27          0.000492\n",
      "           KEGG_2019_Mouse                                        cAMP signaling pathway  15/211          0.000636\n",
      "           KEGG_2019_Mouse                                        Long-term potentiation    8/67          0.001459\n",
      "\n",
      "  [down] Significant terms: 448\n",
      "                  Gene_set                                                                                  Term Overlap  Adjusted P-value\n",
      "GO_Biological_Process_2021                              cellular macromolecule biosynthetic process (GO:0034645)  50/314      8.751611e-31\n",
      "GO_Biological_Process_2021                                                  cytoplasmic translation (GO:0002181)   31/93      4.906936e-29\n",
      "GO_Biological_Process_2021                            cotranslational protein targeting to membrane (GO:0006613)   31/94      4.906936e-29\n",
      "GO_Biological_Process_2021              SRP-dependent cotranslational protein targeting to membrane (GO:0006614)   30/90      2.530390e-28\n",
      "GO_Biological_Process_2021                                                  protein targeting to ER (GO:0045047)  30/103      1.723374e-26\n",
      "GO_Biological_Process_2021      nuclear-transcribed mRNA catabolic process, nonsense-mediated decay (GO:0000184)  31/113      1.723374e-26\n",
      "             Reactome_2022                                                 Peptide Chain Elongation R-HSA-156902   28/86      8.381928e-26\n",
      "             Reactome_2022                                        Eukaryotic Translation Elongation R-HSA-156842   28/90      1.205212e-25\n",
      "             Reactome_2022                                        Eukaryotic Translation Termination R-HSA-72764   28/90      1.205212e-25\n",
      "             Reactome_2022 Nonsense Mediated Decay (NMD) Independent Of Exon Junction Complex (EJC) R-HSA-975956   28/92      1.821364e-25\n",
      "\n",
      "============================================================\n",
      "  Pathway enrichment: Epsilon\n",
      "  Gene sets: ['KEGG_2019_Mouse', 'GO_Biological_Process_2021', 'Reactome_2022']\n",
      "  Up-regulated genes  : 493\n",
      "  Down-regulated genes: 355\n",
      "============================================================\n",
      "\n",
      "  [up] Significant terms: 87\n",
      "       Gene_set                                                                                                      Term Overlap  Adjusted P-value\n",
      "KEGG_2019_Mouse                                                                                                  Lysosome  19/124      4.820439e-08\n",
      "  Reactome_2022                                                                  Peptide Hormone Metabolism R-HSA-2980736   16/89      4.651280e-07\n",
      "  Reactome_2022                                                              Regulation Of Insulin Secretion R-HSA-422356   13/77      2.184121e-05\n",
      "  Reactome_2022                                                             Integration Of Energy Metabolism R-HSA-163685  14/105      9.460261e-05\n",
      "  Reactome_2022                                              Incretin Synthesis, Secretion, And Inactivation R-HSA-400508    7/24      3.011596e-04\n",
      "KEGG_2019_Mouse                                                               Protein processing in endoplasmic reticulum  16/163      3.382496e-04\n",
      "  Reactome_2022                                                                    G-protein Mediated Events R-HSA-112040    9/52      8.465563e-04\n",
      "  Reactome_2022                                                                                   Hemostasis R-HSA-109582  33/576      1.113725e-03\n",
      "  Reactome_2022                    Synthesis, Secretion, And Inactivation Of Glucagon-like Peptide-1 (GLP-1) R-HSA-381771    6/21      1.151083e-03\n",
      "  Reactome_2022 Synthesis, Secretion, And Inactivation Of Glucose-dependent Insulinotropic Polypeptide (GIP) R-HSA-400511    5/13      1.151083e-03\n",
      "\n",
      "  [down] Significant terms: 469\n",
      "                  Gene_set                                                                                  Term Overlap  Adjusted P-value\n",
      "GO_Biological_Process_2021                              cellular macromolecule biosynthetic process (GO:0034645)  50/314      1.342079e-29\n",
      "GO_Biological_Process_2021                                                  cytoplasmic translation (GO:0002181)   31/93      2.855442e-28\n",
      "GO_Biological_Process_2021              SRP-dependent cotranslational protein targeting to membrane (GO:0006614)   30/90      1.815036e-27\n",
      "GO_Biological_Process_2021                            cotranslational protein targeting to membrane (GO:0006613)   30/94      6.131343e-27\n",
      "GO_Biological_Process_2021      nuclear-transcribed mRNA catabolic process, nonsense-mediated decay (GO:0000184)  31/113      9.159060e-26\n",
      "GO_Biological_Process_2021                                                  protein targeting to ER (GO:0045047)  30/103      9.159060e-26\n",
      "             Reactome_2022                                                 Peptide Chain Elongation R-HSA-156902   28/86      4.034992e-25\n",
      "             Reactome_2022                                        Eukaryotic Translation Elongation R-HSA-156842   28/90      5.780159e-25\n",
