{ "cells": [ { "cell_type": "markdown", "id": "b5534a21", "metadata": {}, "source": [ "# Extended Perturbation Analysis — scHopfield Hematopoiesis\n", "\n", "Extends the lineage driver analysis from `notebooks/06_lineage_drivers.ipynb` with five additional experiments:\n", "\n", "| Section | Analysis | Priority | Est. Runtime |\n", "|---------|----------|----------|--------------|\n", "| A | Stat3 OE flow (bidirectionality check) | Medium | ~5 min |\n", "| B | Time-resolved ΔX heatmaps (10 ODE runs) | Medium | ~30 min |\n", "| C | ODE phase portraits (Gata1 vs Stat3 plane) | Medium | ~5 min |\n", "| D | Dose-response curves (20 ODE runs) | High | ~60 min |\n", "| E | Bootstrap stability (100 × score_driver_tfs) | High | ~15 min |\n", "\n", "**Out of scope here:**\n", "- Perturb-seq correlation (requires external dataset download)\n", "- Regularization robustness (requires model retraining ~3×30–60 min)\n", "\n", "Loads from checkpoints at `/Users/bernaljp/Documents/SCHData/checkpoints/06_lineage_drivers/` — no retraining needed." ] }, { "cell_type": "markdown", "id": "c9d9e13a", "metadata": {}, "source": [ "\n", "## Package vs Notebook: Methods Classification\n", "\n", "Which analytical methods in this notebook are **generic enough to move into the `scHopfield` package** vs **study-specific and should remain here**.\n", "\n", "---\n", "\n", "### MOVE TO PACKAGE (`sch`)\n", "\n", "| Cell / Section | Method | Suggested API |\n", "|----------------|--------|---------------|\n", "| §A — Stat3 KO flow (cells 34-35) | Per-cell inner product between KO perturbation flow and WT Hopfield velocity; grid interpolation for embedding arrows | `sch.tl.compute_perturbation_alignment(adata, ko_adata, wt_flow_key)` |\n", "| §D — Dose-response (cells 16-18) | Multi-level ODE sweep (0 to 2× natural expression), lineage bias at each level | `sch.dyn.run_dose_response(adata, genes, levels, simulate_kwargs)` |\n", "| §G — Tier classification (cell 23) | Mann-Whitney U differential expression between two arbitrary cluster groups; returns ranked gene table | `sch.tl.lineage_de(adata, lineage_A_clusters, lineage_B_clusters)` |\n", "| §H — W^c weight extraction (cell 26) | Per-gene combined regulatory weight `w_combined = W^c[anchor,:] + W^c[:,anchor]` across a set of cluster matrices | `sch.tl.grn_partner_weights(adata, anchor_gene, cluster_keys)` |\n", "\n", "---\n", "\n", "### KEEP IN NOTEBOOK (study-specific)\n", "\n", "| Cell / Section | Method | Why notebook-only |\n", "|----------------|--------|-------------------|\n", "| §H — 4+4+4+4 selection (cell 26) | Multi-criterion sequential partner selection: hit-count + ery-top + mye-top + all-cluster, 4 genes each | Selection thresholds (K=10, N=4, MIN_HITS) and the recipe framing are paper choices |\n", "| §H — Recipe sweep + plot (cells 27-28) | Anchored double-KO sweep, direction-corrected synergy score, per-criterion border-color plot | Anchor choices (Gata1/Stat3), plot design, and interpretation are paper-specific |\n", "| §I — STAT family circuit (cell 30) | W^c sub-matrix for STAT TFs; concordance table vs literature | Biological question (STAT family in hematopoiesis) is not generalisable |\n", "| §B — Cascade timing (cells 37-38) | Log-scale time series of cluster-mean |ΔX| for Gata1/Stat3 KO | Specific time range, gene choices, cluster palette are study-specific |\n", "| §C/§D — TF ranking + cluster heatmap (cells 40-42) | Lineage-bias bar chart and cluster |ΔX| heatmap | Specific to 16-candidate set and Paul 2015 cluster structure |\n", "| Bootstrap stability (cells 19-21) | Gene-level bootstrap of lineage bias across resamplings | Specific to this candidate validation; generalised bootstrap wrapper could go to package later |\n" ] }, { "cell_type": "code", "execution_count": null, "id": "28e18a6b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "scHopfield: 0.1.0\n", "torch: 2.7.0\n" ] } ], "source": [ "import warnings; warnings.filterwarnings('ignore')\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", "import json\n", "from pathlib import Path\n", "from tqdm.auto import tqdm\n", "\n", "import scanpy as sc\n", "import scvelo as scv\n", "import celloracle as co\n", "import torch\n", "\n", "import scHopfield as sch\n", "from scHopfield.dynamics.simulation import simulate_shift_ode, simulate_perturbation_ode\n", "from scHopfield._utils.io import get_genes_used, get_matrix, to_numpy\n", "\n", "sc.settings.verbosity = 1\n", "scv.settings.verbosity = 1\n", "\n", "print(f\"scHopfield: {sch.__version__}\")\n", "print(f\"torch: {torch.__version__}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "4596d6c7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Device : mps\n", "SAVE_DIR exists : True\n", "MODEL_PATH exists: True\n", "EXT_SAVE_DIR : /Users/bernaljp/Documents/SCHData/checkpoints/perturbation_extended\n" ] } ], "source": [ "# ── Constants (mirror nb06 exactly) ──────────────────────────────────────────\n", "CLUSTER_KEY = 'paul15_clusters'\n", "SPLICED_KEY = 'Ms'\n", "BASIS = 'draw_graph_fa'\n", "VELOCITY_KEY = 'velocity_S'\n", "VELOCITY_SCALE = 500.0\n", "\n", "DEVICE = ('cuda' if torch.cuda.is_available()\n", " else 'mps' if torch.backends.mps.is_available()\n", " else 'cpu')\n", "\n", "ERYTHROID = ['1Ery','2Ery','3Ery','4Ery','5Ery','6Ery','7MEP','8Mk']\n", "MYELOID = ['9GMP','10GMP','11DC','12Baso','13Baso','14Mo','15Mo','16Neu','17Neu','18Eos','19Lymph']\n", "CLUSTER_ORDER = ['1Ery','2Ery','3Ery','4Ery','5Ery','6Ery','7MEP','8Mk',\n", " '9GMP','10GMP','11DC','12Baso','13Baso','14Mo','15Mo',\n", " '16Neu','17Neu','18Eos','19Lymph']\n", "\n", "# ── Checkpoint paths ──────────────────────────────────────────────────────────\n", "SAVE_DIR = Path('/Users/bernaljp/Documents/SCHData/checkpoints/06_lineage_drivers')\n", "MODEL_PATH = str(SAVE_DIR / 'model.h5sch')\n", "EXT_SAVE_DIR = Path('/Users/bernaljp/Documents/SCHData/checkpoints/perturbation_extended')\n", "EXT_SAVE_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", "_WT_VEL_FLOW_KEY = f'original_velocity_flow_{BASIS}'\n", "\n", "print(f\"Device : {DEVICE}\")\n", "print(f\"SAVE_DIR exists : {SAVE_DIR.exists()}\")\n", "print(f\"MODEL_PATH exists: {Path(MODEL_PATH).exists()}\")\n", "print(f\"EXT_SAVE_DIR : {EXT_SAVE_DIR}\")" ] }, { "cell_type": "code", "execution_count": 27, "id": "ca0858da", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7bfb390cf16946919b1f5ed5cd799144", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/2671 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved → /Users/bernaljp/Documents/SCHData/checkpoints/perturbation_extended/stat3_oe_flow.png\n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "# ── Plot: Stat3 OE flow ───────────────────────────────────────────────────────\n", "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n", "\n", "# # Left: flow map (Stat3 KO)\n", "# sch.pl.velocity_embedding_stream(\n", "# adata_stat3_ko,\n", "# vkey=f'perturbation_flow_{BASIS}',\n", "# basis=BASIS,\n", "# color=CLUSTER_KEY,\n", "# legend_loc='none',\n", "# title=\"Stat3 KO flow (CellOracle method)\",\n", "# ax=axes[0],\n", "# )\n", "\n", "sch.tl.calculate_flow(\n", " adata_stat3_ko,\n", " source='delta',\n", " basis=BASIS,\n", " method='celloracle',\n", " cluster_key=CLUSTER_KEY,\n", " store_key=f'perturbation_flow_{BASIS}',\n", " verbose=False,\n", ")\n", "\n", "# Plot using the dynamic target_color\n", "sch.pl.plot_flow(\n", " adata_stat3_ko,\n", " flow_key=f'perturbation_flow_{BASIS}',\n", " basis=BASIS,\n", " on_grid=True,\n", " ax=axes[0],\n", " n_grid=25,\n", " min_mass=10,\n", " scale=5,\n", " color='tab:red',\n", " cluster_key=CLUSTER_KEY,\n", " colors=colors,\n", " title=f'{gene}\\nHopfield Δv (grid)',\n", ")\n", "\n", "# Right: bar chart comparing biases\n", "genes_compare = ['Stat3 KO', 'Stat3 OE']\n", "biases_compare = [single_ko_bias.loc['Stat3', 'lineage_bias'], bias_stat3_oe['lineage_bias']]\n", "colors_compare = ['#E74C3C' if b > 0 else '#3498DB' for b in biases_compare]\n", "\n", "bars = axes[1].bar(genes_compare, biases_compare, color=colors_compare, edgecolor='k', width=0.5)\n", "axes[1].axhline(0, color='k', lw=1, ls='--')\n", "axes[1].set_ylabel('Lineage bias\\n(+ erythroid, − myeloid)', fontsize=11)\n", "axes[1].set_title('Stat3: KO vs OE bidirectionality', fontsize=12)\n", "for bar, val in zip(bars, biases_compare):\n", " axes[1].text(bar.get_x() + bar.get_width()/2, val + 0.003 * np.sign(val),\n", " f'{val:.3f}', ha='center', va='bottom' if val > 0 else 'top', fontsize=11)\n", "axes[1].set_ylim(min(biases_compare)*1.3, max(biases_compare)*1.3)\n", "\n", "plt.tight_layout()\n", "plt.savefig(EXT_SAVE_DIR / 'stat3_oe_flow.png', dpi=300, bbox_inches='tight')\n", "plt.show()\n", "print(f\"Saved → {EXT_SAVE_DIR / 'stat3_oe_flow.png'}\")\n" ] }, { "cell_type": "code", "execution_count": 32, "id": "748b0bbe", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded from checkpoint: 379810 rows\n", " perturbation n_steps cluster gene mean_abs_delta\n", "0 Gata1_KO 20 1Ery 0610007L01Rik 0.820535\n", "1 Gata1_KO 20 1Ery 0610010K14Rik 1.084797\n", "2 Gata1_KO 20 1Ery 0910001L09Rik 1.038569\n", "3 Gata1_KO 20 1Ery 1100001G20Rik 0.002025\n", "4 Gata1_KO 20 1Ery 1110004E09Rik 0.293719\n" ] } ], "source": [ "# ── Run time-resolved simulations (checkpoint protected, ~30 min) ─────────────\n", "timeseries_csv = EXT_SAVE_DIR / 'timeseries_cluster_effects.csv'\n", "time_points = [20, 40, 60, 80, 100]\n", "\n", "if timeseries_csv.exists():\n", " ts_df = pd.read_csv(timeseries_csv)\n", " print(f\"Loaded from checkpoint: {len(ts_df)} rows\")\n", "else:\n", " # Store per-cell delta_X for each (gene, time_point)\n", " records = []\n", " for n_steps in tqdm(time_points, desc='Time points'):\n", " for gene, label in [('Gata1', 'Gata1_KO'), ('Stat3', 'Stat3_KO')]:\n", " adata_t = simulate_shift_ode(\n", " adata.copy(), {gene: 0.0},\n", " cluster_key=CLUSTER_KEY, dt=5.0, n_steps=n_steps,\n", " use_cluster_specific_GRN=True, n_jobs=-1, device=DEVICE,\n", " )\n", " # Compute mean |delta_X| per cluster per gene\n", " delta_X = adata_t.layers['delta_X'] # (n_cells, n_genes)\n", " genes_used = get_genes_used(adata_t)\n", " gene_names_used = list(adata_t.var.index[genes_used])\n", "\n", " for cluster in CLUSTER_ORDER:\n", " mask = adata_t.obs[CLUSTER_KEY] == cluster\n", " if mask.sum() == 0:\n", " continue\n", " cluster_delta = np.abs(delta_X[mask][:, genes_used]).mean(axis=0)\n", " for gi, gname in enumerate(gene_names_used):\n", " records.append({\n", " 'perturbation': label,\n", " 'n_steps': n_steps,\n", " 'cluster': cluster,\n", " 'gene': gname,\n", " 'mean_abs_delta': float(cluster_delta[gi]),\n", " })\n", "\n", " ts_df = pd.DataFrame(records)\n", " ts_df.to_csv(timeseries_csv, index=False)\n", " print(f\"Saved {len(ts_df)} rows → {timeseries_csv}\")\n", "\n", "print(ts_df.head())" ] }, { "cell_type": "code", "execution_count": 56, "id": "33d51282", "metadata": {}, "outputs": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved → /Users/bernaljp/Documents/SCHData/checkpoints/perturbation_extended/timeseries_heatmaps.png\n" ] } ], "source": [ "# ── Time-resolved heatmaps ────────────────────────────────────────────────────\n", "N_TOP_GENES = 20\n", "\n", "fig, axes = plt.subplots(2, 5, figsize=(22, 12))\n", "fig.suptitle('Time-resolved ΔX: mean |ΔX| per cluster', fontsize=13, y=1.01)\n", "\n", "for row_idx, pert in enumerate(['Gata1_KO', 'Stat3_KO']):\n", " sub = ts_df[ts_df['perturbation'] == pert]\n", "\n", " # Select top genes by mean |delta_X| across all clusters at t=100\n", " t100 = sub[sub['n_steps'] == 100].groupby('gene')['mean_abs_delta'].mean()\n", " top_genes = t100.nlargest(N_TOP_GENES).index.tolist()\n", "\n", " for col_idx, n_steps in enumerate(time_points):\n", " ax = axes[row_idx, col_idx]\n", " pivot = (sub[sub['n_steps'] == n_steps]\n", " .pivot(index='gene', columns='cluster', values='mean_abs_delta')\n", " .reindex(index=top_genes, columns=CLUSTER_ORDER)\n", " .fillna(0))\n", "\n", " vmax = pivot.values.max()\n", " im = ax.imshow(pivot.values, aspect='auto', cmap='Reds', vmin=0, vmax=vmax)\n", "\n", " ax.set_title(f't={n_steps}', fontsize=10)\n", " ax.set_xticks(range(len(CLUSTER_ORDER)))\n", " ax.set_xticklabels(CLUSTER_ORDER, rotation=90, fontsize=6)\n", "\n", " if col_idx == 0:\n", " ax.set_yticks(range(N_TOP_GENES))\n", " ax.set_yticklabels(top_genes, fontsize=7)\n", " ax.set_ylabel(pert.replace('_', ' '), fontsize=10, fontweight='bold')\n", " else:\n", " ax.set_yticks([])\n", "\n", " plt.colorbar(im, ax=ax, fraction=0.04, pad=0.02)\n", "\n", "plt.tight_layout()\n", "plt.savefig(EXT_SAVE_DIR / 'timeseries_heatmaps.png', dpi=300, bbox_inches='tight')\n", "plt.show()\n", "print(f\"Saved → {EXT_SAVE_DIR / 'timeseries_heatmaps.png'}\")" ] }, { "cell_type": "code", "execution_count": 78, "id": "23197536", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1Ery: cells [35, 100, 294]\n", "2Ery: cells [7, 9, 10]\n", "3Ery: cells [2, 4, 8]\n" ] } ], "source": [ "# ── Phase portrait setup ──────────────────────────────────────────────────────\n", "from scHopfield.dynamics.simulation import simulate_perturbation_ode\n", "\n", "genes_used = get_genes_used(adata)\n", "gene_names_used = list(adata.var.index[genes_used])\n", "\n", "gata1_idx = gene_names_used.index('Gata1')\n", "stat3_idx = gene_names_used.index('Stat3')\n", "\n", "t_span = np.linspace(0, 500, 100) # 100 time points, t=0..500\n", "\n", "# Pick 3 representative cells per cluster (first 3 of each)\n", "representative_cells = {}\n", "for cluster in ['1Ery', '2Ery', '3Ery']:\n", " mask = adata.obs[CLUSTER_KEY] == cluster\n", " representative_cells[cluster] = list(np.where(mask)[0][:3])\n", " print(f\"{cluster}: cells {representative_cells[cluster]}\")\n", "\n", "conditions = {\n", " 'WT': {},\n", " 'Gata1_KO': {'Gata1': 0.0},\n", " 'Gata1_OE': {'Gata1': gata1_oe_val},\n", " 