      "             Reactome_2022                                        Eukaryotic Translation Termination R-HSA-72764   28/90      5.780159e-25\n",
      "             Reactome_2022 Nonsense Mediated Decay (NMD) Independent Of Exon Junction Complex (EJC) R-HSA-975956   28/92      8.718902e-25\n",
      "\n",
      "============================================================\n",
      "  Pathway enrichment: Alpha\n",
      "  Gene sets: ['KEGG_2019_Mouse', 'GO_Biological_Process_2021', 'Reactome_2022']\n",
      "  Up-regulated genes  : 497\n",
      "  Down-regulated genes: 437\n",
      "============================================================\n",
      "\n",
      "  [up] Significant terms: 141\n",
      "                  Gene_set                                                  Term Overlap  Adjusted P-value\n",
      "GO_Biological_Process_2021          regulation of insulin secretion (GO:0050796)  19/104      2.499845e-08\n",
      "             Reactome_2022         Integration Of Energy Metabolism R-HSA-163685  18/105      5.126029e-08\n",
      "             Reactome_2022          Regulation Of Insulin Secretion R-HSA-422356   16/77      5.126029e-08\n",
      "             Reactome_2022              Peptide Hormone Metabolism R-HSA-2980736   15/89      1.587271e-06\n",
      "GO_Biological_Process_2021  regulation of peptide hormone secretion (GO:0090276)   14/74      4.221168e-06\n",
      "           KEGG_2019_Mouse                                  Dopaminergic synapse  17/135      1.054884e-05\n",
      "GO_Biological_Process_2021 positive regulation of insulin secretion (GO:0032024)   10/38      1.757779e-05\n",
      "           KEGG_2019_Mouse                                              Lysosome  15/124      5.576827e-05\n",
      "           KEGG_2019_Mouse                                Synaptic vesicle cycle   11/77      2.462138e-04\n",
      "GO_Biological_Process_2021          regulation of protein secretion (GO:0050708)  15/125      3.236405e-04\n",
      "\n",
      "  [down] Significant terms: 476\n",
      "                  Gene_set                                                                             Term Overlap  Adjusted P-value\n",
      "             Reactome_2022                                                  Cell Cycle, Mitotic R-HSA-69278  65/523      1.514502e-27\n",
      "             Reactome_2022                                                         Cell Cycle R-HSA-1640170  70/654      4.031318e-26\n",
      "GO_Biological_Process_2021                                             cytoplasmic translation (GO:0002181)   31/93      1.940433e-25\n",
      "GO_Biological_Process_2021                         cellular macromolecule biosynthetic process (GO:0034645)  50/314      1.940433e-25\n",
      "GO_Biological_Process_2021                       cotranslational protein targeting to membrane (GO:0006613)   31/94      1.940433e-25\n",
      "GO_Biological_Process_2021         SRP-dependent cotranslational protein targeting to membrane (GO:0006614)   30/90      7.639023e-25\n",
      "             Reactome_2022                                            Peptide Chain Elongation R-HSA-156902   28/86      4.680497e-23\n",
      "GO_Biological_Process_2021 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay (GO:0000184)  31/113      5.687463e-23\n",
      "GO_Biological_Process_2021                                             protein targeting to ER (GO:0045047)  30/103      5.687463e-23\n",
      "             Reactome_2022                                              Viral mRNA Translation R-HSA-192823   28/90      9.896098e-23\n",
      "\n",
      "============================================================\n",
      "  Pathway enrichment: Beta\n",
      "  Gene sets: ['KEGG_2019_Mouse', 'GO_Biological_Process_2021', 'Reactome_2022']\n",
      "  Up-regulated genes  : 657\n",
      "  Down-regulated genes: 431\n",
      "============================================================\n",
      "\n",
      "  [up] Significant terms: 137\n",
      "                  Gene_set                                                 Term Overlap  Adjusted P-value\n",
      "GO_Biological_Process_2021         regulation of insulin secretion (GO:0050796)  24/104      9.438360e-11\n",
      "           KEGG_2019_Mouse          Protein processing in endoplasmic reticulum  27/163      1.008634e-09\n",
      "           KEGG_2019_Mouse                 Maturity onset diabetes of the young   12/27      2.024887e-09\n",
      "GO_Biological_Process_2021 regulation of peptide hormone secretion (GO:0090276)   18/74      2.995445e-08\n",
      "             Reactome_2022         Regulation Of Insulin Secretion R-HSA-422356   18/77      4.483648e-08\n",
      "             Reactome_2022        Integration Of Energy Metabolism R-HSA-163685  20/105      9.876732e-08\n",
      "           KEGG_2019_Mouse                                             Lysosome  20/124      3.369271e-07\n",
      "GO_Biological_Process_2021 IRE1-mediated unfolded protein response (GO:0036498)   14/53      1.061323e-06\n",
      "           KEGG_2019_Mouse                                    Insulin secretion   16/86      1.119385e-06\n",
      "GO_Biological_Process_2021         regulation of protein secretion (GO:0050708)  20/125      3.321822e-06\n",
      "\n",
      "  [down] Significant terms: 476\n",
      "                  Gene_set                                                                             Term Overlap  Adjusted P-value\n",
      "             Reactome_2022                                                  Cell Cycle, Mitotic R-HSA-69278  64/523      4.941445e-27\n",
      "GO_Biological_Process_2021                         cellular macromolecule biosynthetic process (GO:0034645)  51/314      1.748442e-26\n",
      "             Reactome_2022                                                         Cell Cycle R-HSA-1640170  69/654      1.072989e-25\n",
      "GO_Biological_Process_2021                                             cytoplasmic translation (GO:0002181)   31/93      1.271360e-25\n",
      "GO_Biological_Process_2021                       cotranslational protein targeting to membrane (GO:0006613)   31/94      1.271360e-25\n",
      "GO_Biological_Process_2021         SRP-dependent cotranslational protein targeting to membrane (GO:0006614)   30/90      5.075307e-25\n",
      "             Reactome_2022                                            Peptide Chain Elongation R-HSA-156902   28/86      3.238070e-23\n",
      "GO_Biological_Process_2021 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay (GO:0000184)  31/113      3.747490e-23\n",
      "GO_Biological_Process_2021                                             protein targeting to ER (GO:0045047)  30/103      3.747490e-23\n",
      "             Reactome_2022                                   Eukaryotic Translation Termination R-HSA-72764   28/90      8.225337e-23\n",
      "\n",
      "============================================================\n",
      "  Pathway enrichment: Delta\n",
      "  Gene sets: ['KEGG_2019_Mouse', 'GO_Biological_Process_2021', 'Reactome_2022']\n",
      "  Up-regulated genes  : 210\n",
      "  Down-regulated genes: 269\n",
      "============================================================\n",
      "\n",
      "  [up] Significant terms: 72\n",
      "                  Gene_set                                                                   Term Overlap  Adjusted P-value\n",
      "           KEGG_2019_Mouse                                   Maturity onset diabetes of the young    5/27          0.001473\n",
      "             Reactome_2022                               Peptide Hormone Metabolism R-HSA-2980736    8/89          0.002027\n",
      "             Reactome_2022                                          GPER1 Signaling R-HSA-9634597    6/43          0.002027\n",
      "GO_Biological_Process_2021                  positive regulation of insulin secretion (GO:0032024)    6/38          0.002066\n",
      "GO_Biological_Process_2021                           regulation of insulin secretion (GO:0050796)   9/104          0.002066\n",
      "             Reactome_2022                           Regulation Of Insulin Secretion R-HSA-422356    7/77          0.003584\n",
      "             Reactome_2022                Glucagon Signaling In Metabolic Regulation R-HSA-163359    5/32          0.003584\n",
      "           KEGG_2019_Mouse                                                      GABAergic synapse    7/90          0.004117\n",
      "           KEGG_2019_Mouse                                                  Circadian entrainment    7/99          0.005051\n",
      "             Reactome_2022 Anti-inflammatory Response Favoring Leishmania Infection R-HSA-9662851   9/165          0.005561\n",
      "\n",
      "  [down] Significant terms: 389\n",
      "                  Gene_set                                                                                  Term Overlap  Adjusted P-value\n",
      "GO_Biological_Process_2021                                                  cytoplasmic translation (GO:0002181)   31/93      7.807101e-32\n",
      "GO_Biological_Process_2021              SRP-dependent cotranslational protein targeting to membrane (GO:0006614)   30/90      5.017236e-31\n",
      "GO_Biological_Process_2021                            cotranslational protein targeting to membrane (GO:0006613)   30/94      1.532275e-30\n",
      "GO_Biological_Process_2021      nuclear-transcribed mRNA catabolic process, nonsense-mediated decay (GO:0000184)  31/113      1.975454e-29\n",
      "GO_Biological_Process_2021                                                  protein targeting to ER (GO:0045047)  30/103      2.140043e-29\n",
      "             Reactome_2022                                                 Peptide Chain Elongation R-HSA-156902   28/86      1.334897e-28\n",
      "             Reactome_2022                                        Eukaryotic Translation Elongation R-HSA-156842   28/90      1.944818e-28\n",
      "             Reactome_2022                                        Eukaryotic Translation Termination R-HSA-72764   28/90      1.944818e-28\n",
      "             Reactome_2022 Nonsense Mediated Decay (NMD) Independent Of Exon Junction Complex (EJC) R-HSA-975956   28/92      2.958477e-28\n",
      "             Reactome_2022                                                Selenocysteine Synthesis R-HSA-2408557   27/90      3.518813e-27\n",
      "\n",
      "============================================================\n",
      "  Pathway enrichment: Epsilon\n",
      "  Gene sets: ['KEGG_2019_Mouse', 'GO_Biological_Process_2021', 'Reactome_2022']\n",
      "  Up-regulated genes  : 422\n",
      "  Down-regulated genes: 361\n",
      "============================================================\n",
      "\n",
      "  [up] Significant terms: 94\n",
      "       Gene_set                                        Term Overlap  Adjusted P-value\n",