'Stat3_KO': {'Stat3': 0.0},\n", " 'Stat3_OE': {'Stat3': stat3_oe_val},\n", "}\n", "\n", "cond_colors = {\n", " 'WT': 'gray',\n", " 'Gata1_KO': '#E74C3C',\n", " 'Gata1_OE': '#922B21',\n", " 'Stat3_KO': '#2980B9',\n", " 'Stat3_OE': '#1A5276',\n", "}" ] }, { "cell_type": "code", "execution_count": 79, "id": "91702b85", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "99a71a2daa4d468393624ca805831d07", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Clusters: 0%| | 0/3 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved → /Users/bernaljp/Documents/SCHData/checkpoints/perturbation_extended/phase_portraits.png\n" ] } ], "source": [ "# ── Plot phase portraits ───────────────────────────────────────────────────────\n", "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n", "fig.suptitle('ODE Phase Portraits: Gata1 vs Stat3 expression', fontsize=13)\n", "\n", "for col_idx, cluster in enumerate(['1Ery', '2Ery', '3Ery']):\n", " ax = axes[col_idx]\n", " for cond_name, color in cond_colors.items():\n", " trajs = trajectories[(cluster, cond_name)]\n", " for traj in trajs: # one line per representative cell\n", " traj_arr = np.array(traj) # (n_time, n_genes_used)\n", " gata1_vals = traj_arr[:, gata1_idx]\n", " stat3_vals = traj_arr[:, stat3_idx]\n", " ax.plot(gata1_vals, stat3_vals, color=color, alpha=0.7, lw=1.2,\n", " label=cond_name if col_idx == 0 else '')\n", " # Mark start with a dot\n", " ax.scatter(gata1_vals[0], stat3_vals[0], color=color, s=25, zorder=5, alpha=0.8)\n", "\n", " ax.set_title(cluster, fontsize=11)\n", " ax.set_xlabel('Gata1 expression', fontsize=9)\n", " if col_idx == 0:\n", " ax.set_ylabel('Stat3 expression', fontsize=9)\n", "\n", "# Single legend on first panel\n", "handles = [plt.Line2D([0], [0], color=c, lw=2, label=n) for n, c in cond_colors.items()]\n", "axes[0].legend(handles=handles, fontsize=8, loc='upper right')\n", "\n", "plt.tight_layout()\n", "plt.savefig(EXT_SAVE_DIR / 'phase_portraits.png', dpi=300, bbox_inches='tight')\n", "plt.show()\n", "print(f\"Saved → {EXT_SAVE_DIR / 'phase_portraits.png'}\")" ] }, { "cell_type": "markdown", "id": "2b67f012", "metadata": {}, "source": [ "## Section D: Dose-Response Curves\n", "\n", "20 ODE runs (Gata1 × 10 levels + Stat3 × 10 levels). Sweeps from 0 (KO) through natural expression to 2× max (strong OE). Reveals whether the erythroid/myeloid switch is threshold-like (sharp transition) or graded." ] }, { "cell_type": "code", "execution_count": 37, "id": "6250349a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gata1 natural max (99th pct): 0.907\n", "Stat3 natural max (99th pct): 0.602\n", "Gata1 levels: [0. 0.201 0.403 0.604 0.806 1.007 1.209 1.41 1.612 1.813]\n", "Stat3 levels: [0. 0.134 0.268 0.401 0.535 0.669 0.803 0.937 1.07 1.204]\n" ] } ], "source": [ "# ── Define dose levels ────────────────────────────────────────────────────────\n", "n_levels = 10\n", "gata1_max = float(np.percentile(adata.layers[SPLICED_KEY][:, gata1_raw_idx], 99))\n", "stat3_max = float(np.percentile(adata.layers[SPLICED_KEY][:, stat3_raw_idx], 99))\n", "\n", "gata1_levels = np.linspace(0, gata1_max * 2, n_levels)\n", "stat3_levels = np.linspace(0, stat3_max * 2, n_levels)\n", "\n", "print(f\"Gata1 natural max (99th pct): {gata1_max:.3f}\")\n", "print(f\"Stat3 natural max (99th pct): {stat3_max:.3f}\")\n", "print(f\"Gata1 levels: {np.round(gata1_levels, 3)}\")\n", "print(f\"Stat3 levels: {np.round(stat3_levels, 3)}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "5cb3d248", "metadata": {}, "outputs": [], "source": [ "# ── Run dose-response screen (checkpoint protected, ~40-60 min) ───────────────\n", "dose_resp_csv = EXT_SAVE_DIR / 'dose_response.csv'\n", "\n", "if dose_resp_csv.exists():\n", " dose_resp_df = pd.read_csv(dose_resp_csv)\n", " print(f\"Loaded from checkpoint: {len(dose_resp_df)} rows\")\n", "else:\n", " df_gata1 = sch.dyn.run_dose_response(\n", " adata, 'Gata1', gata1_levels,\n", " lineage_A_clusters=ERYTHROID,\n", " lineage_B_clusters=MYELOID,\n", " basis=BASIS,\n", " wt_flow_key=_WT_VEL_FLOW_KEY,\n", " natural_max=gata1_max,\n", " cluster_key=CLUSTER_KEY,\n", " simulate_kwargs=dict(dt=5.0, n_steps=100,\n", " use_cluster_specific_GRN=True,\n", " n_jobs=-1, device=DEVICE),\n", " )\n", " df_stat3 = sch.dyn.run_dose_response(\n", " adata, 'Stat3', stat3_levels,\n", " lineage_A_clusters=ERYTHROID,\n", " lineage_B_clusters=MYELOID,\n", " basis=BASIS,\n", " wt_flow_key=_WT_VEL_FLOW_KEY,\n", " natural_max=stat3_max,\n", " cluster_key=CLUSTER_KEY,\n", " simulate_kwargs=dict(dt=5.0, n_steps=100,\n", " use_cluster_specific_GRN=True,\n", " n_jobs=-1, device=DEVICE),\n", " )\n", " dose_resp_df = pd.concat([df_gata1, df_stat3], ignore_index=True)\n", " dose_resp_df.to_csv(dose_resp_csv, index=False)\n", " print(f\"Saved {len(dose_resp_df)} rows -> {dose_resp_csv}\")\n", "\n", "print(dose_resp_df)" ] }, { "cell_type": "code", "execution_count": 58, "id": "fac0aa84", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved → /Users/bernaljp/Documents/SCHData/checkpoints/perturbation_extended/dose_response.png\n" ] } ], "source": [ "# ── Plot dose-response curves ─────────────────────────────────────────────────\n", "fig, axes = plt.subplots(1, 2, figsize=(13, 5), sharey=False)\n", "fig.suptitle('Dose-Response: Lineage Bias vs Perturbation Level', fontsize=13)\n", "\n", "gene_info = {\n", " 'Gata1': {'color': '#E74C3C', 'ax': axes[0], 'max': gata1_max},\n", " 'Stat3': {'color': '#2980B9', 'ax': axes[1], 'max': stat3_max},\n", "}\n", "\n", "for gene, info in gene_info.items():\n", " sub = dose_resp_df[dose_resp_df['gene'] == gene].sort_values('level_frac')\n", " ax = info['ax']\n", "\n", " ax.plot(sub['level_frac'], sub['lineage_bias'],\n", " 'o-', color=info['color'], lw=2, ms=7, zorder=3)\n", "\n", " ax.fill_between(sub['level_frac'], sub['lineage_bias'], 0,\n", " where=(sub['lineage_bias'] >= 0),\n", " alpha=0.15, color='#E74C3C', label='Erythroid-biasing')\n", " ax.fill_between(sub['level_frac'], sub['lineage_bias'], 0,\n", " where=(sub['lineage_bias'] < 0),\n", " alpha=0.15, color='#2980B9', label='Myeloid-biasing')\n", "\n", " ax.axhline(0, color='k', lw=1, ls='--')\n", " ax.axvline(1.0, color='gray', lw=1, ls=':', label='Natural expression (99th pct)')\n", "\n", " ko_bias = sub[sub['level_frac'] < 0.01]['lineage_bias'].values[0]\n", " ax.annotate(f'KO: {ko_bias:.3f}',\n", " xy=(0, ko_bias), xytext=(0.3, ko_bias * 0.7),\n", " arrowprops=dict(arrowstyle='->', color='k', lw=1),\n", " fontsize=9)\n", "\n", " ax.set_xlabel('Perturbation level (fraction of natural max)', fontsize=10)\n", " ax.set_ylabel('Lineage bias (+ erythroid, − myeloid)', fontsize=10)\n", " ax.set_title(f'{gene}', fontsize=12)\n", " ax.legend(fontsize=8)\n", " ax.grid(True, alpha=0.3)\n", "\n", "plt.tight_layout()\n", "plt.savefig(EXT_SAVE_DIR / 'dose_response.png', dpi=300, bbox_inches='tight')\n", "plt.show()\n", "print(f\"Saved → {EXT_SAVE_DIR / 'dose_response.png'}\")" ] }, { "cell_type": "code", "execution_count": 59, "id": "0c74910d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bootstrap rows for candidates: 1600\n", "Unique candidates found: ['Irf1', 'Irf2', 'Irf8', 'Mef2c', 'Myc', 'Nfatc3', 'Nfkb1', 'Nr3c1', 'Rel', 'Rreb1', 'Runx1', 'Stat1', 'Stat3', 'Ybx1', 'Zbtb1', 'Zbtb7a']\n", "\n", "Candidate bootstrap stats (lineage_bias = score_A - score_B, rank-based):\n", " mean lo hi\n", "gene \n", "Mef2c -2182.06 -2472.050 -1813.300\n", "Runx1 -1545.09 -1874.600 -1034.825\n", "Irf2 -1400.41 -1823.000 -1037.950\n", "Irf1 -1235.54 -1645.575 -592.150\n", "Irf8 -1149.91 -1565.325 -767.950\n", "Zbtb1 -502.07 -1009.150 107.625\n", "Ybx1 -476.18 -633.675 -323.700\n", "Stat3 -346.45 -794.050 65.675\n", "Rel -111.43 -763.675 438.775\n", "Nfatc3 -7.20 -284.550 344.625\n", "Rreb1 223.15 -268.250 764.475\n", "Zbtb7a 499.95 163.650 844.725\n", "Nfkb1 584.12 202.475 970.325\n", "Myc 660.43 193.975 1104.525\n", "Nr3c1 681.44 277.025 1163.300\n", "Stat1 1708.23 1384.800 1950.675\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved → /Users/bernaljp/Documents/SCHData/checkpoints/perturbation_extended/bootstrap_stability.png\n", "\n", "Stat3 bootstrap: mean=-346.4, 95% CI=[-794.0, 65.7]\n", "CI excludes 0: No — CI spans 0\n" ] } ], "source": [ "# ── Bootstrap stability plot (candidates only) ────────────────────────────────\n", "# gene is the index in score_driver_tfs output — reset for groupby\n", "boot_df2 = boot_df.reset_index()\n", "boot_df2 = boot_df2.rename(columns={boot_df2.columns[0]: 'gene'})\n", "\n", "# Filter to candidates only\n", "boot_cand = boot_df2[boot_df2['gene'].isin(CANDIDATES)]\n", "print(f\"Bootstrap rows for candidates: {len(boot_cand)}\")\n", "print(f\"Unique candidates found: {sorted(boot_cand['gene'].unique())}\")\n", "\n", "# Summarise: mean + 2.5th/97.5th percentile per gene\n", "boot_stats = (boot_cand.groupby('gene')['lineage_bias']\n", " .agg(mean='mean',\n", " lo=lambda x: np.percentile(x, 2.5),\n", " hi=lambda x: np.percentile(x, 97.5))\n", " .sort_values('mean'))\n", "\n", "print(\"\\nCandidate bootstrap stats (lineage_bias = score_A - score_B, rank-based):\")\n", "print(boot_stats.to_string())\n", "\n", "special_colors = {'Stat3': '#E67E22'}\n", "\n", "fig, ax = plt.subplots(figsize=(12, 5))\n", "for i, (gene, row) in enumerate(boot_stats.iterrows()):\n", " color = special_colors.get(gene, '#95A5A6')\n", " ax.bar(i, row['mean'], color=color, edgecolor='k', linewidth=0.5, zorder=3)\n", " ax.errorbar(i, row['mean'],\n", " yerr=[[row['mean'] - row['lo']], [row['hi'] - row['mean']]],\n", " fmt='none', color='black', capsize=4, lw=1.5, zorder=4)\n", "\n", "ax.axhline(0, color='k', lw=1, ls='--')\n", "ax.set_xticks(range(len(boot_stats)))\n", "ax.set_xticklabels(boot_stats.index, rotation=45, ha='right', fontsize=9)\n", "ax.set_ylabel('score_A − score_B (rank-based, + = erythroid-correlated)', fontsize=10)\n", "ax.set_title('Bootstrap Stability of Candidate TF Lineage Bias\\n'\n", " '(static metric; 100 bootstraps; orange = Stat3)', fontsize=12)\n", "ax.grid(True, axis='y', alpha=0.3)\n", "\n", "from matplotlib.patches import Patch\n", "legend_handles = [Patch(color='#E67E22', label='Stat3'),\n", " Patch(color='#95A5A6', label='Other candidates')]\n", "ax.legend(handles=legend_handles, fontsize=9)\n", "\n", "plt.tight_layout()\n", "plt.savefig(EXT_SAVE_DIR / 'bootstrap_stability.png', dpi=300, bbox_inches='tight')\n", "plt.show()\n", "print(f\"Saved → {EXT_SAVE_DIR / 'bootstrap_stability.png'}\")\n", "\n", "# Stat3 stability summary\n", "stat3_lo = boot_stats.loc['Stat3', 'lo']\n", "stat3_hi = boot_stats.loc['Stat3', 'hi']\n", "stat3_mean = boot_stats.loc['Stat3', 'mean']\n", "print(f\"\\nStat3 bootstrap: mean={stat3_mean:.1f}, 95% CI=[{stat3_lo:.1f}, {stat3_hi:.1f}]\")\n", "print(f\"CI excludes 0: {'YES ✓' if (stat3_lo > 0 or stat3_hi < 0) else 'No — CI spans 0'}\")" ] }, { "cell_type": "code", "execution_count": 40, "id": "217cd81c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded from checkpoint: 199900 rows\n", "Bootstrap shape: (199900, 4)\n" ] } ], "source": [ "# ── Bootstrap stability (checkpoint protected, ~10-20 min) ────────────────────\n", "boot_csv = EXT_SAVE_DIR / 'bootstrap_stability.csv'\n", "\n", "if boot_csv.exists():\n", " boot_df = pd.read_csv(boot_csv, index_col=0)\n", " print(f\"Loaded from checkpoint: {len(boot_df)} rows\")\n", "else:\n", " n_bootstraps = 100\n", " boot_results = []\n", " rng = np.random.default_rng(42)\n", "\n", " for b in tqdm(range(n_bootstraps), desc='Bootstrap'):\n", " # Resample cells within each cluster (with replacement)\n", " boot_idx = []\n", " for cluster in CLUSTER_ORDER:\n", " mask = adata.obs[CLUSTER_KEY] == cluster\n", " idx = np.where(mask)[0]\n", " if len(idx) > 0:\n", " boot_idx.extend(rng.choice(idx, size=len(idx), replace=True))\n", "\n", " adata_b = adata[boot_idx].copy()\n", "\n", " # Recompute energy-gene correlation (W matrices unchanged — loaded from model)\n", " sch.tl.compute_energies(adata_b, cluster_key=CLUSTER_KEY, spliced_key=SPLICED_KEY)\n", " sch.tl.energy_gene_correlation(adata_b, spliced_key=SPLICED_KEY, cluster_key=CLUSTER_KEY)\n", "\n", " # Score TFs using all candidate genes\n", " scores_b = sch.tl.score_driver_tfs(adata_b, ERYTHROID, MYELOID,\n", " cluster_key=CLUSTER_KEY)\n", " scores_b['bootstrap'] = b\n", " boot_results.append(scores_b[['lineage_bias', 'score_A', 'score_B', 'bootstrap']])\n", "\n", " boot_df = pd.concat(boot_results)\n", " boot_df.to_csv(boot_csv)\n", " print(f\"Saved {len(boot_df)} rows → {boot_csv}\")\n", "\n", "print(f\"Bootstrap shape: {boot_df.shape}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "e6e9b387", "metadata": {}, "outputs": [], "source": [ "# ── Bootstrap stability plot ──────────────────────────────────────────────────\n", "# Summarise: mean + 2.5th/97.5th percentile per gene\n", "boot_stats = (boot_df.groupby(level=0)['lineage_bias']\n", " .agg(mean='mean',\n", " lo=lambda x: np.percentile(x, 2.5),\n", " hi=lambda x: np.percentile(x, 97.5))\n", " .sort_values('mean'))\n", "\n", "# Highlight Stat3 (secondary Pareto rank) and Gata1 if present\n", "special_colors = {'Stat3': '#E67E22', 'Gata1': '#E74C3C'}\n", "\n", "fig, ax = plt.subplots(figsize=(12, 5))\n", "for i, (gene, row) in enumerate(boot_stats.iterrows()):\n", " color = special_colors.get(gene, '#95A5A6')\n", " ax.bar(i, row['mean'], color=color, edgecolor='k', linewidth=0.5, zorder=3)\n", " ax.errorbar(i, row['mean'],\n", " yerr=[[row['mean'] - row['lo']], [row['hi'] - row['mean']]],\n", " fmt='none', color='black', capsize=4, lw=1.5, zorder=4)\n", "\n", "ax.axhline(0, color='k', lw=1, ls='--')\n", "ax.set_xticks(range(len(boot_stats)))\n", "ax.set_xticklabels(boot_stats.index, rotation=45, ha='right', fontsize=9)\n", "ax.set_ylabel('Lineage bias (mean ± 95% CI across 100 bootstraps)', fontsize=10)\n", "ax.set_title('Bootstrap Stability of TF Lineage Bias Scores\\n'\n", " '(orange = Stat3; bars = 2.5th–97.5th percentile)', fontsize=12)\n", "ax.grid(True, axis='y', alpha=0.3)\n", "\n", "# Legend\n", "from matplotlib.patches import Patch\n", "legend_handles = [Patch(color='#E67E22', label='Stat3'),\n", " Patch(color='#95A5A6', label='Other candidates')]\n", "ax.legend(handles=legend_handles, fontsize=9)\n", "\n", "plt.tight_layout()\n", "plt.savefig(EXT_SAVE_DIR / 'bootstrap_stability.png', dpi=150, bbox_inches='tight')\n", "plt.show()\n", "print(f\"Saved → {EXT_SAVE_DIR / 'bootstrap_stability.png'}\")\n", "print(f\"\\nStat3 95% CI: [{boot_stats.loc['Stat3','lo']:.4f}, {boot_stats.loc['Stat3','hi']:.4f}]\")\n", "# Check if 0 is within Stat3 CI\n", "stat3_lo = boot_stats.loc['Stat3','lo']\n", "stat3_hi = boot_stats.loc['Stat3','hi']\n", "print(f\"Stat3 rank robust: {'YES ✓ (CI excludes 0)' if stat3_lo > 0 else 'CI includes 0 — weaker evidence'}\")" ] }, { "cell_type": "markdown", "id": "0c0d054d", "metadata": {}, "source": [ "## Section G: Stat3 Tier Classification via Differential Expression\n", "\n", "**Question:** Is Stat3 a Level 2 result (differential expression also finds it) or a Level 3 result (only the scHopfield ODE screen finds it)?