      "  Reactome_2022    Peptide Hormone Metabolism R-HSA-2980736   14/89          0.000005\n",
      "KEGG_2019_Mouse                                    Lysosome  14/124          0.000044\n",
      "KEGG_2019_Mouse Protein processing in endoplasmic reticulum  16/163          0.000044\n",
      "KEGG_2019_Mouse                        Dopaminergic synapse  13/135          0.000450\n",
      "KEGG_2019_Mouse                           GABAergic synapse   10/90          0.001159\n",
      "KEGG_2019_Mouse                                   Phagosome  14/180          0.001186\n",
      "KEGG_2019_Mouse                        Long-term depression    8/61          0.001186\n",
      "KEGG_2019_Mouse                      Synaptic vesicle cycle    9/77          0.001186\n",
      "KEGG_2019_Mouse                       Circadian entrainment   10/99          0.001186\n",
      "KEGG_2019_Mouse                      cAMP signaling pathway  15/211          0.001186\n",
      "\n",
      "  [down] Significant terms: 433\n",
      "                  Gene_set                                                                             Term Overlap  Adjusted P-value\n",
      "GO_Biological_Process_2021                       cotranslational protein targeting to membrane (GO:0006613)   31/94      6.997589e-28\n",
      "GO_Biological_Process_2021                                             cytoplasmic translation (GO:0002181)   31/93      6.997589e-28\n",
      "GO_Biological_Process_2021         SRP-dependent cotranslational protein targeting to membrane (GO:0006614)   30/90      2.976495e-27\n",
      "GO_Biological_Process_2021                         cellular macromolecule biosynthetic process (GO:0034645)  47/314      1.126661e-26\n",
      "GO_Biological_Process_2021                                             protein targeting to ER (GO:0045047)  30/103      1.496237e-25\n",
      "GO_Biological_Process_2021 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay (GO:0000184)  31/113      1.496237e-25\n",
      "             Reactome_2022                                            Peptide Chain Elongation R-HSA-156902   28/86      6.336531e-25\n",
      "             Reactome_2022                                   Eukaryotic Translation Termination R-HSA-72764   28/90      6.799818e-25\n",
      "             Reactome_2022                                   Eukaryotic Translation Elongation R-HSA-156842   28/90      6.799818e-25\n",
      "             Reactome_2022                                                  Cell Cycle, Mitotic R-HSA-69278  56/523      6.799818e-25\n"
     ]
    },
    {
     "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>condition</th>\n",
       "      <th>fate</th>\n",
       "      <th>direction</th>\n",
       "      <th>Gene_set</th>\n",
       "      <th>Term</th>\n",
       "      <th>Overlap</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Adjusted P-value</th>\n",
       "      <th>Old P-value</th>\n",
       "      <th>Old Adjusted P-value</th>\n",
       "      <th>Odds Ratio</th>\n",
       "      <th>Combined Score</th>\n",
       "      <th>Genes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>up</td>\n",
       "      <td>KEGG_2019_Mouse</td>\n",
       "      <td>Thyroid hormone synthesis</td>\n",
       "      <td>4/73</td>\n",
       "      <td>2.094671e-04</td>\n",
       "      <td>2.639285e-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.141876</td>\n",
       "      <td>128.265986</td>\n",
       "      <td>TTR;GPX3;GNAS;PDIA4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>down</td>\n",
       "      <td>GO_Biological_Process_2021</td>\n",
       "      <td>SRP-dependent cotranslational protein targetin...</td>\n",
       "      <td>25/90</td>\n",
       "      <td>5.142006e-39</td>\n",
       "      <td>4.833486e-36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>117.426036</td>\n",
       "      <td>10352.675635</td>\n",
       "      <td>RPL3;RPL32;RPLP1;RPL12;RPLP0;RPL10A;RPL7;RPS4X...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>down</td>\n",
       "      <td>GO_Biological_Process_2021</td>\n",
       "      <td>cytoplasmic translation (GO:0002181)</td>\n",
       "      <td>25/93</td>\n",
       "      <td>1.319036e-38</td>\n",
       "      <td>5.612907e-36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112.228507</td>\n",
       "      <td>9788.719925</td>\n",
       "      <td>RPL3;RPL32;RPLP1;RPL12;RPLP0;RPL10A;RPL7;RPS4X...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>down</td>\n",
       "      <td>GO_Biological_Process_2021</td>\n",
       "      <td>cotranslational protein targeting to membrane ...</td>\n",
       "      <td>25/94</td>\n",
       "      <td>1.791353e-38</td>\n",
       "      <td>5.612907e-36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110.596433</td>\n",
       "      <td>9612.517927</td>\n",
       "      <td>RPL3;RPL32;RPLP1;RPL12;RPLP0;RPL10A;RPL7;RPS4X...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>high_velocity</td>\n",
       "      <td>Beta</td>\n",
       "      <td>up</td>\n",
       "      <td>KEGG_2019_Mouse</td>\n",
       "      <td>Protein processing in endoplasmic reticulum</td>\n",
       "      <td>27/163</td>\n",
       "      <td>3.186142e-13</td>\n",
       "      <td>7.997215e-11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.796547</td>\n",
       "      <td>195.569247</td>\n",
       "      <td>ERO1LB;RPN2;TUSC3;HSPA4L;RPN1;SEL1L;RRBP1;RNF5...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Epsilon</td>\n",
       "      <td>up</td>\n",
       "      <td>KEGG_2019_Mouse</td>\n",
       "      <td>Lysosome</td>\n",
       "      <td>14/124</td>\n",
       "      <td>3.691181e-07</td>\n",
       "      <td>4.410919e-05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.072906</td>\n",
       "      <td>89.952783</td>\n",
       "      <td>CD164;CD63;ATP6V0B;ATP6AP1;CTSZ;HEXA;CLTB;CTSO...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Epsilon</td>\n",
       "      <td>up</td>\n",
       "      <td>KEGG_2019_Mouse</td>\n",
       "      <td>Protein processing in endoplasmic reticulum</td>\n",
       "      <td>16/163</td>\n",
       "      <td>3.802516e-07</td>\n",
       "      <td>4.410919e-05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.209209</td>\n",
       "      <td>77.004778</td>\n",
       "      <td>HSPA8;RPN2;HSPA5;TUSC3;SSR2;HSPA4L;RPN1;EDEM2;...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Epsilon</td>\n",
       "      <td>down</td>\n",
       "      <td>GO_Biological_Process_2021</td>\n",
       "      <td>cotranslational protein targeting to membrane ...</td>\n",
       "      <td>31/94</td>\n",
       "      <td>5.836188e-31</td>\n",
       "      <td>6.997589e-28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.189803</td>\n",
       "      <td>2032.079063</td>\n",
       "      <td>RPL3;ARL6IP1;RPL32;RPL10;RPLP1;RPL12;RPLP0;RPL...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Epsilon</td>\n",
       "      <td>down</td>\n",
       "      <td>GO_Biological_Process_2021</td>\n",
       "      <td>cytoplasmic translation (GO:0002181)</td>\n",
       "      <td>31/93</td>\n",
       "      <td>3.975154e-31</td>\n",
       "      <td>6.997589e-28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.662121</td>\n",
       "      <td>2076.350692</td>\n",
       "      <td>RPL3;RPL32;RPL10;RPLP1;RPL12;RPLP0;RPL11;RPL10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>med_velocity</td>\n",
       "      <td>Epsilon</td>\n",
       "      <td>down</td>\n",
       "      <td>GO_Biological_Process_2021</td>\n",
       "      <td>SRP-dependent cotranslational protein targetin...</td>\n",
       "      <td>30/90</td>\n",
       "      <td>3.723722e-30</td>\n",
       "      <td>2.976495e-27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.575529</td>\n",
       "      <td>2004.121493</td>\n",
       "      <td>RPL3;RPL32;RPL10;RPLP1;RPL12;RPLP0;RPL11;RPL10...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        condition     fate direction                    Gene_set                                               Term Overlap       P-value  Adjusted P-value  \\\n",
       "0   high_velocity    Alpha        up             KEGG_2019_Mouse                          Thyroid hormone synthesis    4/73  2.094671e-04      2.639285e-02   \n",
       "1   high_velocity    Alpha      down  GO_Biological_Process_2021  SRP-dependent cotranslational protein targetin...   25/90  5.142006e-39      4.833486e-36   \n",
       "2   high_velocity    Alpha      down  GO_Biological_Process_2021               cytoplasmic translation (GO:0002181)   25/93  1.319036e-38      5.612907e-36   \n",
       "3   high_velocity    Alpha      down  GO_Biological_Process_2021  cotranslational protein targeting to membrane ...   25/94  1.791353e-38      5.612907e-36   \n",
       "4   high_velocity     Beta        up             KEGG_2019_Mouse        Protein processing in endoplasmic reticulum  27/163  3.186142e-13      7.997215e-11   \n",
       "..            ...      ...       ...                         ...                                                ...     ...           ...               ...   \n",
       "59   med_velocity  Epsilon        up             KEGG_2019_Mouse                                           Lysosome  14/124  3.691181e-07      4.410919e-05   \n",
       "60   med_velocity  Epsilon        up             KEGG_2019_Mouse        Protein processing in endoplasmic reticulum  16/163  3.802516e-07      4.410919e-05   \n",
       "61   med_velocity  Epsilon      down  GO_Biological_Process_2021  cotranslational protein targeting to membrane ...   31/94  5.836188e-31      6.997589e-28   \n",
       "62   med_velocity  Epsilon      down  GO_Biological_Process_2021               cytoplasmic translation (GO:0002181)   31/93  3.975154e-31      6.997589e-28   \n",
       "63   med_velocity  Epsilon      down  GO_Biological_Process_2021  SRP-dependent cotranslational protein targetin...   30/90  3.723722e-30      2.976495e-27   \n",
       "\n",
       "    Old P-value  Old Adjusted P-value  Odds Ratio  Combined Score                                              Genes  \n",
       "0             0                     0   15.141876      128.265986                                TTR;GPX3;GNAS;PDIA4  \n",
       "1             0                     0  117.426036    10352.675635  RPL3;RPL32;RPLP1;RPL12;RPLP0;RPL10A;RPL7;RPS4X...  \n",
       "2             0                     0  112.228507     9788.719925  RPL3;RPL32;RPLP1;RPL12;RPLP0;RPL10A;RPL7;RPS4X...  \n",
       "3             0                     0  110.596433     9612.517927  RPL3;RPL32;RPLP1;RPL12;RPLP0;RPL10A;RPL7;RPS4X...  \n",
       "4             0                     0    6.796547      195.569247  ERO1LB;RPN2;TUSC3;HSPA4L;RPN1;SEL1L;RRBP1;RNF5...  \n",
       "..          ...                   ...         ...             ...                                                ...  \n",