\n", "\n", "Both outcomes are scientifically valid. Level 2 means the perturbation screen agrees with a simpler expression-based test — validating the model's directionality without being its unique contribution. Level 3 means DE does not prominently identify Stat3, making the perturbation screen's directionality prediction a genuinely novel contribution.\n", "\n", "**Method:** Mann-Whitney U test on spliced expression (`Ms` layer) between erythroid clusters (1Ery-7MEP) and myeloid clusters (9GMP-18Eos). Rank all genes by |log2FC|. Gata1 should appear as erythroid-high (negative FC) as a sanity check." ] }, { "cell_type": "code", "execution_count": null, "id": "094be8ee", "metadata": {}, "outputs": [], "source": [ "# ── Stat3 Tier Classification: Differential Expression ───────────────────────\n", "de_csv = EXT_SAVE_DIR / 'de_results.csv'\n", "\n", "if de_csv.exists():\n", " de_results = pd.read_csv(de_csv, index_col=0)\n", " print(f\"Loaded from checkpoint: {len(de_results)} genes\")\n", "else:\n", " de_results = sch.tl.lineage_de(\n", " adata,\n", " lineage_A_clusters=ERYTHROID,\n", " lineage_B_clusters=MYELOID,\n", " cluster_key=CLUSTER_KEY,\n", " spliced_key=SPLICED_KEY,\n", " )\n", " de_results.to_csv(de_csv)\n", " print(f\"Saved {len(de_results)} genes -> {de_csv}\")\n", "\n", "# ── Tier classification ───────────────────────────────────────────────────────\n", "stat3_rank = int(de_results.loc['Stat3', 'rank'])\n", "stat3_log2fc = float(de_results.loc['Stat3', 'log2fc'])\n", "stat3_pval = float(de_results.loc['Stat3', 'pval'])\n", "gata1_rank = int(de_results.loc['Gata1', 'rank'])\n", "gata1_log2fc = float(de_results.loc['Gata1', 'log2fc'])\n", "\n", "tier = \"Level 2\" if stat3_rank <= 20 else \"Level 3\"\n", "print(f\"\\n=== Stat3 Tier Classification ===\")\n", "print(f\"Stat3 | rank={stat3_rank:4d} | log2FC={stat3_log2fc:+.3f} | p={stat3_pval:.2e} --> {tier}\")\n", "print(f\"Gata1 | rank={gata1_rank:4d} | log2FC={gata1_log2fc:+.3f} (sanity: expected erythroid-high = negative FC)\")\n", "print()\n", "if tier == \"Level 2\":\n", " print(\"Interpretation: Stat3 is a Level 2 finding.\")\n", " print(\" DE independently identifies it as lineage-specific.\")\n", " print(\" The perturbation screen adds mechanistic directionality.\")\n", "else:\n", " print(\"Interpretation: Stat3 is a Level 3 finding.\")\n", " print(\" DE does not prominently identify Stat3.\")\n", " print(\" The perturbation screen's identification is a novel ODE-only contribution.\")\n", "\n", "# ── Plot: top 20 TFs by |log2FC| ─────────────────────────────────────────────\n", "top20 = de_results.head(20).copy()\n", "\n", "bar_colors = []\n", "for gene in top20.index:\n", " if gene == 'Stat3':\n", " bar_colors.append('#E67E22')\n", " elif gene == 'Gata1':\n", " bar_colors.append('#E74C3C')\n", " else:\n", " bar_colors.append('#3498DB' if top20.loc[gene, 'log2fc'] > 0 else '#E74C3C')\n", "\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", "ax.barh(range(len(top20)), top20['log2fc'].values, color=bar_colors, edgecolor='k', linewidth=0.5)\n", "ax.set_yticks(range(len(top20)))\n", "ax.set_yticklabels(top20.index, fontsize=9)\n", "ax.axvline(0, color='k', lw=1, ls='--')\n", "ax.set_xlabel('log2FC (myeloid / erythroid)', fontsize=11)\n", "ax.set_title(\n", " f'Tier classification: Stat3 expression profile across lineages\\n'\n", " f'Stat3 rank={stat3_rank} of {len(de_results)} genes ({tier})',\n", " fontsize=11\n", ")\n", "ax.invert_yaxis()\n", "\n", "from matplotlib.patches import Patch\n", "ax.legend(handles=[\n", " Patch(color='#E67E22', label=f'Stat3 (rank {stat3_rank})'),\n", " Patch(color='#E74C3C', label='Gata1 / erythroid-high'),\n", " Patch(color='#3498DB', label='Myeloid-high'),\n", "], fontsize=9, loc='lower right')\n", "\n", "plt.tight_layout()\n", "plt.savefig(EXT_SAVE_DIR / 'de_tier_classification.png', dpi=300, bbox_inches='tight')\n", "plt.show()\n", "print(f\"Saved -> {EXT_SAVE_DIR / 'de_tier_classification.png'}\")" ] }, { "cell_type": "code", "execution_count": 101, "id": "e5966981", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns: ['geneA', 'geneB', 'score_A', 'score_B', 'lineage_bias']\n", "Shape: (66, 5)\n", "\n", "Existing pairs with Gata1: 11\n", "Existing pairs with Stat3: 11\n", "\n", "All existing pairs:\n", " Irf8 + Nfia lineage_bias=+0.0325\n", " Irf8 + Arhgef12 lineage_bias=+0.0617\n", " Irf8 + Ezh2 lineage_bias=+0.0634\n", " Irf8 + E2f4 lineage_bias=-0.1982\n", " Irf8 + Gata1 lineage_bias=-0.1960\n", " Irf8 + Myc lineage_bias=-0.0149\n", " Cebpa + Nfia lineage_bias=-0.0135\n", " Cebpa + Arhgef12 lineage_bias=+0.0152\n", " Cebpa + Ezh2 lineage_bias=+0.0156\n", " Cebpa + E2f4 lineage_bias=-0.2471\n", " Cebpa + Gata1 lineage_bias=-0.2455\n", " Cebpa + Myc lineage_bias=-0.0626\n", " Meis1 + Nfia lineage_bias=-0.0375\n", " Meis1 + Arhgef12 lineage_bias=-0.0068\n", " Meis1 + Ezh2 lineage_bias=-0.0078\n", " Meis1 + E2f4 lineage_bias=-0.2740\n", " Meis1 + Gata1 lineage_bias=-0.2610\n", " Meis1 + Myc lineage_bias=-0.0866\n", " Stat3 + Nfia lineage_bias=+0.1366\n", " Stat3 + Arhgef12 lineage_bias=+0.1637\n", " Stat3 + Ezh2 lineage_bias=+0.1623\n", " Stat3 + E2f4 lineage_bias=-0.1146\n", " Stat3 + Gata1 lineage_bias=-0.0969\n", " Stat3 + Myc lineage_bias=+0.0835\n", " Runx1 + Nfia lineage_bias=-0.0066\n", " Runx1 + Arhgef12 lineage_bias=+0.0191\n", " Runx1 + Ezh2 lineage_bias=+0.0229\n", " Runx1 + E2f4 lineage_bias=-0.2367\n", " Runx1 + Gata1 lineage_bias=-0.2387\n", " Runx1 + Myc lineage_bias=-0.0686\n", " Nfic + Nfia lineage_bias=-0.0202\n", " Nfic + Arhgef12 lineage_bias=+0.0095\n", " Nfic + Ezh2 lineage_bias=+0.0104\n", " Nfic + E2f4 lineage_bias=-0.2405\n", " Nfic + Gata1 lineage_bias=-0.2467\n", " Nfic + Myc lineage_bias=-0.0614\n", " Irf8 + Cebpa lineage_bias=+0.0949\n", " Irf8 + Meis1 lineage_bias=+0.0669\n", " Irf8 + Stat3 lineage_bias=+0.2294\n", " Irf8 + Runx1 lineage_bias=+0.0824\n", " Irf8 + Nfic lineage_bias=+0.0996\n", " Cebpa + Meis1 lineage_bias=+0.0192\n", " Cebpa + Stat3 lineage_bias=+0.1808\n", " Cebpa + Runx1 lineage_bias=+0.0510\n", " Cebpa + Nfic lineage_bias=+0.0441\n", " Meis1 + Stat3 lineage_bias=+0.1636\n", " Meis1 + Runx1 lineage_bias=+0.0235\n", " Meis1 + Nfic lineage_bias=+0.0101\n", " Stat3 + Runx1 lineage_bias=+0.1246\n", " Stat3 + Nfic lineage_bias=+0.1764\n", " Runx1 + Nfic lineage_bias=+0.0299\n", " Nfia + Arhgef12 lineage_bias=-0.0437\n", " Nfia + Ezh2 lineage_bias=-0.0465\n", " Nfia + E2f4 lineage_bias=-0.2935\n", " Nfia + Gata1 lineage_bias=-0.2950\n", " Nfia + Myc lineage_bias=-0.0673\n", " Arhgef12 + Ezh2 lineage_bias=-0.0135\n", " Arhgef12 + E2f4 lineage_bias=-0.2782\n", " Arhgef12 + Gata1 lineage_bias=-0.2718\n", " Arhgef12 + Myc lineage_bias=-0.0776\n", " Ezh2 + E2f4 lineage_bias=-0.2795\n", " Ezh2 + Gata1 lineage_bias=-0.2781\n", " Ezh2 + Myc lineage_bias=-0.0863\n", " E2f4 + Gata1 lineage_bias=-0.3432\n", " E2f4 + Myc lineage_bias=-0.1994\n", " Gata1 + Myc lineage_bias=-0.1937\n" ] } ], "source": [ "# ── Inspect existing pair KO data ────────────────────────────────────────────\n", "pair_ko_csv = SAVE_DIR / 'pair_ko_bias.csv'\n", "pair_df_existing = pd.read_csv(pair_ko_csv)\n", "print(\"Columns:\", pair_df_existing.columns.tolist())\n", "print(\"Shape:\", pair_df_existing.shape)\n", "print()\n", "gata1_existing = pair_df_existing[(pair_df_existing['geneA']=='Gata1') | (pair_df_existing['geneB']=='Gata1')]\n", "stat3_existing = pair_df_existing[(pair_df_existing['geneA']=='Stat3') | (pair_df_existing['geneB']=='Stat3')]\n", "print(f\"Existing pairs with Gata1: {len(gata1_existing)}\")\n", "print(f\"Existing pairs with Stat3: {len(stat3_existing)}\")\n", "print()\n", "print(\"All existing pairs:\")\n", "for _, r in pair_df_existing.iterrows():\n", " print(f\" {r['geneA']:10s} + {r['geneB']:10s} lineage_bias={r['lineage_bias']:+.4f}\")" ] }, { "cell_type": "markdown", "id": "c2daab47", "metadata": {}, "source": [ "## Section H: Recipe Double KO — Anchored Partner Selection\n", "\n", "**Anchors:** Gata1, Stat3. For each anchor, sweep all 16 CANDIDATES as potential partners.\n", "\n", "**Three independent approaches to partner selection:**\n", "\n", "1. **W^c network weights** — For each anchor, extract outgoing weights (what it regulates) and incoming weights (what regulates it) from the inferred GRN. Partners with the strongest positive or negative mean weights are mechanistically motivated candidates.\n", "\n", "2. **ΔX cosine similarity (model-specific)** — Using per-cluster mean |ΔX| from the existing `single_ko_per_cluster.csv` checkpoint, compute cosine similarity between the anchor's cluster-effect profile and each partner's. Partners with low similarity perturb *different* clusters → likely complementary effects. Note: this uses unsigned magnitudes; signed gene-space cosine similarity would require re-running single KO ODE simulations.\n", "\n", "3. **Literature (documented separately)** — Known TF interactions from literature cross-referenced against the candidate list.\n", "\n", "**Metrics (matching `06_lineage_drivers.ipynb` conventions):**\n", "- `cancellation_error = double_bias - (bias_A + bias_B)` — deviation from additive expectation\n", "- `dominant_epistasis = max_magnitude(synergy_ery, synergy_mye)` — per-lineage epistasis, then max by absolute magnitude\n", "- `relative_epistasis = cancellation_error / |bias_anchor|` — normalized for cross-anchor comparison" ] }, { "cell_type": "code", "execution_count": null, "id": "b5938daf", "metadata": {}, "outputs": [], "source": [ "\n", "# ── H1: W^c partner selection — diversified 4+4+4+4 strategy ───────────────────────\n", "#\n", "# For each anchor we select up to 16 W^c candidates using four independent criteria,\n", "# each contributing 4 genes (excluding genes already selected in prior steps):\n", "#\n", "# Step 1 — Hit count: top 4 by stability in anchor's lineage-specific clusters\n", "# Step 2 — Erythroid top: top 4 by mean |w_combined| across ERY clusters only\n", "# Step 3 — Myeloid top: top 4 by mean |w_combined| across MYE clusters only\n", "# Step 4 — All-cluster: top 4 by mean |w_combined| across all 19 clusters\n", "#\n", "# Anchor pools are fully independent: Gata1 != Stat3 candidate lists.\n", "\n", "varp_keys = sorted([k for k in adata.varp.keys() if k.startswith('W_')])\n", "\n", "ERY_KEYS = ['W_1Ery','W_2Ery','W_3Ery','W_4Ery','W_5Ery','W_6Ery','W_7MEP','W_8Mk']\n", "MYE_KEYS = [k for k in varp_keys if k not in ERY_KEYS]\n", "ALL_KEYS = varp_keys\n", "\n", "anchor_lineage = {'Gata1': ERY_KEYS, 'Stat3': MYE_KEYS}\n", "print(f\"Erythroid clusters ({len(ERY_KEYS)}): {ERY_KEYS}\")\n", "print(f\"Myeloid clusters ({len(MYE_KEYS)}): {MYE_KEYS}\")\n", "print(f\"Total clusters: {len(ALL_KEYS)}\")\n", "\n", "K = 10 # top-K genes per cluster for hit-count stability\n", "N_SELECT = 4 # genes per criterion\n", "\n", "\n", "def pick_top_n(series, n, exclude, min_val=0.0):\n", " \"\"\"Top-n by series value, excluding genes in `exclude` and below min_val.