       "59            0                     0    6.072906       89.952783  CD164;CD63;ATP6V0B;ATP6AP1;CTSZ;HEXA;CLTB;CTSO...  \n",
       "60            0                     0    5.209209       77.004778  HSPA8;RPN2;HSPA5;TUSC3;SSR2;HSPA4L;RPN1;EDEM2;...  \n",
       "61            0                     0   29.189803     2032.079063  RPL3;ARL6IP1;RPL32;RPL10;RPLP1;RPL12;RPLP0;RPL...  \n",
       "62            0                     0   29.662121     2076.350692  RPL3;RPL32;RPL10;RPLP1;RPL12;RPLP0;RPL11;RPL10...  \n",
       "63            0                     0   29.575529     2004.121493  RPL3;RPL32;RPL10;RPLP1;RPL12;RPLP0;RPL11;RPL10...  \n",
       "\n",
       "[64 rows x 13 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "enrichment_per_cond = {}\n",
    "try:\n",
    "    for cond in mscorer.conditions:\n",
    "        enrichment_per_cond[cond] = run_enrichment_per_fate(\n",
    "            deg_drivers_per_cond[cond],\n",
    "            fate_names=fate_names,\n",
    "            organism=\"mouse\",\n",
    "            pval_threshold=0.05,\n",
    "            logfc_threshold=0.25,\n",
    "            plot=False,\n",
    "            n_top_pathways=10,\n",
    "        )\n",
    "    display(_stack_enrichment_by_condition(enrichment_per_cond, n_per_group=3))\n",
    "except Exception as e:\n",
    "    print(f\"Enrichment skipped (offline?): {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "022c5a32",
   "metadata": {},
   "source": [
    "## 12. Expression trends along fate arms — per condition\n",
    "\n",
    "`plot_expression_trends` shows how marker genes evolve along each fate arm. Running it once per condition on the masked `adata_sub` puts the per-condition curves side by side, so you can spot timing shifts, magnitude changes, or qualitatively different trajectories.\n",
    "\n",
    "We use `x_axis=\"affinity\"` here — distance along each fate arm in the shared embedding. `\"pseudotime\"` and `\"radial_distance\"` are also supported.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f7f4a12e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- high_velocity (n=633 cells) ---\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- low_velocity (n=957 cells) ---\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- med_velocity (n=610 cells) ---\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dataclasses import replace\n",
    "\n",
    "genes = [\"Ins1\", \"Gcg\"]  # Beta-cell + Alpha-cell markers\n",
    "genes = [g for g in genes if g in adata_sub_shared.var_names]\n",
    "\n",
    "# Use cell_obs_names from each per-condition result to mask the shared\n",
    "# CommitmentScoreResult to that condition (plot_expression_trends expects\n",
    "# a result whose cell_obs_names index into the passed AnnData).\n",
    "cell_names_full = np.array(adata_sub_shared.obs_names)\n",
    "for cond in mscorer.conditions:\n",
    "    mask = (adata_sub_shared.obs[mscorer.condition_obs_key].astype(str) == cond).values\n",
    "    res_full = results[cond]\n",
    "    if res_full.cell_scores is None:\n",
    "        print(f\"\\n--- {cond}: no cell-level scores; rerun score_all_conditions(cell_level=True). Skipping. ---\")\n",
    "        continue\n",
    "    # Some results store full-shape arrays (one per shared-embedding cell)\n",
    "    # while others store only this-condition cells; handle both.\n",
    "    if res_full.cell_scores.shape[0] == adata_sub_shared.n_obs:\n",
    "        cs = res_full.cell_scores[mask]\n",
    "        cnames = cell_names_full[mask]\n",
    "        nne = res_full.nn_cell_entropy[mask] if res_full.nn_cell_entropy is not None else None\n",
    "    else:\n",
    "        cs = res_full.cell_scores\n",
    "        cnames = np.asarray(res_full.cell_obs_names)\n",
    "        nne = res_full.nn_cell_entropy\n",
    "    res_masked = replace(\n",
    "        res_full,\n",
    "        cell_scores=cs,\n",
    "        cell_obs_names=cnames,\n",
    "        nn_cell_entropy=nne,\n",
    "    )\n",
    "    print(f\"\\n--- {cond} (n={cs.shape[0]} cells) ---\")\n",
    "    try:\n",
    "        fig = plot_expression_trends(\n",
    "            adata_sub_shared,\n",
    "            res_masked,\n",
    "            genes=genes,\n",
    "            x_axis=\"affinity\",\n",
    "            figsize=(12, 4),\n",
    "        )\n",
    "        plt.suptitle(f\"Expression trends — {cond}\", y=1.02)\n",
    "        plt.show()\n",
    "    except Exception as e:\n",
    "        print(f\"Expression trends failed for {cond}: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36de0443",
   "metadata": {},
   "source": [
    "## 13. Transfer per-cell labels back to the full AnnData\n",
    "\n",
    "`transfer_labels` writes per-cell, per-condition affinity scores and dominant fate labels back to the FULL adata as new `obs` columns (named `cs_{fate}_{condition}` and `cs_dominant_fate_{condition}`). This lets you use the scCS outputs in downstream Scanpy / scVelo workflows — UMAP coloring, marker discovery, etc.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fdfefcf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[scCS] Labels transferred to adata.obs for 2200 / 3696 cells. Columns: ['cs_high_velocity_Alpha', 'cs_high_velocity_Beta', 'cs_high_velocity_Delta', 'cs_high_velocity_Epsilon', 'cs_high_velocity_dominant_fate', 'cs_high_velocity_entropy']\n",
      "[scCS] Labels transferred to adata.obs for 2200 / 3696 cells. Columns: ['cs_low_velocity_Alpha', 'cs_low_velocity_Beta', 'cs_low_velocity_Delta', 'cs_low_velocity_Epsilon', 'cs_low_velocity_dominant_fate', 'cs_low_velocity_entropy']\n",