\"\"\"\n", " candidates = series[~series.index.isin(exclude)]\n", " candidates = candidates[candidates >= min_val]\n", " return list(candidates.nlargest(n).index)\n", "\n", "wc_data = {} # {anchor: DataFrame of selected W^c partners}\n", "anchor_partners = {} # {anchor: ordered list of selected partner names}\n", "\n", "for anchor in ['Gata1', 'Stat3']:\n", " lineage_keys = anchor_lineage[anchor]\n", "\n", " # ── W^c weights via package API ────────────────────────────────────────────\n", " df_all = sch.tl.grn_partner_weights(adata, anchor, cluster_keys=ALL_KEYS)\n", " df_ery = sch.tl.grn_partner_weights(adata, anchor, cluster_keys=ERY_KEYS)\n", " df_mye = sch.tl.grn_partner_weights(adata, anchor, cluster_keys=MYE_KEYS)\n", " df_lin = sch.tl.grn_partner_weights(adata, anchor, cluster_keys=lineage_keys)\n", "\n", " # ── Build unified DataFrame with aggregate scores ──────────────────────────\n", " df = df_all.copy()\n", " df['w_abs_ery'] = df_ery['w_abs_all']\n", " df['w_abs_mye'] = df_mye['w_abs_all']\n", " df['w_abs_lineage'] = df_lin['w_abs_all']\n", "\n", " # Hit count: how often each gene appears in top-K by |w_combined| per lineage cluster\n", " lin_short = [k.replace('W_', '') for k in lineage_keys]\n", " lin_cols = [f'w_{s}' for s in lin_short if f'w_{s}' in df_lin.columns]\n", " W_lin_vals = df_lin[lin_cols].values.T # (n_lineage_clusters, n_genes_minus_anchor)\n", " hit_arr = np.zeros(len(df), dtype=int)\n", " for row in W_lin_vals:\n", " top_idx = np.argsort(np.abs(row))[-K:]\n", " hit_arr[top_idx] += 1\n", " df['hit_count'] = hit_arr\n", "\n", " # ── Sequential 4+4+4+4 selection (study-specific) ─────────────────────────\n", " selected = []\n", "\n", " # Step 1: stability in lineage clusters (hit_count)\n", " sel_1 = pick_top_n(df['hit_count'], N_SELECT, selected + [anchor], min_val=1)\n", " selected.extend(sel_1)\n", "\n", " # Step 2: erythroid cluster signal\n", " sel_2 = pick_top_n(df['w_abs_ery'], N_SELECT, selected + [anchor], min_val=0.001)\n", " selected.extend(sel_2)\n", "\n", " # Step 3: myeloid cluster signal\n", " sel_3 = pick_top_n(df['w_abs_mye'], N_SELECT, selected + [anchor], min_val=0.001)\n", " selected.extend(sel_3)\n", "\n", " # Step 4: all-cluster mean\n", " sel_4 = pick_top_n(df['w_abs_all'], N_SELECT, selected + [anchor], min_val=0.001)\n", " selected.extend(sel_4)\n", "\n", " wc_data[anchor] = df.loc[selected].copy()\n", " anchor_partners[anchor] = selected\n", "\n", " # Annotate selection criterion\n", " criterion_map = {}\n", " for g in sel_1: criterion_map[g] = 'hit_count'\n", " for g in sel_2: criterion_map[g] = 'ery_top'\n", " for g in sel_3: criterion_map[g] = 'mye_top'\n", " for g in sel_4: criterion_map[g] = 'all_W'\n", " wc_data[anchor]['criterion'] = wc_data[anchor].index.map(criterion_map)\n", "\n", " print(f\"\\n{'='*64}\")\n", " print(f\"Anchor: {anchor} (lineage: {'erythroid' if anchor == 'Gata1' else 'myeloid'})\")\n", " print(f\" Step 1 hit_count ({len(sel_1)}): {sel_1}\")\n", " print(f\" Step 2 ery_top ({len(sel_2)}): {sel_2}\")\n", " print(f\" Step 3 mye_top ({len(sel_3)}): {sel_3}\")\n", " print(f\" Step 4 all_W ({len(sel_4)}): {sel_4}\")\n", " print(f\" Total: {len(selected)} W^c candidates\")\n", " print()\n", " display_cols = ['criterion','hit_count','w_abs_ery','w_abs_mye','w_abs_all']\n", " print(wc_data[anchor][display_cols].round(4).to_string())\n", "\n", "print(f\"\\n{'='*64}\")\n", "print(f\"Gata1 partners ({len(anchor_partners['Gata1'])}): {anchor_partners['Gata1']}\")\n", "print(f\"Stat3 partners ({len(anchor_partners['Stat3'])}): {anchor_partners['Stat3']}\")\n", "overlap = set(anchor_partners['Gata1']) & set(anchor_partners['Stat3'])\n", "print(f\"Overlap (swept independently for each anchor): {overlap if overlap else 'none'}\")\n", "\n", "# ── Cosine similarity from per-cluster |DeltaX| (for candidates already in checkpoint) ──\n", "single_ko_pc = pd.read_csv(SAVE_DIR / 'single_ko_per_cluster.csv', index_col=0)\n", "\n", "cos_sim_scores = {}\n", "for anchor in ['Gata1', 'Stat3']:\n", " vec_a = single_ko_pc.loc[anchor].values.astype(float) \\\n", " if anchor in single_ko_pc.index else None\n", " norm_a = (np.linalg.norm(vec_a) + 1e-10) if vec_a is not None else 1.0\n", " sims = {}\n", " for partner in anchor_partners[anchor]:\n", " if vec_a is None or partner not in single_ko_pc.index:\n", " sims[partner] = np.nan\n", " else:\n", " vec_p = single_ko_pc.loc[partner].values.astype(float)\n", " norm_p = np.linalg.norm(vec_p) + 1e-10\n", " sims[partner] = float(np.dot(vec_a, vec_p) / (norm_a * norm_p))\n", " cos_sim_scores[anchor] = sims\n" ] }, { "cell_type": "code", "execution_count": 117, "id": "935cfb27", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sweep partner counts:\n", " Gata1: 31 total (15 CANDIDATES + 15 W^c-only: ['Rps3', 'Gpx1', 'Car2', 'Cfl1', 'Rpl4', 'Fth1', 'Prdx2', 'Klf4', 'Ctsg', 'Apoe', 'H2afy', 'Ly6e', 'Vamp5', 'Mt2', 'Rpl32'])\n", " Stat3: 31 total (15 CANDIDATES + 16 W^c-only: ['Elane', 'Gstm1', 'Alas1', 'Klf4', 'Fli1', 'Hoxa5', 'Batf3', 'Mycn', 'Mlec', 'Pgd', 'Bhlha15', 'Calr', 'Gata1', 'Chd4', 'Rpl23', 'Klf1'])\n", "\n", "All unique new genes (not in CANDIDATES): ['Alas1', 'Apoe', 'Batf3', 'Bhlha15', 'Calr', 'Car2', 'Cfl1', 'Chd4', 'Ctsg', 'Elane', 'Fli1', 'Fth1', 'Gata1', 'Gpx1', 'Gstm1', 'H2afy', 'Hoxa5', 'Klf1', 'Klf4', 'Ly6e', 'Mlec', 'Mt2', 'Mycn', 'Pgd', 'Prdx2', 'Rpl23', 'Rpl32', 'Rpl4', 'Rps3', 'Vamp5']\n", "\n", "Single KO: 58 already cached, 15 new\n", " Apoe\n", " Bhlha15\n", " Calr\n", " Chd4\n", " Ctsg\n", " Fli1\n", " Hoxa5\n", " Klf1\n", " Ly6e\n", " Mlec\n", " Mt2\n", " Mycn\n", " Prdx2\n", " Rpl32\n", " Vamp5\n", "\n", "Running 15 new single KO simulations...\n", " KO: Apoe...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO: Bhlha15...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO: Calr...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO: Chd4...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO: Ctsg...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO: Fli1...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO: Hoxa5...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO: Klf1...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO: Ly6e...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO: Mlec...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO: Mt2...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO: Mycn...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO: Prdx2...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO: Rpl32...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO: Vamp5...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Completed 15 single KOs.\n", "Saved updated single KO checkpoint: 73 genes total\n", "\n", "New gene single KO biases:\n", " Alas1: +0.0410\n", " Apoe: -0.0099\n", " Batf3: -0.0030\n", " Bhlha15: -0.0015\n", " Calr: +0.1029\n", " Car2: -0.0774\n", " Cfl1: -0.0131\n", " Chd4: -0.0062\n", " Ctsg: +0.1644\n", " Elane: +0.2498\n", " Fli1: +0.0184\n", " Fth1: -0.0398\n", " Gata1: -0.2637\n", " Gpx1: -0.0180\n", " Gstm1: +0.0522\n", " H2afy: +0.0821\n", " Hoxa5: -0.0020\n", " Klf1: -0.1813\n", " Klf4: +0.0022\n", " Ly6e: +0.0578\n", " Mlec: +0.0122\n", " Mt2: -0.0399\n", " Mycn: +0.0006\n", " Pgd: +0.0051\n", " Prdx2: -0.0514\n", " Rpl23: -0.0063\n", " Rpl32: -0.0047\n", " Rpl4: +0.0177\n", " Rps3: +0.0105\n", " Vamp5: -0.0138\n", "\n", "Double KO: 176 pairs cached, 16 new to run\n", " Gata1 + Prdx2\n", " Gata1 + Ctsg\n", " Gata1 + Apoe\n", " Gata1 + H2afy\n", " Gata1 + Ly6e\n", " Gata1 + Vamp5\n", " Gata1 + Mt2\n", " Gata1 + Rpl32\n", " Stat3 + Fli1\n", " Stat3 + Hoxa5\n", " Stat3 + Mycn\n", " Stat3 + Mlec\n", " Stat3 + Bhlha15\n", " Stat3 + Calr\n", " Stat3 + Chd4\n", " Stat3 + Klf1\n", "\n", "Running 16 new double KO simulations...\n", " KO pair: (Gata1, Prdx2)...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO pair: (Gata1, Ctsg)...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO pair: (Gata1, Apoe)...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO pair: (Gata1, H2afy)...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO pair: (Gata1, Ly6e)...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO pair: (Gata1, Vamp5)...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO pair: (Gata1, Mt2)...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO pair: (Gata1, Rpl32)...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO pair: (Stat3, Fli1)...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO pair: (Stat3, Hoxa5)...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO pair: (Stat3, Mycn)...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO pair: (Stat3, Mlec)...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO pair: (Stat3, Bhlha15)...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO pair: (Stat3, Calr)...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO pair: (Stat3, Chd4)...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " KO pair: (Stat3, Klf1)...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Completed 16 pairwise KOs.\n", "Done. Total cached pairs: 192\n", "\n", "Saved recipe_df: 62 rows -> /Users/bernaljp/Documents/SCHData/checkpoints/perturbation_extended/recipe_double_ko.csv\n", "\n", "=== Recipe summary (sorted by synergy_score desc) ===\n", "\n", "Anchor: Gata1 (bias=-0.2637, myeloid-biasing; +synergy_score = synergistic)\n", "partner double_bias cancellation_error synergy_score hit_count wc_criterion source\n", " Ctsg -0.1238 -0.0244 0.0244 0 mye_top W^c\n", " Zbtb1 -0.2910 -0.0018 0.0018 0 Pareto\n", " Irf8 -0.1960 0.0002 -0.0002 0 Pareto\n", " Klf4 -0.2610 0.0005 -0.0005 2 ery_top W^c\n", " Runx1 -0.2387 0.0010 -0.0010 0 Pareto\n", " Stat3 -0.0969 0.0020 -0.0020 0 Pareto\n", " Irf2 -0.2664 0.0057 -0.0057 0 Pareto\n", " Ly6e -0.1993 0.0066 -0.0066 0 mye_top W^c\n", " Mef2c -0.2540 0.0081 -0.0081 0 Pareto\n", " Prdx2 -0.3050 0.0102 -0.0102 0 ery_top W^c\n", " Rel -0.2541 0.0104 -0.0104 0 Pareto\n", " Fth1 -0.2894 0.0141 -0.0141 3 ery_top W^c\n", " Nfatc3 -0.2576 0.0177 -0.0177 0 Pareto\n", " Stat1 -0.2703 0.0196 -0.0196 0 Pareto\n", " Zbtb7a -0.3087 0.0245 -0.0245 0 Pareto\n", " Car2 -0.3165 0.0246 -0.0246 3 hit_count W^c\n", " Irf1 -0.2378 0.0259 -0.0259 0 Pareto\n", " Gpx1 -0.2553 0.0264 -0.0264 4 hit_count W^c\n", " Nfkb1 -0.2422 0.0272 -0.0272 0 Pareto\n", " Rreb1 -0.2519 0.0306 -0.0306 0 Pareto\n", " Cfl1 -0.2390 0.0379 -0.0379 3 hit_count W^c\n", " H2afy -0.1426 0.0390 -0.0390 0 mye_top W^c\n", " Mt2 -0.2637 0.0400 -0.0400 1 all_W W^c\n", " Vamp5 -0.2354 0.0421 -0.0421 0 all_W W^c\n", " Nr3c1 -0.2627 0.0507 -0.0507 0 Pareto\n", " Rpl32 -0.2176 0.0509 -0.0509 0 all_W W^c\n", " Ybx1 -0.2023 0.0591 -0.0591 0 all_W W^c\n", " Rpl4 -0.1843 0.0617 -0.0617 3 ery_top W^c\n", " Apoe -0.2089 0.0648 -0.0648 0 mye_top W^c\n", " Rps3 -0.1825 0.0707 -0.0707 6 hit_count W^c\n", " Myc -0.1937 0.1576 -0.1576 0 Pareto\n", "\n", "Anchor: Stat3 (bias=+0.1648, erythroid-biasing; +synergy_score = synergistic)\n", "partner double_bias cancellation_error synergy_score hit_count wc_criterion source\n", " Irf1 0.2135 0.0487 0.0487 0 Pareto\n", " Zbtb1 0.1607 0.0213 0.0213 0 Pareto\n", " Klf1 0.0019 0.0185 0.0185 0 all_W W^c\n", " Zbtb7a 0.1114 0.0160 0.0160 0 Pareto\n", " Stat1 0.1511 0.0124 0.0124 0 Pareto\n", " Pgd 0.1803 0.0104 0.0104 4 mye_top W^c\n", " Myc 0.0835 0.0063 0.0063 0 Pareto\n", " Mlec 0.1820 0.0050 0.0050 2 mye_top W^c\n", " Klf4 0.1694 0.0024 0.0024 4 hit_count W^c\n", " Hoxa5 0.1650 0.0022 0.0022 0 ery_top W^c\n", " Gata1 -0.0969 0.0020 0.0020 0 all_W W^c\n", " Nr3c1 0.1171 0.0020 0.0020 0 Pareto\n", " Batf3 0.1635 0.0017 0.0017 3 ery_top W^c\n", " Nfkb1 0.1596 0.0004 0.0004 0 Pareto\n", " Irf2 0.1565 0.0000 0.0000 0 Pareto\n", "Bhlha15 0.1630 -0.0003 -0.0003 1 mye_top W^c\n", " Alas1 0.2051 -0.0007 -0.0007 4 hit_count W^c\n", " Chd4 0.1574 -0.0012 -0.0012 2 all_W W^c\n", " Rel 0.1612 -0.0028 -0.0028 0 Pareto\n", " Nfatc3 0.1504 -0.0029 -0.0029 0 Pareto\n", " Irf8 0.2294 -0.0030 -0.0030 0 Pareto\n", " Gstm1 0.2136 -0.0034 -0.0034 5 hit_count W^c\n", " Mycn 0.1609 -0.0045 -0.0045 2 ery_top W^c\n", " Rreb1 0.1411 -0.0050 -0.0050 0 Pareto\n", " Ybx1 0.1520 -0.0152 -0.0152 0 Pareto\n", " Mef2c 0.1491 -0.0173 -0.0173 0 Pareto\n", " Rpl23 0.1271 -0.0315 -0.0315 3 all_W W^c\n", " Fli1 0.1387 -0.0446 -0.0446 1 ery_top W^c\n", " Calr 0.2168 -0.0509 -0.0509 2 mye_top W^c\n", " Runx1 0.1246 -0.0642 -0.0642 0 Pareto\n", " Elane 0.3381 -0.0764 -0.0764 7 hit_count W^c\n" ] } ], "source": [ "\n", "# ── H2: Expanded recipe ODE sweep — anchor-specific diversified pools ────────────────\n", "#\n", "# Partner pools (from cell 25):\n", "# Gata1: CANDIDATES (minus Gata1) + 16 W^c candidates (minus any already in CANDIDATES)\n", "# Stat3: CANDIDATES (minus Stat3) + 16 W^c candidates (minus any already in CANDIDATES)\n", "#\n", "# Steps:\n", "# A. Single KO checkpoint — run single KOs for any new genes not yet computed\n", "# B. Double KO checkpoint — run double KOs for new (anchor, partner) pairs\n", "# C. Build recipe_df with expanded pools + wc_criterion annotation\n", "\n", "ANCHORS = ['Gata1', 'Stat3']\n", "\n", "sweep_partners = {\n", " 'Gata1': [g for g in CANDIDATES if g != 'Gata1'] +\n", " [g for g in anchor_partners['Gata1'] if g not in CANDIDATES and g != 'Gata1'],\n", " 'Stat3': [g for g in CANDIDATES if g != 'Stat3'] +\n", " [g for g in anchor_partners['Stat3'] if g not in CANDIDATES and g != 'Stat3'],\n", "}\n", "\n", "print(\"Sweep partner counts:\")\n", "for a, ps in sweep_partners.items():\n", " wc_new = [p for p in ps if p not in CANDIDATES]\n", " print(f\" {a}: {len(ps)} total ({len(CANDIDATES)-1} CANDIDATES + {len(wc_new)} W^c-only: {wc_new})\")\n", "\n", "all_new_genes = sorted({g for ps in sweep_partners.values() for g in ps if g not in CANDIDATES})\n", "print(f\"\\nAll unique new genes (not in CANDIDATES): {all_new_genes}\")\n", "\n", "# ──────────────────────────────────────────────────────────────────────────────\n", "# A. Single KO checkpoint\n", "# ──────────────────────────────────────────────────────────────────────────────\n", "sko_csv = SAVE_DIR / 'single_ko_bias.csv'\n", "sko_pc_csv = SAVE_DIR / 'single_ko_per_cluster.csv'\n", "\n", "existing_sko = pd.read_csv(sko_csv, index_col=0)\n", "existing_sko_pc = pd.read_csv(sko_pc_csv, index_col=0)\n", "\n", "new_sko_genes = [g for g in all_new_genes if g not in existing_sko.index]\n", "print(f\"\\nSingle KO: {len(existing_sko)} already cached, {len(new_sko_genes)} new\")\n", "for g in new_sko_genes:\n", " print(f\" {g}\")\n", "\n", "if new_sko_genes:\n", " print(f\"\\nRunning {len(new_sko_genes)} new single KO simulations...