      "[scCS] Labels transferred to adata.obs for 2200 / 3696 cells. Columns: ['cs_med_velocity_Alpha', 'cs_med_velocity_Beta', 'cs_med_velocity_Delta', 'cs_med_velocity_Epsilon', 'cs_med_velocity_dominant_fate', 'cs_med_velocity_entropy']\n",
      "New columns written to adata.obs (18):\n",
      "  - cs_high_velocity_Alpha\n",
      "  - cs_high_velocity_Beta\n",
      "  - cs_high_velocity_Delta\n",
      "  - cs_high_velocity_Epsilon\n",
      "  - cs_high_velocity_dominant_fate\n",
      "  - cs_high_velocity_entropy\n",
      "  - cs_low_velocity_Alpha\n",
      "  - cs_low_velocity_Beta\n",
      "  - cs_low_velocity_Delta\n",
      "  - cs_low_velocity_Epsilon\n",
      "  - cs_low_velocity_dominant_fate\n",
      "  - cs_low_velocity_entropy\n",
      "  - cs_med_velocity_Alpha\n",
      "  - cs_med_velocity_Beta\n",
      "  - cs_med_velocity_Delta\n",
      "  ... +3 more\n"
     ]
    }
   ],
   "source": [
    "# MultiScorer.transfer_labels writes cs_{condition}_{...} columns to\n",
    "# mscorer.adata internally — no positional adata argument needed.\n",
    "mscorer.transfer_labels(results, prefix=\"cs_\")\n",
    "\n",
    "new_cols = [c for c in mscorer.adata.obs.columns\n",
    "            if c.startswith(\"cs_\") and any(cond in c for cond in mscorer.conditions)]\n",
    "print(f\"New columns written to adata.obs ({len(new_cols)}):\")\n",
    "for c in new_cols[:15]:\n",
    "    print(f\"  - {c}\")\n",
    "if len(new_cols) > 15:\n",
    "    print(f\"  ... +{len(new_cols) - 15} more\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4bd1872a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No cs_dominant_fate_* columns found in mscorer.adata.obs — make sure transfer_labels() ran successfully above.\n"
     ]
    }
   ],
   "source": [
    "# Quick UMAP showing dominant fate per condition (if UMAP is available).\n",
    "# transfer_labels wrote columns to mscorer.adata, so plot from there.\n",
    "full = mscorer.adata\n",
    "if \"X_umap\" not in full.obsm:\n",
    "    sc.pp.neighbors(full, n_neighbors=15) if \"distances\" not in full.obsp else None\n",
    "    sc.tl.umap(full)\n",
    "\n",
    "dom_cols = [c for c in full.obs.columns if c.startswith(\"cs_dominant_fate_\")]\n",
    "if dom_cols:\n",
    "    sc.pl.umap(full, color=dom_cols, ncols=len(mscorer.conditions),\n",
    "               wspace=0.4, show=True)\n",
    "else:\n",
    "    print(\"No cs_dominant_fate_* columns found in mscorer.adata.obs — \"\n",
    "          \"make sure transfer_labels() ran successfully above.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7b466bc",
   "metadata": {},
   "source": [
    "## 14. Summary — scCS 3+ condition workflow\n",
    "\n",
    "```python\n",
    "import scCS\n",
    "\n",
    "# 1. Add condition column (3+ levels) to adata.obs\n",
    "adata.obs[\"condition\"] = ...     # e.g., 'control', 'drug_A', 'drug_B'\n",
    "adata.obs[\"sample\"]    = ...     # biological replicates\n",
    "\n",
    "# 2. Initialize MultiScorer\n",
    "mscorer = scCS.MultiScorer(\n",
    "    adata,\n",
    "    root=\"Ductal\",\n",
    "    branches=[\"Alpha\", \"Beta\", \"Delta\", \"Epsilon\"],\n",
    "    condition_obs_key=\"condition\",\n",
    "    obs_key=\"clusters\",\n",
    ")\n",
    "\n",
    "# 3. Build SHARED embedding on pooled data, then fit\n",
    "mscorer.build_embedding(ordering_metric=\"velocity_pseudotime\")\n",
    "mscorer.fit()\n",
    "# (optional) mscorer.refit_pseudotime() — only call when the subset has\n",
    "# a connected velocity_graph; otherwise stick with velocity_pseudotime.\n",
    "\n",
    "# ── Tier 1 — score each condition ────────────────────────────────────────────\n",
    "results = mscorer.score_all_conditions(cell_level=True, n_bootstrap=200)\n",
    "\n",
    "# ── Tier 2 — omnibus + post-hoc ──────────────────────────────────────────────\n",
    "omnibus_df = mscorer.compare_omnibus(results, test=\"kruskal\")\n",
    "posthoc_df = mscorer.compare_posthoc(\n",
    "    results, omnibus_results=omnibus_df, method=\"dunn\",\n",
    "    pval_correction=\"bonferroni\", pval_threshold=0.05,\n",
    ")\n",
    "deltas     = mscorer.compute_pairwise_deltas(n_bootstrap=100)\n",
    "\n",
    "# ── Tier 3 — mixed model with reference contrasts ────────────────────────────\n",
    "contrasts_df = mscorer.fit_mixed_model_contrasts(\n",
    "    results, ref_condition=\"control\", replicate_key=\"sample\"\n",
    ")\n",
    "\n",
    "# ── Visualizations ───────────────────────────────────────────────────────────\n",
    "mscorer.plot_omnibus_summary(omnibus_df, results, posthoc_df=posthoc_df)\n",
    "mscorer.plot_posthoc_heatmap(posthoc_df)\n",
    "mscorer.plot_pairwise_delta_grid(deltas)\n",
    "mscorer.plot_star_grid(results)\n",
    "mscorer.plot_rose_grid(results)\n",
    "\n",
    "# ── Downstream §10 — driver genes per condition ──────────────────────────────\n",
    "# Loop conditions: mask adata_sub per condition -> get_velocity_drivers /\n",
    "# get_deg_drivers -> _stack_drivers_by_condition -> one tidy table.\n",
    "\n",
    "# ── Downstream §11 — pathway enrichment per condition ────────────────────────\n",
    "# Loop conditions: run_enrichment_per_fate on each cond's DEG drivers ->\n",
    "# _stack_enrichment_by_condition -> one tidy table.\n",
    "\n",
    "# ── Downstream §12 — expression trends per condition ─────────────────────────\n",
    "# Loop conditions: build a per-condition CommitmentScoreResult via\n",
    "# dataclasses.replace, masking cell_scores/cell_obs_names/nn_cell_entropy\n",
    "# to that condition's cells, then plot_expression_trends.\n",
    "\n",
    "# ── Downstream §13 — transfer labels back to mscorer.adata ───────────────────\n",
    "mscorer.transfer_labels(results, prefix=\"cs_\")\n",
    "# Writes cs_{condition}_{fate} + cs_{condition}_dominant_fate + cs_{condition}_entropy\n",
    "```\n",
    "\n",
    "### Interpretation guide\n",
    "\n",
    "| Result | Interpretation |\n",
    "|---|---|\n",
    "| Omnibus q < 0.05 for a fate | Affinity for that fate differs across at least one condition pair |\n",
    "| Post-hoc q < 0.05 for (a, b) | That specific condition pair drives the difference |\n",
    "| ΔnCS CI excludes 0 | Pairwise commitment shift is significant |\n",
    "| Mixed-model contrast coef significant | Condition effect on affinity, controlling for replicate |\n",
    "| Top driver gene in only 1 condition | Condition-specific regulator candidate |\n",
    "| Pathway enriched in only 1 condition | Differentially activated pathway |\n",
    "| Expression curve shifted in 1 condition | Altered developmental timing for that gene |\n",
    "\n",
    "For **2-condition** comparisons see `Pairwise Comparison Analysis.ipynb`; for **single-condition** analysis see `Single Condition Analysis.ipynb`.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.12 lab-py312",
   "language": "python",
   "name": "lab-py312"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.13"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "05d7cfbb6c2b499cb8aa159aa33f7dab": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "2120fc2f54af44188009fad1739d989b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_24c25dfaa0fc4fc0a3b4be180369fd5c",
       "placeholder": "​",
       "style": "IPY_MODEL_b4e984c4107d4aebb7ac8f5fd00c8d17",
       "tabbable": null,
       "tooltip": null,
       "value": " 50.0M/50.0M [00:00&lt;00:00, 79.0MB/s]"
      }
     },
     "24c25dfaa0fc4fc0a3b4be180369fd5c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "27d2332642ea4ff7bcc3df2f198badcb": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "3b14f16868b544a99d77052aa185c8dd": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_76d2957fe7f34b2da36a12ddef45baa9",
       "max": 3696,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_71d224e6403544a3bb239c44ebd4e61e",
       "tabbable": null,
       "tooltip": null,
       "value": 3696
      }
     },
     "3cb5334000a4457eb81c823f1ae0752d": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "46bb5bb5a60f4e00960431a75fac1e8e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_685eab2cc3eb47279e5a841a9aac4281",
        "IPY_MODEL_3b14f16868b544a99d77052aa185c8dd",
        "IPY_MODEL_c86622a97f324ad2b2f4c7521703a586"
       ],
       "layout": "IPY_MODEL_493cd98b5e9c4818a4aa0ab5a6e6c5e0",
       "tabbable": null,
       "tooltip": null
      }
     },
     "46fe881f804b4a42b9ac8818cffd3fa1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "493cd98b5e9c4818a4aa0ab5a6e6c5e0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "6404b988d162468084f9443cb2aa47c3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_ccb086090cfc4716b4d10c854412774f",
        "IPY_MODEL_eb50e6dc2ba14f3c82c6be75ac9d15c0",
        "IPY_MODEL_2120fc2f54af44188009fad1739d989b"
       ],
       "layout": "IPY_MODEL_896237690c9044a2b0a122c6b7974ffa",
       "tabbable": null,
       "tooltip": null
      }
     },
     "64ecff54ae2e4889b162f9730bb62182": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "685eab2cc3eb47279e5a841a9aac4281": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_64ecff54ae2e4889b162f9730bb62182",
       "placeholder": "​",
       "style": "IPY_MODEL_05d7cfbb6c2b499cb8aa159aa33f7dab",
       "tabbable": null,
       "tooltip": null,
       "value": "100%"
      }
     },
     "71d224e6403544a3bb239c44ebd4e61e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "76d2957fe7f34b2da36a12ddef45baa9": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "896237690c9044a2b0a122c6b7974ffa": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "b04a0a478aed4528a4e169f241f47118": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "b3cfd704350a4c0886312c89b1080064": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "b4e984c4107d4aebb7ac8f5fd00c8d17": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "b5b6f766b0d746a4ac59e4d216b98902": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "c86622a97f324ad2b2f4c7521703a586": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_b3cfd704350a4c0886312c89b1080064",
       "placeholder": "​",
       "style": "IPY_MODEL_27d2332642ea4ff7bcc3df2f198badcb",
       "tabbable": null,
       "tooltip": null,
       "value": " 3696/3696 [00:03&lt;00:00, 1335.55cells/s]"
      }
     },
     "ccb086090cfc4716b4d10c854412774f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_3cb5334000a4457eb81c823f1ae0752d",
       "placeholder": "​",
       "style": "IPY_MODEL_46fe881f804b4a42b9ac8818cffd3fa1",
       "tabbable": null,
       "tooltip": null,
       "value": "100%"
      }
     },
     "eb50e6dc2ba14f3c82c6be75ac9d15c0": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_b5b6f766b0d746a4ac59e4d216b98902",
       "max": 52477898,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_b04a0a478aed4528a4e169f241f47118",
       "tabbable": null,
       "tooltip": null,
       "value": 52477898
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}