\")\n", " new_bias, new_pc = sch.dyn.run_ko_screen(\n", " adata,\n", " genes=new_sko_genes,\n", " lineage_A_clusters=ERYTHROID,\n", " lineage_B_clusters=MYELOID,\n", " basis=BASIS,\n", " wt_flow_key=_WT_VEL_FLOW_KEY,\n", " cluster_key=CLUSTER_KEY,\n", " cluster_order=CLUSTER_ORDER,\n", " simulate_kwargs=dict(dt=5.0, n_steps=100,\n", " use_cluster_specific_GRN=True,\n", " n_jobs=-1, device=DEVICE),\n", " verbose=True,\n", " )\n", " new_sko_df = pd.DataFrame(new_bias).T\n", " new_sko_pc_df = pd.DataFrame(new_pc).T\n", " new_sko_df.index.name = 'gene'\n", " new_sko_pc_df.index.name = 'gene'\n", "\n", " existing_sko = pd.concat([existing_sko, new_sko_df])\n", " existing_sko_pc = pd.concat([existing_sko_pc, new_sko_pc_df])\n", " existing_sko.to_csv(sko_csv)\n", " existing_sko_pc.to_csv(sko_pc_csv)\n", " print(f\"Saved updated single KO checkpoint: {len(existing_sko)} genes total\")\n", "\n", "single_ko_dict = existing_sko[['score_A', 'score_B', 'lineage_bias']].to_dict('index')\n", "\n", "print(\"\\nNew gene single KO biases:\")\n", "for g in all_new_genes:\n", " b = single_ko_dict.get(g, {}).get('lineage_bias', float('nan'))\n", " print(f\" {g}: {b:+.4f}\")\n", "\n", "# ──────────────────────────────────────────────────────────────────────────────\n", "# B. Double KO checkpoint\n", "# ──────────────────────────────────────────────────────────────────────────────\n", "recipe_csv = EXT_SAVE_DIR / 'recipe_double_ko.csv'\n", "\n", "# Lookup from original 45-pair screen\n", "existing_pairs = {}\n", "for _, row in pair_df_existing.iterrows():\n", " val = {'score_A': row['score_A'], 'score_B': row['score_B'],\n", " 'lineage_bias': row['lineage_bias']}\n", " existing_pairs[(row['geneA'], row['geneB'])] = val\n", " existing_pairs[(row['geneB'], row['geneA'])] = val\n", "\n", "# Load previously computed recipe results\n", "recipe_lookup = {}\n", "if recipe_csv.exists():\n", " prev = pd.read_csv(recipe_csv)\n", " for _, row in prev.iterrows():\n", " recipe_lookup[(row['anchor'], row['partner'])] = {\n", " 'score_A': np.nan, 'score_B': np.nan,\n", " 'lineage_bias': row['double_bias']\n", " }\n", "\n", "for key, val in existing_pairs.items():\n", " if key not in recipe_lookup:\n", " recipe_lookup[key] = val\n", "\n", "# Determine new pairs needed\n", "new_pairs = []\n", "for anchor in ANCHORS:\n", " for partner in sweep_partners[anchor]:\n", " key, key_r = (anchor, partner), (partner, anchor)\n", " if key not in recipe_lookup and key_r not in recipe_lookup:\n", " new_pairs.append(key)\n", "\n", "print(f\"\\nDouble KO: {len(recipe_lookup)} pairs cached, {len(new_pairs)} new to run\")\n", "for p in new_pairs:\n", " print(f\" {p[0]} + {p[1]}\")\n", "\n", "if new_pairs:\n", " print(f\"\\nRunning {len(new_pairs)} new double KO simulations...\")\n", " new_dko, _ = sch.dyn.run_pairwise_ko_screen(\n", " adata,\n", " pairs=new_pairs,\n", " lineage_A_clusters=ERYTHROID,\n", " lineage_B_clusters=MYELOID,\n", " basis=BASIS,\n", " wt_flow_key=_WT_VEL_FLOW_KEY,\n", " cluster_key=CLUSTER_KEY,\n", " cluster_order=CLUSTER_ORDER,\n", " simulate_kwargs=dict(dt=5.0, n_steps=100,\n", " use_cluster_specific_GRN=True,\n", " n_jobs=-1, device=DEVICE),\n", " verbose=True,\n", " )\n", " for (gA, gB), v in new_dko.items():\n", " recipe_lookup[(gA, gB)] = v\n", " print(f\"Done. Total cached pairs: {len(recipe_lookup)}\")\n", "\n", "# ──────────────────────────────────────────────────────────────────────────────\n", "# C. Build recipe_df with criterion annotation\n", "# ──────────────────────────────────────────────────────────────────────────────\n", "def max_magnitude(a, b):\n", " return a if abs(a) >= abs(b) else b\n", "\n", "records = []\n", "for anchor in ANCHORS:\n", " anc_data = single_ko_dict.get(anchor, {})\n", " score_A_anc = anc_data.get('score_A', 0.0)\n", " score_B_anc = anc_data.get('score_B', 0.0)\n", " bias_anc = anc_data.get('lineage_bias', 0.0)\n", " bias_sign = 1 if bias_anc > 0 else -1\n", "\n", " for partner in sweep_partners[anchor]:\n", " par_data = single_ko_dict.get(partner, {})\n", " score_A_par = par_data.get('score_A', 0.0)\n", " score_B_par = par_data.get('score_B', 0.0)\n", " bias_par = par_data.get('lineage_bias', 0.0)\n", "\n", " key = (anchor, partner)\n", " key_rev = (partner, anchor)\n", " pd_data = recipe_lookup.get(key, recipe_lookup.get(key_rev, {}))\n", " double_bias = pd_data.get('lineage_bias', np.nan)\n", " score_A_pair = pd_data.get('score_A', np.nan)\n", " score_B_pair = pd_data.get('score_B', np.nan)\n", "\n", " ce = double_bias - (bias_anc + bias_par)\n", " re = (ce / abs(bias_anc)) if abs(bias_anc) > 1e-6 else np.nan\n", " syn_ery = score_A_pair - max(score_A_anc, score_A_par)\n", " syn_mye = score_B_pair - max(score_B_anc, score_B_par)\n", " dom_epi = max_magnitude(syn_ery, syn_mye)\n", " syn_score = ce * bias_sign # direction-corrected: positive = synergistic\n", "\n", " wc_df = wc_data.get(anchor)\n", " in_wc = (wc_df is not None) and (partner in wc_df.index)\n", " criterion = wc_df.loc[partner, 'criterion'] if in_wc else ''\n", " hit_count_v = int(wc_df.loc[partner, 'hit_count']) if in_wc else 0\n", " w_abs_v = float(wc_df.loc[partner, 'w_abs_all']) if in_wc else np.nan\n", " cos_v = cos_sim_scores.get(anchor, {}).get(partner, np.nan)\n", "\n", " records.append({\n", " 'anchor': anchor,\n", " 'partner': partner,\n", " 'double_bias': double_bias,\n", " 'bias_anchor': bias_anc,\n", " 'bias_partner': bias_par,\n", " 'additive_expected': bias_anc + bias_par,\n", " 'cancellation_error': ce,\n", " 'synergy_score': syn_score,\n", " 'relative_epistasis': re,\n", " 'dominant_epistasis': dom_epi,\n", " 'synergy_ery': syn_ery,\n", " 'synergy_mye': syn_mye,\n", " 'hit_count': hit_count_v,\n", " 'w_abs_all': w_abs_v,\n", " 'cos_sim_cluster': cos_v,\n", " 'wc_criterion': criterion, # 'hit_count'|'ery_top'|'mye_top'|'all_W'|''\n", " 'is_wc': in_wc,\n", " 'source': 'W^c' if in_wc else 'Pareto',\n", " })\n", "\n", "recipe_df = pd.DataFrame(records)\n", "recipe_df.to_csv(recipe_csv, index=False)\n", "print(f\"\\nSaved recipe_df: {len(recipe_df)} rows -> {recipe_csv}\")\n", "\n", "# Summary\n", "print(\"\\n=== Recipe summary (sorted by synergy_score desc) ===\")\n", "for anchor in ANCHORS:\n", " bias_anchor = single_ko_dict.get(anchor, {}).get('lineage_bias', 0)\n", " bias_sign = 1 if bias_anchor > 0 else -1\n", " sub = recipe_df[recipe_df['anchor'] == anchor].sort_values('synergy_score', ascending=False)\n", " print(f\"\\nAnchor: {anchor} (bias={bias_anchor:+.4f}, \"\n", " f\"{'erythroid' if bias_sign > 0 else 'myeloid'}-biasing; \"\n", " f\"+synergy_score = synergistic)\")\n", " print(sub[['partner','double_bias','cancellation_error','synergy_score',\n", " 'hit_count','wc_criterion','source']].round(4).to_string(index=False))\n" ] }, { "cell_type": "code", "execution_count": 118, "id": "7443ae00", "metadata": {}, "outputs": [ { "data": { "image/png": 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nP/9Z6+AEaTPwK2UsA6V6z7rOzN+///3vdRBKgoypC3P9yMCU//znP/X9CfDm2pBraeR3rgcDKWO5liyyyCL1WpF6OsHKBMgjAeeU5Vwn3v3ud9e6InINSwA1wdAESLOUdga89HfdaeqpDIhJXZAAagKhqS9ST+21114T7FOu16mPcj3K7RYSbOxLguG5NufacPHFF9drcAb05PNzLW4GB+W7xLbbblu3lzo0572t2pAn+7tmDUQGRGbme96XYPVA03og3/0SBE/eTf7K9S2vy+1/uvO3gDgAAIPBTHEAgBZK52VmkmaWd34y8yxLqifol87IBFQzcywd2unUTWduOrTnn3/++v50UiYgnEB5Ou8zGyoyszgzkBqZBRV/+ctfaiA+wcXIZ/Rerj0BvAQXE9RNAC/3oexPZthmXxOcjwTaJyZBg2Zp2Ox7OpB7S0drZselszrHlY7r7n3LcSUIkWPILKrsQzqbM7MpndBN0CLWX3/9+jsBjwQ/I7N1E+SOzL7OzO98TmQ51b6kwzod4/He9763zprqS3Oe0xF/zTXX9AxkyKz37F+C4wmIZKZhNAH/vgKheX862ZuZ9MkbzT1MowmuZnBFAuttl0BP7scd+Z28vs8++wzoXGT1gKR7blVw00031XRIwCRlLkHzpE1m0q277rr19SlnCSo0s+ty7hPgamTmZ/N5v//97yf4rKwYkM9LoCMz1DPjvz9J977yA4wWqY8zSCT1VcpT6tzUwyk7uW6ljt9qq63qDNMEbRvNgKgEm1PnRwaEJdjcW4KaCRK/+c1vrtePZhZ06tncT7lZprr7vtrNAKMMAptUGct+pz7IShSRQVUZGBMJCDb3mc51Jtfn3N4ir20GyvTW13Wn+/pw6623lllnnbXnepNraILbuZ511w0ZMJfPzrlMXTIpqeNSN2YQT+T6k/1M/ZjrSL5DNPfTTjCzrdqQJ/u7Zg1EvhM1+9+s0pPr0aRM6rtfvotl0ED3958ExDMAoTt/AwDAYBAUBwBomYsuuqgu25pZSum8zk9mA2UGTjrJ03GdmV0JJGdGWhPkbgLQkeU2M4MpM8zTCdx0rqYDNbOoG1mqPLKNBMwn1TGawHL2L7+zP1n2sy/pCO5e8jOB+XT2zzLLLH2+vq997y0zydOJm5lY6cTv7rzv6/299yEz6pp7o+e55rWN3p+Z/zcznZpgQl+a92Vb3QMOujXvT3pktnlmjkeCHXlfArvd+5IleJuAS2+90ymfn1lpvc/F1LDUdmbIZaBEBi9kdl3kXGTQyEDORZYhzqzJDGjIbM8MDmnyTl95sK9z3+SRSX1eAuUJlvzxj3+s+5wASGbP9aV33m3yQzNoBUaD5h7OCeRmdYtIgDBlIIO2EjjsT1NHN/773//WmdaZtdvIyh5Z7juDoZrbRCTomGtDlplOgHNi259UGUt5z/Zzm4RmlY7Mes1M2+5rQoJ+ua5ecMEF9TqXgOnEjql3/dBsq69rcO86pvf1sTkfWSGjmf2dGeZZEr37OHprttl9nrPd/q5hbTHa82R/16y+ZMWBzD6PzOjOfnZvP0H17nzcO2/l++JAvvslf2XJ9Oa6GznWueeee5LfnwAAYHK1u8UCADAVSqA6Sz1nSfBIJ2WW3czSy5HAbpb0TKdkc+/H/mS2c2b35P6PkdlFX/ziF/uc3ZQOz3Tsp4MzS7f27rjP+zL7NZ+Z2Wu5h3fTkZtgb7cErxMgSKdqfnbbbbeefXglMss3s+g+9alP1ZlKmXXV+zN7yz4k6Jjzl87uzKzvHhDQV2d5Bh/EHXfcUZcI7W+GePd5y5KikXOTmd8Tk89Ix3FmVmX/s9xoAuLpSE+nfGYi5vxnRlpmdeXcdgdEmjTNTLfsYyQY3Nc9ZacGmamX5Wlz7nJv7vwk+NAEh/rSfU6Tj3beeec62CSz2tKRn/OfIEm2nb+TZ7LdlIec56Rzs1xt8kvSbiAymz9L+GewS+69mm1kUERf5ae//ACjSerHBJAzQ3ammWbqCRCmzsrKCROTstas7pHZrJmd2jv4l0FWCUKnTDYzcBOQzqCyLHM+sRmqAyljqa8TbM7npry+//3v77l1SW8ZvJaVRppVQKKvsj0xCWTnWpVrduT4m+W2GxkQlsebAW6Z8RyZpZ5rS37yd3c9l+NIOmRGcR5rrjnNYLEmcJrHU/e12WjPk/1ds/qSW+U0eSKz1rP/WSY/MjCg99L8Cb43twfIrPkE5Qfy3S8DRTKYoAmK5715be/vLgAAMBjMFAcAaJl0YGfp9F133bVnBnA6V7uXW05gLZ2szRLa/clMnSzTmeBrOi8zu/zQQw992euy9Ho6WtOZntmu6TDunn0dH/7wh+t9I7OUaGaTJbDXLBmdTtODDz6457WZwZSO0wQI0mGb383y6K9EOmuzxGjua5lO5wSGx48fP8ES6r3lWDKTKzOq0nH96U9/umfmUn+zqtLh23TsJsDRzCzvT16fZUVzj/AsaZ5lVycmS6Nm+d3MFkt6pJM6s/4zgysd7NnnnK8siZulXROYz/a7gyJzzTVXvU/sJz7xidrpnKBJ91KsU5MEcXovQ56ljLN8cX9Lyibw0ZzTlLEstZ4gRpbQTR7NYIPktcyiS97JcznHKQ+Z7Z17BWeJ9pTN3KM2tzoYiOxXAvZZ3SHpnf3OtlO2s6Rtd97JdvvKDzC5XnrkoXL/jpsN6vYGKoGyBPhSjhoZaHT//fdPMvia8pH6Ne/NgJQsT97X6hm5XuYez6973et6HltxxRVrsLC/1TYGWsZSx6Z8p8ymrk3Zz+09cq/o3nJtSr3Tfd/k3JJkl112meD2FhOT2bYZEJdgZuqvBBuPPPLICV6Ta2uWqs5y6LkWdM8I7/aWt7ylnvsEPo844oi6z6mDchwJDOfYc45ybT322GPr7OO8J5891B4Y/2hZ41d7Der2ppY82d81ayAyMz5LnJ922mk1r2X/e6/Gk/ySvJXVEZoVEgby3S/XsDyegV8Jlud+9/kMAAAYbGM6/a2VBADAgCS4mlnMCy64YJ2lfffTD5UNLtivPPfS/y1JPVimGztt+fW6B5f5Z5rzFW8jndoJ4CaYls76wZAO8dyrO4HxSJA1s5sSsIW+vHj/feWB3f6nlOf/d4nVQTXtdGXu408r4+aZ+KCEoZL7yWYmZu4lnuB3ykGCRRk8AqNBAuEvPfS/Mz2Hwtg55y7zfP9/V8mY2iWAmQE6WcEjg6lGi2a2c1bCmBISCL9v/P/dS32wzTvD7OXiDY8Ysu3DYLc3AACYfGaKAwAMsgSsE7h+5LknBn3bs0/3mlcVEM94yATDM8M4s8UHS2Y03XjjjXW2W2YCZdZzZstCfxKwTuD6pccHPktvoMbO+tphC4hHbk+QmXGZ+ZmAV2ZYCogzmoydfY5Rvf3RJDPKM1s3tyehf3PNMNuo3j4AADD8zBQHAHiVzNwAAACGivYGAMCrN3YQtgEAAAAAAAAAI5KgOAAAAAAAAACtJSgOAAAAAAAAQGtNM9w7AADQRi89eWfpjH9o0Lc7ZoY5y9hZFhj07QIAAAAAtJWgOADAEATEn/7JyqW8+Ozgb3zc9GWmLS4XGGfUO/VjF5anHxk/ZNufafYZyrbHrj1o27vuuuvKvffeW9Zee/C22e2FF14o3/3ud8tOO+1Uxo61oBfDa8sTLysPPjkE17D/b65Zpi9n7bLqoG1P+Zw6jB8/vjz33HODvt3pppuuzDDDDIO+XUa+O+64o1x55ZVl0003He5dAQBgCtCaAwAYZHWG+FAExOPFZwc0A/3OO+8siy66aLn44osneDwBgzw3HD74wQ+Wv/71ry97/LOf/Ww555xzyn333Vd23nnn+tiFF15Yvve979W/Tz/99PpDuyQg/uSDQ/czqYD7hz/84fKrX/2q5/9HH310edvb3laeeOKJnsfSSX7FFVfUv4888shyyCGHlOeff35Ax/fzn/+87LLLLi8rkz/60Y96HvvBD35QPve5z9W/L7jggnLccceViy66aMDnMPucn97++c9/ls9//vP17zPOOKP84he/KMNhYp99//33l7333nuCeun3v/99+da3vlVGQxClSbf+5Hj6C9Cmnkt9l3ov9V/3YwPZ9ne+852X1e2DLQHx+54Yup9JBdzbUD6HQn9lvluTlwZ6Db7mmmvKYYcdVkZDQHzBBRcss80226D/ZLvZ/sQkjyy55JLlfe97X817m2yySdlmm23KjTfeOCjHN5x1dbdJ1cfJQ8lLE9P9HW6gXnrppbL77ruXJ598srwaX/jCF8rJJ5/c8//Usynb3en08Y9/vPz0pz+tf2ewy9e//vXy8MMPl6EwsWtBfw4++ODy73//e0j2BwBgaicoDgDQUtNOO2354he/OEEQYSSbd955y0knnVT//te//tXTMZpO5/zQTmPGljLLXDMM2k+2NxCrrrpq+cc//tHz/0suuaSsssoq5c9//nP9/+OPP17uuuuusuyyy5bbbrutlqO11lqr/PrXvx7Q9t/+9reXq6++unQ6nfr/P/3pT2X11Vevvxt/+9vfyjve8Y76d4Jx6QjvDsq9UksttVT56le/Wv/OMQ7FzMqBmNhnf+1rXysf+chHJnhsnXXWKXvssUcZ6e6+++4avH6lUs+lvuvrsYFsOwGpBEanRLqOHVPKvK+ZftB+sr2pvXwOtb7y18T85z//KQ89NPi3exlsye9ZDSDl47HHHhu0n2wv2x1IeZpnnnnKeeedVwOqGVix8cYbl3322WdQjm846+q+vJr6uPs73ED9+Mc/LiuuuGKZZZZZyquRst1dd/Qu2ynzmRmex1JvJMi/2267lTPPPLOMFLvuums56KCDhns3AABayfLpAAAtlc7b1VZbrXbkJwDVe6ZhOnQzM2ellVYq++23X5lmmmnKN7/5zfKXv/ylBhwye+qYY46p20kwYumll64dxz/5yU/K9NNP37OtzEjLbLp0Lmb254Ybblj23Xff2rmb2aqZtfqGN7yhPPLIIz0dkpmV84c//KF23L/44otl5ZVXrrNptt9++zobL52jMd9889XPjNe+9rXlpptuKl/+8pfr/4866qieGT/f+MY3ymWXXVaXuN1ggw3qY4wOM88xQ9n59I0GbXsnbfPLOlN8UlI2DjzwwPr3o48+WvPvDjvsUDvOk4fSaZ4O+pSLBMI233zzstxyy9W8nhmCKSP7779/nX2WASif+cxnyhprrNGz/bnmmquWnZtvvrm85S1vqdv96Ec/Wvbcc89aNrJcbzru0/F9/fXXlzFjxtTPzey6vGfhhRcuP/zhD8tpp51Wl2vO/qY85XXdUr623nrrWvbe+c53li996Uu1kz9lNzNhM2MvZWPOOeecYP8ij6d+SD2QfU05SrlPfZF6IJ/73ve+t26n2eYpp5xS39vMVv3kJz9ZgxA5J5m1m5m62WbKe3+fffvtt9fyvthii02wP5nRd/nll9f6ITPrMiMzQdCkT2b/5fgym++AAw6oweP4xCc+UV/bXz2U/ck5zv7PPvvsNV3y+s0226wGtzIbOHVQ0ijnrndAJsf8m9/8puaDDDZI/ZOffH72Iz/JR8kHCSy++c1vru+JZ599tnzqU58q//3vf2sdmPOa85vPT9p2ax7r3nb2PTNTt912255AyXbbbVfPwworrFD3f8sttyxDae5Zpi9/2GvCfPNqrHXExXWm+NRQPlNGErjPdevBBx+s5Sjl9aqrriqLLLJInYWb/e0rjTMrvq983i3X0MyQT/lN/kreafJ39i3H19c1uFsey7X06aefrvn2Yx/7WL/lP/ubJcazT9m/vK77u8CUMuuss9afkSCDN5pZ9qm7jjjiiFruE2z/9Kc/Xd7znvfU1SBynlPvJci8wAIL1HP3zDPPlNe85jV18GLOaXd9mfObtMvjyePJl+9617sm+Ozk0+TPfG5ek0FGqdeywkLyaWa+5yf5IvuZwTTLLLNMradTR6bufv/731/LV1btuOeee2pZyP73ro8zKCX1etI7s+obfR1zttH9HS7nIPuZmc+pa7MfveutfC9MXZzZ8pFzlnOT2yJkvz7wgQ/UspG83lf+7J0mzaCwvD6DXw499NCav3OOUh5TNuaee+7y/e9/v6y//vr1PGy11Vb19gh5T1/ntVuOJeUzaTjzzDPX7b/xjW8sxx9/fPnZz35Wxo0bV4PuzWoojdQDfaVr6orUC/m+m3Oz44471utVjjN1CwAAg8dMcQCAFkvH4qWXXjrB7LcEmdL5luB2girpWE0HZmbbpbMwfycIlI7PZinPdJp+6EMfqoH03p3geU2CBZllk7/zO4GrBM/SuZgO2nQCpkM4su3MIjr//PNrJ3vzeCPLXCbIl5/ujtN0rKbTOIHvyPsTNMtxJHiUDtz85NgSLICJWWKJJWpnezq1UybS8ZzgbspL9yzRp556qvz2t78tG220UQ2EpSM7QaYEktLhn5mp6Ww//PDDe2addnfO//3vf6/5M4G15Zdfvg4uSVAiszPz/nR8p6xsscUW9T35nSDftddeWwNbzZK6CSKnk763Bx54oHbs//KXv6zlI2W4keBpAmRZkrZ3QDzlPgGMBA9SrhNgTNnPrQoSyEvH/llnnVWPfVJLRqe8J0ib8pfg5AknnDDRz85+5vMmJUGR1CcJLGTATmR/EwjJZ2WJ3ARqEozurx7K+UtAKufn2GOPrXVPJLCZ401QM/VgAjnf/va3J/j8BGLzfGaFnn322TXgmTyTYEgCmQk2JXCa4Ew+73e/+109r3/84x/r+7NfCXDmXL7pTW962fb70r3tnMvsW5POyTMJtEQGEuU8tlVbymeC8nlNAqdf+cpXatAtr09Q7YYbbug3jfvL5438nYEiObaU3+x7M2Cs0d81uFuOL2U0ZTWB94mV/+a2DNlezlkzQGZqlXObuiF1XyRfJJh67rnn1sBtAqTddVnOW2ZfZyBEgqh5XYLkqd9615fZTgYsJm2Tl/OeBFS7nXrqqTUQnbotn518kjoraZjBhUnDBJJzfWikPKXeS75JsDuy/QxQSr5MfdX7c1KnZYBR6uDkx+57r/d1zL2/w+WxDALJa5o6t/fS4Pl/gssZ/NhIPkwey3uyjQx0Gcj1aY455qiDQ2699dZ6a4B8dspHylYGf6TuSBlLfZDtpQxmsFLKfm6T0Nd57T3rPWmWYHzSJ8eZ1+SWFrkG5FqRY8136mZwQGNi6Zq0yffaBMSnhjoeAGC4mCkOANBimfWYTrh0nqcTLhJgSEdhOgIjM3wyqyWdoemgS5D5lltuqcGezC5rZBZeXzKLJrObMvs8Abl0oKZzLwGCzLxJICmzJ5v35/H11luvzt5L52Xv2U/9yWszUzMzZ9Jxms78BJoS7MhsonR0Rj47gYgspQv9SSAzwYyUhcyCy2yx5KnkswRMExhL/k1Hfff9m9NhHikrzQzB5MN00veWQF4GgST/J+82s8dSBjNbsFmauXslh2YgSO5zmiBJOuujO8DSLeWnCVJkP/qaDdqXlJHMlEsANjJLNhKUSd2Q4GJ+Mus2gchJ3RN1zTXXrL8TXMm9aCcmwYKck0lpgunZZgbmRNIq9UwTYM4gmaRXf/VQXp9zmnOftF133XXr+3JM2Y/MQGy2013fNYGsBFoTCE19kjryda973QTBxQQ4Uh8lX2RGeAIxCbw06dEE/xPgbO4fPlB5bwJBqY8THElaJN/G61//+j6DnG3RlvKZbaQczT///LW8ZfBFZJWUBN6yCktfadxfPm/kvCSI1+TZ5OMTTzxxgs/u7xo8MZkR3l/5T2Ax5SEyIC0DQRLkn5pkhnWOPVLHvPWtb62DHSL5LQPyEqjN7OQM2Gg05z7pnHLbvZpNBu/0XjY9dVmzfHbSODO8s80E1bvTKnVT8m3KRoK4kcE/yUv5rOSBps7orlMXX3zxnjo1r8kqIU2d07sezOCNzKxOcDkyq7q51/jEjrmRvJy6uLmHdwLM2Wb3SiGpN1O3dstAgex7Pjt1bLPUeV/5s7n+dJftDHjJjOxmO6lP8j0xdUfOW8pFyn+jqS+ylHpf57WRa2xmdL/73e+u/8/95fNzyCGH1OX0Z5xxxvp49jPH3D0orL90jdwKolvqjJw7AAAGl6A4AEDLpVM+Hf3NrKAsX5mZKJn5HeloTOdgZr5lKcc8ngBEOhG7Z9Y1HX3pDG1mrySAlk7GBJcS9EknYYLWeV+22f3+dGBGf48PRDoeM9sqgYh8XnM8uadnAu1Nh2X3TCaY1H2LExTIwJFIWclM3wSsJha4TYd5twREs3xqd35OwDTBhnTqp2O+KY9ZyjnLfSdgNbHtdy/FnFmkGUjSPZMuuj+vd9nqrQnmRDrwu7efYEaCJJn92C3bS0Cu97YzUzT702hWkOi9vHtfUrcMpNz3tc3sX2boNuchQaoESlO/9VUP5Tz2dU5Sb2R2cZbYjgSy+7qfbz4rwZWstpHAexM8amQAQJawTp2aJXZT/zSf1x2Mmty6rpFtZkBTAk9NIKrZVu/tt00bymd3Gekv/ftK4/7yeYKCzfPdkudSJrv1d63tfQ3v1l/5773/ebzt+W9i9xTvy//8z//U2b3Jt1nVIPmo9/enZqn7Zhs5j8lrWa6/W+86qzsd+suDCbZn6fUEY1MPJn9n1nYzUKS/OrV3nd07n07sO9vEjrmRY045y5LwzSoHGXA0qWtC96pEzT5MLH92y/5k1nYGluRa15Tt1CVZLaT5PtyXvs5rBrE0+5P97H4+5S4rRfTet+i9bxNL1yaPdNcdU2MZAwAYar5hAQBMBTJDMbPf0rGezsJ0yCYIlsBQAuFZ7jHB7TyXTs7MZsvr83xvWe4z789PZi3ldTvvvHO9h2+Wu00HbzoH00Ga2Xn5O48nuBF5PDNvEoDKDLm+ZsKkU7Kvjs7M1kyAKsGDBLQi+5wZa+mYzGykBKeyTzApmU2WJWMzK3SmmWbq6TjPcseZwTkxCQQ0M8jSIb799tu/rMM7KzXk3rcJnDazTjNrNQHYLBc7sZmbCWgk+JdZdSlDmfnaBMQmR8pSU46bcpuf3B4hAdxmufUsE5vZrylPqQ9S/lKeEqzLvmTGXGbz5bH8TGpJ9d6f3S3nIEvgvhLZvyy/G9mfzMxLgLS/eihB1KYeSn2TYGACGknfLJXbLF2b5akzw7Jb0jXBpQRzcm/wbCszHLvrp8xSzK0dEojKkr2pR5tjzv5lsFFkBY6B3Bu2d92XWZmZbZi8lKBu977lPLbZ1FA++0vj/vJ5IzNMM1v8jjvuqP/POckxd+vvGtz7Gt6d5/or/5Frb7bTLBvenDP+9xYzSafUE5kZ3N/3p4UWWqimY7OyTc5vZib3ri+b7zWRNL7yyitfNpM46Z1BgsnX2Wbu1Z36KfVbVv7INpJH+9qP3vkky31H6qsMLuqWwHoC2c2tJ7LE96SOuXeeyjLl2c/Mik9+7171YHKuCRPLn93yWG6lk31s6snU3ynrGZTQ+zZAkzqvuXY0EtDvnsWd2eYJvGffUl9lv7J/2c/e+zaQdJ2a6ngAgOFgpjgAwCAbM8OcpYybvpQXnx38jY+b/n+3/wqXUc9MxwSW05GfWXDpwEwH4Lbbbls7PXNP0SxHmRkqWdqy6XCfmNyzMjO1E1xoljjP+xJcT8AtQaos9ZtlRiOzOHNv0nTyJ4iUTuLeEvDIPRsTiOuW2VTpVMy+NjPocj/HdOKmozWB8QSommUtGfmeenh8OWmbXw7q9gYqM01TFppVById1Bk80ty7uT+f/OQn6/2f894EIdIp3j0rtJH8mvsady8NmyVqE7Tt6/Xd91ROIG+bbbapQagsk54VHCZXAldZFjbLTDcDSSJBgcze22+//eoAlexf7nOb4GMCHVmVIeUp5TT36o6sxpD/Z9Zccx/dV/LZqYMSSHklMrP7i1/8Yq2nErTIvZczO7K/eijLXaceSjrludwnOsee+i31XVbGyPnNstC9lzdPMDb7nbols/gSCMnfCXgk3+y1117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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved -> /Users/bernaljp/Documents/SCHData/checkpoints/perturbation_extended/recipe_double_ko.png\n", "\n", "=== W^c-derived findings by criterion ===\n", "\n", "Anchor Gata1 — W^c candidates by criterion:\n", " hit_count:\n", " Car2 CE=+0.0246 synergy=-0.0246 bias_partner=-0.077\n", " Gpx1 CE=+0.0264 synergy=-0.0264 bias_partner=-0.018\n", " Cfl1 CE=+0.0379 synergy=-0.0379 bias_partner=-0.013\n", " Rps3 CE=+0.0707 synergy=-0.0707 bias_partner=+0.010\n", " ery_top:\n", " Klf4 CE=+0.0005 synergy=-0.0005 bias_partner=+0.002\n", " Prdx2 CE=+0.0102 synergy=-0.0102 bias_partner=-0.051\n", " Fth1 CE=+0.0141 synergy=-0.0141 bias_partner=-0.040\n", " Rpl4 CE=+0.0617 synergy=-0.0617 bias_partner=+0.018\n", " mye_top:\n", " Ctsg CE=-0.0244 synergy=+0.0244 bias_partner=+0.164\n", " Ly6e CE=+0.0066 synergy=-0.0066 bias_partner=+0.058\n", " H2afy CE=+0.0390 synergy=-0.0390 bias_partner=+0.082\n", " Apoe CE=+0.0648 synergy=-0.0648 bias_partner=-0.010\n", " all_W:\n", " Mt2 CE=+0.0400 synergy=-0.0400 bias_partner=-0.040\n", " Vamp5 CE=+0.0421 synergy=-0.0421 bias_partner=-0.014\n", " Rpl32 CE=+0.0509 synergy=-0.0509 bias_partner=-0.005\n", " Ybx1 CE=+0.0591 synergy=-0.0591 bias_partner=+0.002\n", "\n", "Anchor Stat3 — W^c candidates by criterion:\n", " hit_count:\n", " Klf4 CE=+0.0024 synergy=+0.0024 bias_partner=+0.002\n", " Alas1 CE=-0.0007 synergy=-0.0007 bias_partner=+0.041\n", " Gstm1 CE=-0.0034 synergy=-0.0034 bias_partner=+0.052\n", " Elane CE=-0.0764 synergy=-0.0764 bias_partner=+0.250\n", " ery_top:\n", " Hoxa5 CE=+0.0022 synergy=+0.0022 bias_partner=-0.002\n", " Batf3 CE=+0.0017 synergy=+0.0017 bias_partner=-0.003\n", " Mycn CE=-0.0045 synergy=-0.0045 bias_partner=+0.001\n", " Fli1 CE=-0.0446 synergy=-0.0446 bias_partner=+0.018\n", " mye_top:\n", " Pgd CE=+0.0104 synergy=+0.0104 bias_partner=+0.005\n", " Mlec CE=+0.0050 synergy=+0.0050 bias_partner=+0.012\n", " Bhlha15 CE=-0.0003 synergy=-0.0003 bias_partner=-0.001\n", " Calr CE=-0.0509 synergy=-0.0509 bias_partner=+0.103\n", " all_W:\n", " Klf1 CE=+0.0185 synergy=+0.0185 bias_partner=-0.181\n", " Gata1 CE=+0.0020 synergy=+0.0020 bias_partner=-0.264\n", " Chd4 CE=-0.0012 synergy=-0.0012 bias_partner=-0.006\n", " Rpl23 CE=-0.0315 synergy=-0.0315 bias_partner=-0.006\n" ] } ], "source": [ "\n", "# ── H3: Recipe double KO plot — 4+4+4+4 diversified pools ─────────────────────────\n", "#\n", "# Criterion color coding on bar edge: each W^c criterion gets a distinct border color.\n", "# Fill: green = synergistic, red = antagonistic, orange = near-additive (|synergy_score| < 0.005).\n", "\n", "import matplotlib.patches as mpatches\n", "import matplotlib.lines as mlines\n", "\n", "CRIT_COLORS = {\n", " 'hit_count': '#8E44AD', # purple\n", " 'ery_top': '#E74C3C', # red\n", " 'mye_top': '#2980B9', # blue\n", " 'all_W': '#27AE60', # green\n", " '': 'none', # Pareto (no border highlight)\n", "}\n", "CRIT_LABELS = {\n", " 'hit_count': 'W^c hit-count (lineage stability)',\n", " 'ery_top': 'W^c erythroid-top',\n", " 'mye_top': 'W^c myeloid-top',\n", " 'all_W': 'W^c all-cluster',\n", "}\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(20, 14), sharey=False)\n", "\n", "for ax_idx, anchor in enumerate(['Gata1', 'Stat3']):\n", " bias_anchor = single_ko_dict.get(anchor, {}).get('lineage_bias', 0)\n", " bias_sign = 1 if bias_anchor > 0 else -1\n", " anchor_dir = 'erythroid' if bias_sign > 0 else 'myeloid'\n", "\n", " sub = recipe_df[recipe_df['anchor'] == anchor].copy()\n", " sub = sub.sort_values('synergy_score', ascending=False).reset_index(drop=True)\n", "\n", " ax = axes[ax_idx]\n", "\n", " # Bar fill color by synergy direction\n", " fill_colors = []\n", " for s in sub['synergy_score']:\n", " if s > 0.005: fill_colors.append('#27AE60') # synergistic\n", " elif s < -0.005: fill_colors.append('#E74C3C') # antagonistic\n", " else: fill_colors.append('#F39C12') # near-additive\n", "\n", " bars = ax.barh(range(len(sub)), sub['cancellation_error'].values,\n", " color=fill_colors, edgecolor='k', linewidth=0.4, height=0.7)\n", "\n", " # Criterion border: thicker colored edge for W^c bars\n", " for yi, (_, row) in enumerate(sub.iterrows()):\n", " crit = row['wc_criterion']\n", " if crit and crit in CRIT_COLORS:\n", " ax.barh(yi, row['cancellation_error'],\n", " color='none', edgecolor=CRIT_COLORS[crit],\n", " linewidth=2.5, height=0.7)\n", "\n", " # Y-axis labels\n", " labels = []\n", " for _, row in sub.iterrows():\n", " crit_tag = f' [{row[\"wc_criterion\"][:3]}]' if row['wc_criterion'] else ''\n", " bias_tag = f' b={row[\"bias_partner\"]:+.2f}'\n", " labels.append(f\"{row['partner']}{crit_tag}{bias_tag}\")\n", " ax.set_yticks(range(len(sub)))\n", " ax.set_yticklabels(labels, fontsize=7.5)\n", "\n", " ax.axvline(0, color='k', lw=1, ls='--')\n", "\n", " xlim = max(abs(sub['cancellation_error'].max()),\n", " abs(sub['cancellation_error'].min())) * 1.25\n", " ax.set_xlim(-xlim, xlim)\n", "\n", " synergy_dir = '+CE = synergistic' if bias_sign > 0 else '-CE = synergistic'\n", " ax.set_title(\n", " f'Anchor: {anchor} KO (bias = {bias_anchor:+.3f}, {anchor_dir}-biasing)\\n'\n", " f'{synergy_dir}', fontsize=11)\n", " ax.set_xlabel('Cancellation error (double KO bias - additive expectation)', fontsize=9)\n", " ax.invert_yaxis()\n", " ax.grid(True, axis='x', alpha=0.3)\n", "\n", "# Legend\n", "legend_elements = [\n", " mpatches.Patch(color='#27AE60', label='Synergistic in anchor direction'),\n", " mpatches.Patch(color='#F39C12', label='Near-additive'),\n", " mpatches.Patch(color='#E74C3C', label='Antagonistic'),\n", "]\n", "for crit, color in CRIT_COLORS.items():\n", " if crit:\n", " legend_elements.append(\n", " mpatches.Patch(facecolor='white', edgecolor=color, linewidth=2,\n", " label=f'W^c {CRIT_LABELS[crit]}'))\n", "legend_elements.append(\n", " mpatches.Patch(facecolor='white', edgecolor='k', linewidth=0.8,\n", " label='Pareto candidate (no W^c color)'))\n", "\n", "fig.legend(handles=legend_elements, loc='lower center', ncol=4, fontsize=8,\n", " bbox_to_anchor=(0.5, -0.04))\n", "\n", "fig.suptitle(\n", " 'Recipe Double KO: Diversified 4+4+4+4 Partner Selection\\n'\n", " 'W^c criteria: hit-count (lineage stability) | ery-top | mye-top | all-cluster\\n'\n", " 'Border color = W^c criterion that selected each partner | b = partner single-KO bias',\n", " fontsize=10)\n", "\n", "plt.tight_layout(rect=[0, 0.08, 1, 1])\n", "import os\n", "plt.savefig(EXT_SAVE_DIR / 'recipe_double_ko.png', dpi=300, bbox_inches='tight')\n", "plt.show()\n", "print(f\"Saved -> {EXT_SAVE_DIR / 'recipe_double_ko.png'}\")\n", "\n", "# Print W^c findings per criterion\n", "print(\"\\n=== W^c-derived findings by criterion ===\")\n", "for anchor in ['Gata1', 'Stat3']:\n", " bias_anchor = single_ko_dict.get(anchor, {}).get('lineage_bias', 0)\n", " bias_sign = 1 if bias_anchor > 0 else -1\n", " sub = recipe_df[recipe_df['anchor'] == anchor].copy()\n", " sub['synergy_score'] = sub['cancellation_error'] * bias_sign\n", " wc_only = sub[sub['is_wc']].sort_values('synergy_score', ascending=False)\n", " print(f\"\\nAnchor {anchor} — W^c candidates by criterion:\")\n", " for crit in ['hit_count', 'ery_top', 'mye_top', 'all_W']:\n", " rows = wc_only[wc_only['wc_criterion'] == crit]\n", " if not rows.empty:\n", " print(f\" {crit}:\")\n", " for _, r in rows.iterrows():\n", " print(f\" {r['partner']:10s} CE={r['cancellation_error']:+.4f} \"\n", " f\"synergy={r['synergy_score']:+.4f} bias_partner={r['bias_partner']:+.3f}\")\n" ] }, { "cell_type": "markdown", "id": "4d338f18", "metadata": {}, "source": [ "## Section I: STAT Family Regulatory Circuit via W^c Inspection\n", "\n", "**Question:** What regulatory relationships does the inferred GRN encode among the full STAT family (Stat1–Stat6) and known interactors (Irf8, Gata1)? Do these match known biology?\n", "\n", "**STAT TFs found in model**: Stat1, Stat3, Stat4, Stat5a, Irf8, Gata1. (Stat2, Stat5b, Stat6 not in the 1999-gene scaffold.)\n", "\n", "**Method:** Extract the N×N sub-matrix of mean W^c weights (averaged across all clusters, erythroid clusters, myeloid clusters) for the genes above. Each element W^c[i,j] represents the mean inferred regulatory weight from gene i (regulator) to gene j (target). Positive = activation, negative = repression.\n", "\n", "**Key literature facts used for concordance check:**\n", "- **Stat3 auto-activation**: Stat3 has positive auto-feedback via JAK-STAT signaling loops (Darnell 1997; multiple reviews). Expected: W^c[Stat3,Stat3] > 0.\n", "- **Stat1 → Stat3 repression**: Stat1 and Stat3 compete for overlapping DNA binding sites (GAS elements); Stat1 signaling shifts lineage fate away from Stat3-driven myeloid programs. Expected: W^c[Stat1,Stat3] < 0.\n", "- **Stat3 → Irf8 repression**: STAT3 activation constitutively suppresses Irf8 mRNA; STAT3 inhibition causes Irf8 to be the most upregulated TF (Gabrilovich/JCI 2013; Swaminathan/PMC 2014). Expected: W^c[Stat3,Irf8] < 0.\n", "- **Irf8 → Stat3 repression**: Irf8 drives monocyte/DC differentiation and antagonizes MDSC-promoting STAT3 activity. Expected: W^c[Irf8,Stat3] < 0.\n", "- **Stat5a → Gata1 activation**: STAT5-induced erythropoiesis is entirely GATA1-dependent; Stat5 and Gata1 co-regulate erythroid genes (Choudhary/Blood 2010). Expected: W^c[Stat5a,Gata1] > 0.\n", "- **Gata1 → Stat5a**: Gata1 regulates Epo receptor pathway including Stat5 axis (Gutierrez/IUBMB 2020). Expected: W^c[Gata1,Stat5a] > 0." ] }, { "cell_type": "code", "execution_count": 106, "id": "7daa757a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "STAT TFs present in model (6): ['Stat1', 'Stat3', 'Stat4', 'Stat5a', 'Irf8', 'Gata1']\n", "\n", "Cluster groups: all=19, ery=7, mye=10\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved -> /Users/bernaljp/Documents/SCHData/checkpoints/perturbation_extended/stat_circuit_heatmap.png\n", "\n", "=== W^c Concordance with Literature ===\n", " Stat3 -> Stat3 | W=+0.0352 (activation ) | expected=activation | MATCH [JAK-STAT auto-feedback]\n", " Stat1 -> Stat3 | W=-0.0084 (repression ) | expected=repression | MATCH [Stat1 competes with Stat3 for DNA binding]\n", " Stat3 -> Irf8 | W=-0.0004 (near-zero ) | expected=repression | WEAK/ABSENT [Stat3 promotes myeloid; Irf8 opposes it]\n", " Irf8 -> Stat3 | W=-0.0044 (repression ) | expected=repression | MATCH [Irf8 represses myeloid-promoting genes]\n", " Stat5a -> Gata1 | W=+0.0028 (activation ) | expected=activation | MATCH [Stat5 cooperates with Gata1 in erythropoiesis]\n", " Gata1 -> Stat5a | W=-0.0000 (near-zero ) | expected=activation | WEAK/ABSENT [Gata1 regulates Epo-Stat5 axis]\n" ] } ], "source": [ "# ── I: STAT family circuit via W^c ────────────────────────────────────────────\n", "stat_candidates = ['Stat1', 'Stat2', 'Stat3', 'Stat4', 'Stat5a', 'Stat5b', 'Stat6', 'Irf8', 'Gata1']\n", "genes_of_interest = [g for g in stat_candidates if g in adata.var_names]\n", "print(f\"STAT TFs present in model ({len(genes_of_interest)}): {genes_of_interest}\")\n", "\n", "gene_idx_map = {g: list(adata.var_names).index(g) for g in genes_of_interest}\n", "g_indices = [gene_idx_map[g] for g in genes_of_interest]\n", "N = len(genes_of_interest)\n", "\n", "# Cluster groupings\n", "ery_clusters = [k for k in varp_keys if any(c in k for c in ['1Ery','2Ery','3Ery','4Ery','5Ery','6Ery','7MEP'])]\n", "mye_clusters = [k for k in varp_keys if any(c in k for c in ['9GMP','10GMP','11DC','12Baso','13Baso','14Mo','15Mo','16Neu','17Neu','18Eos'])]\n", "\n", "def extract_submatrix(cluster_keys):\n", " W_sum = np.zeros((N, N))\n", " for key in cluster_keys:\n", " W_c = np.asarray(adata.varp[key])\n", " sub = W_c[np.ix_(g_indices, g_indices)]\n", " W_sum += sub\n", " return W_sum / len(cluster_keys)\n", "\n", "W_all = extract_submatrix(varp_keys)\n", "W_ery = extract_submatrix(ery_clusters) if ery_clusters else np.zeros((N, N))\n", "W_mye = extract_submatrix(mye_clusters) if mye_clusters else np.zeros((N, N))\n", "\n", "print(f\"\\nCluster groups: all={len(varp_keys)}, ery={len(ery_clusters)}, mye={len(mye_clusters)}\")\n", "\n", "# ── Plot: 1x3 heatmap ─────────────────────────────────────────────────────────\n", "import matplotlib.colors as mcolors\n", "\n", "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", "vmax = max(np.abs(W_all).max(), np.abs(W_ery).max(), np.abs(W_mye).max())\n", "vmax = max(vmax, 0.01) # avoid zero range\n", "\n", "for ax, W, title in zip(axes, [W_all, W_ery, W_mye],\n", " ['All clusters (mean)', 'Erythroid clusters', 'Myeloid clusters']):\n", " im = ax.imshow(W, cmap='RdBu_r', vmin=-vmax, vmax=vmax, aspect='auto')\n", " ax.set_xticks(range(N))\n", " ax.set_xticklabels(genes_of_interest, rotation=45, ha='right', fontsize=8)\n", " ax.set_yticks(range(N))\n", " ax.set_yticklabels(genes_of_interest, fontsize=8)\n", " ax.set_xlabel('Target gene', fontsize=9)\n", " ax.set_ylabel('Regulator', fontsize=9)\n", " ax.set_title(f'W^c: {title}', fontsize=10)\n", " plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04, label='Weight')\n", "\n", " # Annotate non-zero cells\n", " for i in range(N):\n", " for j in range(N):\n", " val = W[i, j]\n", " if abs(val) > vmax * 0.1:\n", " ax.text(j, i, f'{val:.3f}', ha='center', va='center',\n", " fontsize=6, color='white' if abs(val) > vmax * 0.5 else 'black')\n", "\n", "plt.suptitle('Inferred regulatory weights: STAT family + Irf8 + Gata1\\n'\n", " 'Red=activation, Blue=repression | Row=regulator, Col=target', fontsize=11)\n", "plt.tight_layout()\n", "plt.savefig(EXT_SAVE_DIR / 'stat_circuit_heatmap.png', dpi=300, bbox_inches='tight')\n", "plt.show()\n", "print(f\"Saved -> {EXT_SAVE_DIR / 'stat_circuit_heatmap.png'}\")\n", "\n", "# ── Concordance table (W^c vs literature) ────────────────────────────────────\n", "print(\"\\n=== W^c Concordance with Literature ===\")\n", "known_edges = {\n", " ('Stat3', 'Stat3'): ('activation', 'JAK-STAT auto-feedback'),\n", " ('Stat1', 'Stat3'): ('repression', 'Stat1 competes with Stat3 for DNA binding'),\n", " ('Stat3', 'Irf8'): ('repression', 'Stat3 promotes myeloid; Irf8 opposes it'),\n", " ('Irf8', 'Stat3'): ('repression', 'Irf8 represses myeloid-promoting genes'),\n", " ('Stat5a','Gata1'): ('activation', 'Stat5 cooperates with Gata1 in erythropoiesis'),\n", " ('Gata1', 'Stat5a'):('activation', 'Gata1 regulates Epo-Stat5 axis'),\n", "}\n", "\n", "for (reg, tgt), (expected_dir, source) in known_edges.items():\n", " if reg not in gene_idx_map or tgt not in gene_idx_map:\n", " print(f\" {reg:8s} -> {tgt:8s} | MISSING from model\")\n", " continue\n", " ri = genes_of_interest.index(reg)\n", " ti = genes_of_interest.index(tgt)\n", " w_val = W_all[ri, ti]\n", " model_dir = 'activation' if w_val > 0.001 else ('repression' if w_val < -0.001 else 'near-zero')\n", " match = 'MATCH' if model_dir == expected_dir else ('NOVEL/DISAGREE' if model_dir != 'near-zero' else 'WEAK/ABSENT')\n", " print(f\" {reg:8s} -> {tgt:8s} | W={w_val:+.4f} ({model_dir:12s}) | expected={expected_dir:10s} | {match} [{source}]\")" ] }, { "cell_type": "code", "execution_count": 108, "id": "98f7539f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved: /Users/bernaljp/Documents/scHopfieldPaper/paper/figures/figure_abridged7.png\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Saved: /Users/bernaljp/Documents/scHopfieldPaper/paper/figures/figure_abridged8.png\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Saved: /Users/bernaljp/Documents/scHopfieldPaper/paper/figures/figure_abridged9.png\n" ] } ], "source": [ "# ── Step 5: Assemble figures 7, 8, 9 ────────────────────────────────────────\n", "import subprocess, sys, os\n", "\n", "scripts = [\n", " '/Users/bernaljp/Documents/scHopfieldPaper/paper/figures/assemble_abridged_fig7.py',\n", " '/Users/bernaljp/Documents/scHopfieldPaper/paper/figures/assemble_abridged_fig8.py',\n", " '/Users/bernaljp/Documents/scHopfieldPaper/paper/figures/assemble_abridged_fig9.py',\n", "]\n", "\n", "env = {**os.environ, 'MPLBACKEND': 'Agg'} # avoid matplotlib_inline interference\n", "for script in scripts:\n", " result = subprocess.run([sys.executable, script], capture_output=True, text=True, env=env)\n", " if result.returncode == 0:\n", " print(result.stdout.strip())\n", " else:\n", " print(f\"ERROR in {script}:\")\n", " print(result.stderr[-500:])" ] }, { "cell_type": "markdown", "id": "fa75b44b", "metadata": {}, "source": [ "## Section F: Future Work\n", "\n", "The following analyses were identified as important but are deferred due to scope:\n", "\n", "### Perturb-seq Correlation (High priority #2)\n", "Compute Spearman rank correlation between scHopfield lineage bias scores and experimentally\n", "measured differentiation shifts (e.g., Dixit et al. 2016, Norman et al. 2019). Requires\n", "downloading and aligning a Perturb-seq dataset with the hematopoiesis system. Deferred to a\n", "separate notebook.\n", "\n", "### Regularization Robustness (Medium priority #8)\n", "Show that Stat3's high lineage bias ranking is stable across scaffold regularization strengths\n", "∈ {0.01, 0.1, 1.0}. Requires full model retraining (~30–60 min per strength level, ×3). The\n", "current model used `scaffold_regularization = 0.1`. Deferred to future validation experiments.\n", "\n", "---\n", "\n", "## Summary of Key Findings\n", "\n", "| Analysis | Key Result |\n", "|----------|-----------|\n", "| **A. Stat3 OE** | lineage_bias = −0.013 (myeloid-biasing) vs Stat3 KO +0.165 — confirms **bidirectional** control of the erythroid/myeloid axis |\n", "| **B. Time-resolved** | Gata1 KO and Stat3 KO show distinct gene-cluster cascade patterns evolving over 20–100 ODE steps |\n", "| **C. Phase portraits** | ODE trajectories from 1Ery/2Ery/3Ery cells diverge clearly under Gata1 vs Stat3 perturbations in the Gata1-Stat3 expression plane |\n", "| **D. Dose-response** | Both genes show **threshold-like** switches: Gata1 transitions near 0.44× natural max; Stat3 transitions near 0.22–0.44× |\n", "| **E. Bootstrap** | Static rank-based metric confirms Stat3 is consistently myeloid-correlated; note static and flow-based metrics use opposite sign conventions |" ] }, { "cell_type": "markdown", "id": "5c94ac2b", "metadata": {}, "source": [ "---\n", "## Section A (revised): Stat3 KO Perturbation Flow\n", "\n", "Replaces Stat3 OE. KO is the biologically relevant perturbation (consistent with perturb-seq). \n", "Shows where cells would move in embedding space when Stat3 is knocked out — expected: erythroid-biasing (red = aligned with WT erythroid flow)." ] }, { "cell_type": "code", "execution_count": null, "id": "28265d75", "metadata": {}, "outputs": [], "source": [ "# ── Stat3 KO simulation + alignment (checkpoint protected) ───────────────────\n", "STAT3_KO_IP_NPY = EXT_SAVE_DIR / 'stat3_ko_inner_product.npy'\n", "STAT3_KO_FLOW_NPY = EXT_SAVE_DIR / 'stat3_ko_flow_vectors.npy'\n", "STAT3_KO_COORD_NPY = EXT_SAVE_DIR / 'stat3_ko_embedding_coords.npy'\n", "STAT3_KO_GX_NPY = EXT_SAVE_DIR / 'stat3_ko_grid_gx.npy'\n", "STAT3_KO_GY_NPY = EXT_SAVE_DIR / 'stat3_ko_grid_gy.npy'\n", "STAT3_KO_GU_NPY = EXT_SAVE_DIR / 'stat3_ko_grid_gu.npy'\n", "STAT3_KO_GV_NPY = EXT_SAVE_DIR / 'stat3_ko_grid_gv.npy'\n", "\n", "if STAT3_KO_IP_NPY.exists() and STAT3_KO_GX_NPY.exists():\n", " print(\"Loading Stat3 KO from checkpoint...\")\n", " ip_ko = np.load(STAT3_KO_IP_NPY)\n", " flow_ko = np.load(STAT3_KO_FLOW_NPY)\n", " coords = np.load(STAT3_KO_COORD_NPY)\n", " GX = np.load(STAT3_KO_GX_NPY)\n", " GY = np.load(STAT3_KO_GY_NPY)\n", " GU = np.load(STAT3_KO_GU_NPY)\n", " GV = np.load(STAT3_KO_GV_NPY)\n", "else:\n", " print(\"Running Stat3 KO simulation...\")\n", " adata_stat3_ko = simulate_shift_ode(\n", " adata.copy(), {'Stat3': 0.0},\n", " cluster_key=CLUSTER_KEY, dt=5.0, n_steps=100,\n", " use_cluster_specific_GRN=True, n_jobs=-1, device=DEVICE,\n", " )\n", " sch.tl.calculate_flow(\n", " adata_stat3_ko, source='delta', basis=BASIS, method='celloracle',\n", " cluster_key=CLUSTER_KEY, store_key=f'perturbation_flow_{BASIS}', verbose=False,\n", " )\n", " sch.tl.calculate_inner_product(\n", " adata_stat3_ko,\n", " flow_key_1=_WT_VEL_FLOW_KEY,\n", " flow_key_2=f'perturbation_flow_{BASIS}',\n", " store_key='ko_vs_wt_inner_product',\n", " )\n", " aln = sch.tl.compute_perturbation_alignment(adata_stat3_ko, basis=BASIS)\n", " ip_ko = aln['ip']\n", " coords = aln['coords']\n", " GX, GY = aln['GX'], aln['GY']\n", " GU, GV = aln['GU'], aln['GV']\n", " flow_ko = adata_stat3_ko.obsm[f'perturbation_flow_{BASIS}']\n", "\n", " np.save(STAT3_KO_IP_NPY, ip_ko)\n", " np.save(STAT3_KO_FLOW_NPY, flow_ko)\n", " np.save(STAT3_KO_COORD_NPY, coords)\n", " np.save(STAT3_KO_GX_NPY, GX)\n", " np.save(STAT3_KO_GY_NPY, GY)\n", " np.save(STAT3_KO_GU_NPY, GU)\n", " np.save(STAT3_KO_GV_NPY, GV)\n", " print(\"Saved checkpoints.\")\n", "\n", "print(f\"ip_ko : {ip_ko.shape}, range [{ip_ko.min():.3f}, {ip_ko.max():.3f}]\")\n", "print(f\"flow_ko: {flow_ko.shape}\")\n", "print(f\"coords : {coords.shape}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "665df43c", "metadata": {}, "outputs": [], "source": [ "# ── Plot: Stat3 KO perturbation flow ──────────────────────────────────────────\n", "# GX, GY, GU, GV are precomputed by sch.tl.compute_perturbation_alignment in cell above\n", "\n", "fig, ax = plt.subplots(1, 1, figsize=(13, 5))\n", "\n", "# Background: cells colored by inner product\n", "vabs = np.percentile(np.abs(ip_ko), 97)\n", "sc0 = ax.scatter(\n", " coords[:, 0], coords[:, 1],\n", " c=ip_ko, cmap='RdBu_r', vmin=-vabs, vmax=vabs,\n", " s=6, alpha=0.7, linewidths=0, rasterized=True,\n", ")\n", "cbar = plt.colorbar(sc0, ax=ax, shrink=0.8, pad=0.01)\n", "cbar.set_label('Flow alignment\\n(+erythroid / -myeloid)', fontsize=9)\n", "\n", "# Grid arrows: normalize for visibility\n", "speed = np.sqrt(np.where(np.isnan(GU), 0, GU)**2 + np.where(np.isnan(GV), 0, GV)**2)\n", "speed_scale = np.nanpercentile(speed[speed > 0], 90) if (speed > 0).any() else 1.0\n", "GU_norm = GU / (speed_scale + 1e-8)\n", "GV_norm = GV / (speed_scale + 1e-8)\n", "\n", "ax.quiver(GX, GY, GU_norm, GV_norm,\n", " alpha=0.6, scale=25, width=0.003,\n", " headwidth=4, headlength=5, color='k')\n", "\n", "ax.set_title('Stat3 KO -- perturbation flow (inner product vs WT erythroid velocity)',\n", " fontsize=11)\n", "ax.axis('off')\n", "\n", "plt.tight_layout()\n", "plt.savefig(EXT_SAVE_DIR / 'stat3_ko_flow.png', dpi=300, bbox_inches='tight')\n", "plt.show()\n", "print(f\"Saved -> {EXT_SAVE_DIR / 'stat3_ko_flow.png'}\")" ] }, { "cell_type": "markdown", "id": "39133391", "metadata": {}, "source": [ "---\n", "## Section B (revised): Short-time Cascade Timing\n", "\n", "Runs Gata1 KO and Stat3 KO at log-scale time points (t = 0.1 → 20 ODE units, dt=0.1). \n", "Shows which clusters respond first — revealing the cascade order initiated by each perturbation." ] }, { "cell_type": "code", "execution_count": 98, "id": "1ed4150d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Simulating Wild_Type: 100%|███████████████████████████████████████████████████████████████████████████| 20/20 [01:33<00:00, 4.68s/it]\n", "Simulating Gata1_KO: 100%|████████████████████████████████████████████████████████████████████████████| 20/20 [02:44<00:00, 8.22s/it]\n", "Simulating Stat3_KO: 100%|████████████████████████████████████████████████████████████████████████████| 20/20 [02:45<00:00, 8.29s/it]\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "from tqdm import tqdm\n", "import matplotlib.pyplot as plt\n", "\n", "# ── Short-time cascade ODE runs (checkpoint-protected, ~30 min) ───────────────\n", "shorttime_csv = EXT_SAVE_DIR / 'timeseries_linear_cluster.csv' # Updated filename\n", "\n", "# Linear scale from ~0 to 100 (using steps of 5 to avoid dt=0)\n", "time_points = [float(t) for t in range(5, 105, 5)] \n", "n_steps = 100\n", "\n", "rerun = True\n", "\n", "if shorttime_csv.exists() and not rerun:\n", " st_df = pd.read_csv(shorttime_csv)\n", "else:\n", " records = []\n", " # 1. Get the indices of the genes used in the model\n", " genes_used_idx = get_genes_used(adata)\n", " all_gene_names = adata.var_names.values\n", " \n", " # 2. Identify the indices of the genes to exclude (Gata1 and Stat3)\n", " genes_to_exclude = {'Gata1', 'Stat3'}\n", " model_gene_names = all_gene_names[genes_used_idx]\n", " \n", " # Create a mask: True for genes we want to KEEP\n", " # We apply this same mask to Wild Type so the baseline is comparable!\n", " exclude_mask = np.array([g not in genes_to_exclude for g in model_gene_names])\n", "\n", " sorted_times = sorted(time_points)\n", " time_deltas = np.diff([0] + sorted_times)\n", "\n", " # ADDED: Wild Type condition (None)\n", " conditions = [(None, 'Wild_Type'), ('Gata1', 'Gata1_KO'), ('Stat3', 'Stat3_KO')]\n", "\n", " for gene, label in conditions:\n", " curr_adata = adata.copy()\n", " \n", " # Determine the perturbation dict (Empty for WT)\n", " pert_dict = {gene: 0.0} if gene is not None else {}\n", " \n", " total_dX_model = np.zeros((curr_adata.n_obs, len(genes_used_idx)))\n", " \n", " for i, dt_segment in tqdm(enumerate(time_deltas), total=len(time_deltas), desc=f\"Simulating {label}\"):\n", " curr_adata = simulate_shift_ode(\n", " curr_adata, pert_dict,\n", " cluster_key=CLUSTER_KEY, dt=dt_segment, n_steps=n_steps,\n", " use_cluster_specific_GRN=True, device=DEVICE,\n", " )\n", " \n", " # 3. Update the delta\n", " segment_dX = curr_adata.layers['delta_X'][:, genes_used_idx]\n", " total_dX_model += segment_dX \n", " \n", " # Apply exclusion mask to the data we are about to average\n", " filtered_dX = total_dX_model[:, exclude_mask]\n", " \n", " temp_df = pd.DataFrame({\n", " 'cluster': curr_adata.obs[CLUSTER_KEY].values,\n", " 'val': np.abs(filtered_dX).mean(axis=1)\n", " })\n", " \n", " cluster_means = temp_df.groupby('cluster', observed=True)['val'].mean()\n", "\n", " for cluster, mean_val in cluster_means.items():\n", " records.append({\n", " 'perturbation': label,\n", " 't_total': sorted_times[i],\n", " 'cluster': cluster,\n", " 'mean_abs_delta': float(mean_val),\n", " })\n", "\n", " st_df = pd.DataFrame(records)\n", " st_df.to_csv(shorttime_csv, index=False)" ] }, { "cell_type": "code", "execution_count": 99, "id": "a71254af", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved → /Users/bernaljp/Documents/SCHData/checkpoints/perturbation_extended/cascade_timing_linear.png\n" ] } ], "source": [ "# ── Plot: Cascade timing — top clusters vs linear time ───────────────────────\n", "cluster_palette = {\n", " '1Ery': '#C0392B', '2Ery': '#E74C3C', '3Ery': '#EC7063',\n", " '4Ery': '#F1948A', '5Ery': '#F9B7B0', '6Ery': '#FDDEDE',\n", " '7MEP': '#884EA0', '8Mk': '#A569BD',\n", " '9GMP': '#1A5276', '10GMP': '#2471A3', '11DC': '#2980B9',\n", " '12Baso':'#5DADE2', '13Baso':'#85C1E9', '14Mo': '#27AE60',\n", " '15Mo': '#2ECC71', '16Neu': '#52BE80', '17Neu': '#82E0AA',\n", " '18Eos': '#ABEBC6', '19Lymph':'#D5F5E3',\n", "}\n", "\n", "# Expanded to 3 subplots for WT, Gata1, Stat3\n", "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", "fig.suptitle('Cascade timing: cluster response to KO perturbation (linear time scale)', fontsize=14)\n", "\n", "plot_conditions = ['Wild_Type', 'Gata1_KO', 'Stat3_KO']\n", "\n", "for col, pert in enumerate(plot_conditions):\n", " sub = st_df[st_df['perturbation'] == pert]\n", " \n", " # Top-6 clusters by mean_abs_delta at max time (t=100)\n", " max_t = sub['t_total'].max()\n", " top_clusters = (sub[sub['t_total'] == max_t]\n", " .sort_values('mean_abs_delta', ascending=False)\n", " .head(6)['cluster'].tolist())\n", "\n", " ax = axes[col]\n", " for cl in top_clusters:\n", " row = sub[sub['cluster'] == cl].sort_values('t_total')\n", " color = cluster_palette.get(cl, 'gray')\n", " # Changed semilogx to standard plot\n", " ax.plot(row['t_total'], row['mean_abs_delta'],\n", " 'o-', color=color, lw=2, ms=5, label=cl)\n", "\n", " ax.set_xlabel('ODE time', fontsize=10)\n", " if col == 0:\n", " ax.set_ylabel('Mean |ΔX| (model genes, all cells in cluster)', fontsize=9)\n", " \n", " ax.set_title(pert.replace('_', ' '), fontsize=12, fontweight='bold')\n", " ax.legend(fontsize=8, loc='upper left')\n", " ax.grid(True, alpha=0.3)\n", "\n", "plt.tight_layout()\n", "plt.savefig(EXT_SAVE_DIR / 'cascade_timing_linear.png', dpi=300, bbox_inches='tight')\n", "plt.show()\n", "print(f\"Saved → {EXT_SAVE_DIR / 'cascade_timing_linear.png'}\")" ] }, { "cell_type": "markdown", "id": "7dac527b", "metadata": {}, "source": [ "---\n", "## Section C (new): TF KO Lineage Bias Ranking\n", "\n", "Bar chart of lineage bias for all 16 Pareto-selected candidate TFs from the single KO screen. \n", "Directly answers: *which TFs are erythroid drivers (positive = KO shifts toward myeloid) vs myeloid drivers (negative = KO shifts toward erythroid)?*" ] }, { "cell_type": "code", "execution_count": null, "id": "f8d6e0a5", "metadata": {}, "outputs": [ { "data": { "image/png": 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sSEhiLqi17LxxKXAxZ84ct9z1pMnvv//e9bejMrZt29b1RaT+vPRSEE59+qh/o48//tguuOCCeMevcccO4sWmoKMCCZqG+hdSP0baXv0DrLHnx1vnCrTmzJkz4HsJXdQntJ1oG9F6VcDKf3/ylsuZ5jup5UjM9xOzbv3LLgMGDHDbSmx6KpwCgLHrnbNN40zDNS7VjQrQqi5U/aJtVX3fJXb/94KSsddJQtP1vn+m+VDH+eo3SvWE6iz1/aVg1pAhQ+IdZ2LqoJTQ+j3Ttq8gsI4hutmhPr7UZ9qrr77qtkH/gDgAIHVxKwAAcM7RxYg6zI2vw1svmyE5Fw1VqlRxWUn+GTr79+93d/Tjc+GFF9qRI0dcRoEubryXAgW6aBf/z/Xyv0jzD6woKKOMJ92d9zojrlixosuKUYZFZGRknOmr82SVVZ21B5suAlXW7du3B5RfF2gK6OgC/a233nKdS3sdyCtzZ8eOHTEXlPq+AhXLli0LGLeybjwKlmgedHHuTUPZV8pQ8LIv4qPsDy17z5IlS9yFcaVKlQK+p/Wnju+1fBUYUIaTsoyUqeFd9GqeFMRQUFMd5StApY6zZ82aFe+0p0yZ4rKTFPxQ5o7W0b///hsz3966i93hvYJHGm98vECk5sOfApLePKlcCkppOeupjLrIV5BI5VTmiMavgKeCEHoqo7KqtJ4UvEqJCRMmuEDe4MGD7ZZbbnGZb1r+Zwo2ekEwdVjvv/0ogOsFcf2zsbxloGXm38G8lqv2Py1PbUv+41Km4dnmOzHlSGq5tT4UwPQCoaLMTAU8ldnjP1/aTrRfq3z+41OWpYItXr2j5enf8f6KFSvOuE7OVAbtb8q+VL2hTCB1tq/sPmX0JfbpdspOU7B26dKlARlKXkZgbNqnxP/7hw4dCugMX8FNZSOOGDHCBXyUOeUF6L1y+S+7xNRBqUWd5ytYprpddYY6edfxRttFch8eAABIHoJSAIBzji7y9PQvXXTpyW6rVq1yFzffffeda8qjQImXZZQU+p2agemCXxd2ugDTRZ2CRLEvoEXNOXQxpkDSr7/+6i6edYGsi1ddRCeFgmn6rYIlevKbR+91waasAl1s66leKpcukpQB1Ldv32TN69koS0nT1DLWU+W0fKdNm+amq2wvUSBJzW8UOFHTIAXJvCZKChboiYF6yp4yDFR2NQlSNph/UEoZRwoEajlrvvTS8lRwQkG/hOji/8EHH3QX5soaeuaZZ1xmkwIV/pSJpnlRkNBrLqSMEQUFvOCHsnCUCfTOO++4+VRAQAGNhJpQKTtP2Xq6ONb6UKBIARtvvkXBLQWL1DROF+YKIOlJeQoaxEfbi5qBPv30027aWlYKlKnc3bp1c99R0EIXyvpM01VmkLZ9lVOBWi0zzZu2Xa0PbYcKZp6tKdjZaD1rGStDTNun9jnN85meTqjgjuZHwUA1I9NyVbBWGY4KkEiOHDnc/wo+ahvo1KmTHT161DXF03agp8vpKYnKQovdFNJztvlOTDmSWm5tswp26jsKqCpbcdSoUS7jT4EczZea1WodKpCjwKW2AzUt1rag4JG2FwXZVH6vCaaaqXlP3PSvA+JzpjJoH1Aw56uvvnLl1/LRvqKAypnWWewsIgW7te9qXWsampaXXRiblo2yirR9KltN61T1qP/0tB0paKV9T8tB+5uWiXjf07LbvXu3K3di6qDUosC49kM9sdA7vigY7WXsAQBCh+Z7AIBzki6y1JxJTXTU1Eb9i6iPGT1uXU1XkktZP7qwUuaPLjB18acL8fiahSnTSdlCukhSeRS8UnBBwQRlsCSV1zRKGThq6qPMD/Wpo0wfPUJemRW6ONKFm4Jn6o8qOdNJLGUWKSCgi0JdKOqiUpk+CgiK3iuTRU3dvOwNZTgpqKALYS0LBRWUbeA9jl4X/OqvyetzS33O6MJ07NixLiihZaqmPcoKOlNfWbowVECwS5cuLmCoZaWmQbFpvan8ys5QsyFdbCr4qGCeAg0qk5a1ggBalwq4KFig7Kf4xieaprYJ76Jb26HGpwt4zfeVV17pgk/ajhTQUCBOgQItTwXOEqJ+h/R64oknXJaJgnLaHtU8TRRw1bJUNpACDNo2tMy87V1lV2Dz3nvvdUEeZZqoH62UBi0V7NC8tG/f3gUrtO0peKbAirJYtN/FR+XRS8FTBWkUuNByVjNA0balcWr8ChhqG9G2oP1JGVkK2CgrTH8n1A9VYub7bOVIarnVl5a332t9apvS9qdtQNScU/WSMpQ0Pwoqqi5RYErbobI41b+YlzWn/Vn7twKrWp8an4Zpe0nImcqg7VfT0bajulHbiZrYqm5RoC2xFCRTubXuFSxU3ZpQUFW0neuloLKa8Wkd+md/KXCoZapgl9atMsS07r0gtNaX5kVBOzU7VDDsbHVQalFQz9t3tdxUT6i+UbZXfMFMAEDqCVNv56k4fgAAzhm6gFIWjzpK9oJQCjooiKEsgTMFFBA/XWCqvyn/AJMu0pVtpAAWAAAAkBAypQAAGYbujuuuvZqMKGNBWSnqu0R39ZX9gqTT8tPTq5RVpKZ0anqmpo7K8gAAAADOhEwpAECGooCJmsmp7yF1pKumZOrvyOuIGkmjPn7UlEh93qiJpZr4qbmf1yQNAAAASAhBKQAAAAAAAIQcT98DAAAAAABAyBGUAgAAAAAAQMgRlAIAAAAAAEDI8fS9IImOjrbdu3dbzpw5LSwsLK2LAwAAAAAAkCbUffnRo0etSJEi7uFCCSEoFSQKSF111VVpXQwAAAAAAIBzwg8//GDFihVLcDhBqSBRhpS3wHPlypXWxQEAAAAAAGlgy5YtVqpUqUS1otqShO+eT44cOeISd7xYSUIISgWJtwEpIEVQCgAAAACA9On333+3sWPH2ooVKyw8PNyqVKliDz/8sNWsWdPeeecdN0zfyZLlzCGXd2J9d+XKlTZkyBD766+/rHDhwtatWze7/fbb7Xx2tmAbHZ0DAAAAAAAkwqFDh+yuu+6y9evXW8eOHe3GG290wSl9tnfvXps7d66dPHkyUeOaG+u7Dz74oG3atMluvfVWy5Mnjz3zzDP222+/WXpGphQAAAAAAEAibN682TVNu+222+yxxx5zn9WoUcO+/PJLe/zxx23hwoXus4svvtgFnU6fPm1PPPGEy37KnDmza9L27LPP2sSJEwO+++2339obb7zhvhMVFWX//vuvrVq16qzZVue79D135xBtVKdOnUrrYgBJpnRUVYwAAAAAkNFdcMEFVrJkSXv//fdt8eLFLsjUqFEjmzBhgvt73bp1tm3bNuvTp4/lzZvX7rvvPhdc6tSpk23YsMEFry677DJr0KCBffrppzHfzZcvn+XOndtN49prr3WfK/uqdu3alp4RlAoBRVG3bt3qHokInI9tgNXxHn2lAQAAAMjosmbNGtMXlB509vrrr7uXsqXefPNNF7BSQKlnz54uy+mll16y/fv3u99988039t1339mePXusQ4cOcb7ruf/++23evHlufApKKUiVXhGUCkGGlAJSOXLkcB2Vpbce9ZG+KZCqClPbsO4IkDEFAAAAIKNfI+mG/ejRo917dVL+7rvvuiDS5MmT43x/0aJFNnLkSIuMjHQdontxgjNp06aNC0TNnj3bpk2bRlAKyacme9pQFZDKnj17WhcHSDJtuxs3bnTbMkEpAAAAABmZAlDDhw+3p556yvUrpWZ4utZXUGrXrl0x31McQK2mHnroIatVq5ZNmjTJPV3v559/jjNOn89nBw8etDvvvNMuv/xy69evn+tQXXLmzGnpGUGpECFDCucrtl0AAAAA+M/1119v48aNc9lPa9ascU/JmzVrlhum/qV2797t3qsz83vuuccFnNSn1IgRI+zHH390w7z+pnP+f8BJ3+3Vq5dFRES4oNeJEydc/1Re1lR6limtC5ARHd6xzXavXBH0l8Z7Nur1X4+sbN68uVWrVs2910udtKXE22+/bS1btnSvAQMGJPoRmIm1ZcsW9yQD0SMxO3funOxxKXrdo0ePeIdVrlw53s81vfgexfnhhx+6V1LpKQwvvviipZSa1fmncioNVFF1zZ8qMlEF2bZtW7fOtX5GjRplx48fT/G0AQAAACCjKV68uL311ltWp04d17xOQSQFlxRYaty4sd1yyy2WP39+dx2mbKeBAwe6h0d98sknVrNmTTdMT+KTW/y+q36nXnjhBWvYsKFNnz7dNfdT4Et/p2dkSoWYAkeTWzSxqJP/BQyCKXNEVuv85beWu3jJBL+jHcULZnTp0sU+++yzFE/3jz/+cDvYxx9/7NIWH330Ufvggw+sa9euFizbt293galgKFq0qHvUZjDoCQrJocpKr2BSQKp///527NgxGz9+vIuyz5gxwz1q9OWXX7by5cu7QJUi9L1793afAwAAAACSRp2aKzAVn2uuucZ+/fXXgMSHhK4br4n1XdFT/DISglIhdmz//lQJSInGq/GfKSiVED2aUm1iDxw44DplV0aVdrTHHnvMNd9au3atHT582O69916XdeNP6YpPPvmk+52o8zYFkWJbsWKFDRs2zAVN9KjLQYMGWcWKFV0Wksaxfv16l8mjR2h6WUR62oDKpPa5GqfK2KJFCxdF1hMKNm/ebAULFnRBFz1Cs379+q7cO3fudB3CKYPr888/d30hKcL8yCOP2I4dO1xATuNUcE6fKQp9ySWXnHEZKeimgE50dLTLBlPbYU1X9AjP9957zwWBvCyk5557zi688EKX2qkIup6mUL16dRsyZIgL4i1cuNCNT5lOylZT22LNq5alHimqjC4F+Pbt2+eeyqC/NT09CS+hgNTp06fdsvOe3KAnPWiZKyAleuKDMs40zSVLlrjPH374Ydd+WZQy2qRJkyRvPwAAAAAAJBXN9+AoMHPrrbfaF1984QIuDzzwQEwTPAWDpkyZ4jpmU/qgf+dtUq5cOatXr557v3fvXtcUMHYWkNrMKhiipmOffvqpG7+m6alUqZJ9/fXX1r17d/d0Aq9TNwV52rdv74JRam6ogI7osZkqp9Ic8+bNa19++aX7XEEddQ6n+VDE+dtvv3Wpj5rmpk2b3Hz4e+aZZ1xASBljl1566RmXkbLANB4tAwWA/JsoqgO7OXPmuKctzJw505o2bWofffSRC+Qpa0zzoXIowKegWGwK0inopWXy/PPPx2S1KXKueVEAbvXq1fGWywtIaRncd999MQEpBbO0nGIH25Q6qo72lOGmcStyryCZnh6hZQ8AAAAAQCgQlIIdPXrUBWyaNWvm/lY7VwV6/vnnH/e3MqMUyChWrJhrN6tHXsbHaxLYoUMHl7EUOxNLWU0KmigIpODSnj17YgI7XkBIgR9l6nz11Vf2559/umCNgl6xKRurbNmy7r2ykZQ55VHARRSUUuaVxqlAjYJbCxYsCBiPspX0HWndurWbz4S0a9cuZtoFChSIWT6iR4IqmKQg2dixY10WlrKvVH5lg910000uY+r22293bZBjU4d43rgVWJOffvrJ/c5bJwn1d6WsMJVbgSn1JxW7P6/4Hjeq7yhApvWkwJSa8ylzShlfAAAAAACEAkEpuKcBxPeZmoKJl3kjarqmpnCx6WkCaifbsWNH18QvNv2udOnSLiNJL2UOTZ061fV7JNmyZYv5roJHCpTo5QWCYvMvk4Ir/vOgIJQ3zdi8eYpv/jWeTJn+2yXUUbjXCbyXGeY/3/qNfxmU/aRgnDK8rrzyShfI88arDDN1bqfyKBNMgbDY1KzOK4NH04tvHmIrUqSIa6J3xx13uKaMyuQSBc7KlCnjgk2xA1LqWE9NCRUEU9NCdYKuLCkFweLbHgAAAAAACDaCUnBZPgoYKTtJli1b5h5jqQwkUbM6BUfUFGz58uVWu3btgN+rmdhdd93l+kJK6Kl4FSpUcP0Wec3DvCZp8VFzMwV3fvjhh5jsLQVo4gsonYn6fFJTOvVhpd+q+VzdunUDvnP55Ze7pmveE/G8J9apI3QvgKaO0b0yizK41FzPy9TyPlNGl5oOqvzz5893GUrKHlMG1sUXX2wPPvig69dKjw1NDH1XzQVl5cqVrl8v/6CVR1lS+lyv4cOHu3nWvMhDDz3kPlOmmheQUpaa1q3W4+uvv+46PFdQavDgwW5dqskhAAAAAACpjY7O4ag/IQUlXnnlFRfkUIfaXhaT+oNSBo0CNk8//bTLwPGnR2AqSKMnvuklV199tQuIeDQudcCtjB51BK5O0ceMGZNgeW644Qb3tD09WtPrc0rT6Nu3r3tsZmKoPyZlcKnsCkopAKXmhf59YqmvKvXjpE7R1UG6N734qNxt2rRx2VRqouff1E8BpA8//NAFdzSvGpeCSOqUXJ8pc0oZXCVKlHDv1f/U2aizeXU0r6CZgoaFCxcOyCiLjwJoWo/qv0sBNa886jBdzQm1HLRc1JRQtGzU5K9Vq1Yu80vN99TpPAAAAAAAqS3MR1udoFDARJknaiqlzCP/QIayVPSUMwUUDu/YZpNbNEmVJ/Bljshqnb/8NllP30uIgiLqxDyhZnTBps1RQTBlXimz6Gydj6dnepqf+vBSEzsF0m677TYXzPKaGIZK7G0YAAAAAPA/l9W+1HbG8wT65CpWooQtWLLU0mOMJDYypUJMASMFjo75dcwdLNnz5w9qQCotqPNzZfcoIykjB6REzQPVebma5an5op4UGOqAFAAAAADgzBSQ6lswR9DG91wQA1znOoJSaUCBo/MleDRixIiQTk+ddi9evDik0zxXNWrUyL0AAAAAAEiPSLsAAAAAAABAyBGUAgAAAAAAQMgRlAIAAAAAAEDIEZQCAAAAAABAyNHReRrYtme/7TscGfTxFsidw0oWzh/08QIAAAAAAAQbQak0CEg16jPGTpw6HfRxZw3PYj++3O+MgaknnnjCVqxYYadOnbLNmzdbxYoV3ec333yz3Xbbbcme9ttvv23Tp09376tXr25PP/20RUREWGq79tprbdKkSVaqVKl4h//55582ZcoUe/bZZwM+37p1q3Xp0sXmzZuX6HG++OKLVq1aNWvcuHGSyvjhhx+6/zt16mQp8dtvv9m4ceNs8uTJ7u9jx45Zz549rWTJkjZ06FDLlCmTvf/++/bBBx9YWFiYe7Vq1cq6d+9umTNnTtG0AQAAAAAINoJSIaYMqdQISInGq/GfKSjlBWe8oMxnn32W4un+8ccf9sknn9jHH39s2bNnt0cffdQFRrp27WppTQEyvYLhgQceSNbvUhqMio8XkKpQoYI99dRTLgA1fvx4W7BggQsQFilSxI4cOWL9+/d3AcIhQ4YEvQwAAAAAAKQEQSk4GzZscMGNAwcOWI4cOVxGVY0aNeyxxx5zAY+1a9fa4cOH7d5777W2bdsG/DZPnjz25JNPut9JlSpVbPv27fFmIDVr1sx++OEH8/l8LsijzJ6NGzfaI4884oZfc801NmfOHMubN6/t2bPHbrnlFvv222/tl19+sRdeeMGioqKsaNGiLtCi/z3R0dE2bNgw9z1lDLVu3druvvvugOyilStXuvnyyngmCvCsXr3awsPDXUBH39eyqFevnrVr186ef/55N61Dhw65smoahQoVcuX6/fffXWaS5qdPnz728ssvu3Hq/WWXXeaylxYvXuyy1YYPH+6yr9avX+/Gf+LECatcubIr9/z5888YkKpatar7jRw/ftzeeOMN++KLL1xASnLlyuWWyZVXXunW28mTJ12QSv+rfAMGDLBLL700iVsKAAAAAADBQUfncBQUuvXWW11QQ8EKBYwUvBAFmNQETk3aRo4cabt27Qr4bbly5VywRvbu3esCTQk1cVPgZubMmS7g9c4779ibb75po0ePtokTJ7qgVpMmTWzWrFnuu59//rkL4ChQ5n3n008/tebNm8dpjqcmctu2bXO/mTp1qgtsff/99wHfUUCmb9++bhwJNffzlC5d2n3vvvvuiwn8eDZt2mTr1q1zy2T27NlWvnx5N08K3Km5oMqgYQq2KYDkb9++fVa7dm2XWda+fXt77bXXYpb/Pffc436rYFbsZRw7IKXstB49esR8rvIoS03l9qeAmZpoqlxaLy1atHDT7tevny1ZsuSMywAAAAAAgNREUAp29OhRF2hRFpPUrFnTBTP++ecf97cyo5QxVKxYMatTp47LBIqP1ySwQ4cOVr9+/Xi/c/XVV7v/S5QoYXXr1rUsWbK498o4kptuuimmSeGMGTNcVtLy5ctdYOyOO+6wG2+80WUEqbz+lFmkII/Gp+CMgllqyuYfDFKgp1GjRu5vjfdMNC656qqr3LS98knZsmXt8ccft2nTptmIESPc8oiMjLQyZcq4QJ765nr33XftoYcecmVJaBko+0oBN7207BSQE60HZZ/FR8ElrQP1AaYgmzLOPMoii48yspTtpnlXBtjDDz/sloXWFQAAAAAAaYXmewgIbPh/dvr0f31fKdDj30wuvk6zV61a5ZrL6dW5c+cEp6Xglsd/vJ5atWq5ANA333zjAjMKACkLSNlFEyZMcN9R4Mc/SOSVK6Hyi4Iy/vPpP20FujxeQCz2PPqXWx3FK+B055132vXXX++aC2rcyvRSIE0Bsp9++sk6duwY0ym5v6xZs8aUyZtWfOsgPgoYqhmgloECZ+o/qlu3blapUiUXfFIzQK/zei8Yt2XLFtdEsHjx4i4LTRlk+l+ZYPo9AAAAAABpgUwpuL6H1Ozrq6++cn8vW7bMdu/ebRdeeKH7++uvv3ZBHzWPU9aSAkT+FPi46667XL9SZwpIJZaCLc8880xMttIll1ziyqR+r0RN/gYNGhTwmwYNGrin/ykQpSZuaoaoTCxP/vz53VPq1D+VfPnllwGBKO/lUXM8UXBMnYn7ZzwtWrTITU/NHRUM+vnnn12WkvqJUpM6DVMWk4JDXpnPJHfu3K4JpFc2PREwdtDN4z3RUP+rSaP6q1L2lMrXq1cvl8GldSfq6FxNMdW/lgJSWmZz5851WWLqP0x9bAEAAAAAkFbIlIKjAMfgwYPtlVdecVlBCnZ4ARBl4KhZnTrhVkfeBQoUCPitmqopAKKmYXp5TdSUTZQc6vdIHYffcMMN7u/ChQu7Drs1PgV/ChYsaKNGjQr4jTpEVx9Obdq0ceVt2bKl+72ylvznUUEajVsZR2ei5oHKoFL2kzoj96c+rXr37u2aCGpZqRmespEUrFMAS9POli2bXXTRRa6T8b/++uus86y+utQJu8qmDswTar7nT9NVn1deP1kKiOXLl8/9r+WkQKLKouw1UUaVgmXqW0rZXVrfAAAAAACklTBfYtsN4YwUlFFQQp1HK/PIo6eiKVtGnWErULFtz35r1GeMnTj1v6ZlwZI1PIv9+HI/K1k4f9DG6f/EuVBQIEUZT+rIW9lSGYWCeVrGymjSU/8UoNJyOBfE3oYBAAAAAP9Tvngx61vwv6fRB8NzeyNtw46dlh5jJLGRKRViChgpcLTvcGTQx10gd46gBqTSgjKQlHWkJnoZiZoWKsPJ66g99tMFAQAAAABIbwhKpQEFjs6X4JGeLhdKaj6YEanZoV4AAAAAAGQUdHQOAAAAAACAkCMoBQAAAAAAgJAjKAUAAAAAAICQIygFAAAAAACAkKOj8zQQtXuXRR86EPTxZsqTzzIXKRr08QIAAAAAAAQbQak0CEjt6Xmr2amTwR95eIQVnvDBWQNTkZGR9vzzz9v3339v2bNnt2zZslmPHj3suuuuc8PnzZtnmzZtsjvvvNNefvll91mfPn0srX300UeWM2dOa9mypT322GNWr149a9euXbLG9eGHH7r/O3XqFPD5J598YgsXLozz1MGtW7daly5d3LKJTctu6NChVrRo0gKCTzzxhHXs2NGqV69uKRF7HWndde3a1e677z676aabzrq+AQAAAABICwSlQsxlSKVGQEpOnXTjP1tQqlevXlaxYkX78ssvLSIiwgUx7r77bjtx4oQL+Pz11192Lvr9999dICoYYgejUuKNN95I1u+effZZCzYvmNi3b19r1apVotY3AAAAAABpgaBUBqMsoM2bN9vbb79tYWFh7rOyZcva448/7oIkF1xwgU2ZMsV9XqxYMff/n3/+6TJ6du/ebY0aNbKnn37aff7mm2/aF198YdHR0Va3bl0bMGCA7dy507p3724FCxZ043///fcDpp+Y32TKlMnuuusuu+qqq9xvFFy57bbbXJbSr7/+6r4n8+fPdxlP//77r3Xo0MEFX5Tp9Omnn9qBAwfsiiuucONVRtL27dstS5Ys9tBDD9mVV14ZkF00Y8YMe/XVVy1XrlxWsmRJy5EjR7zLTkGcBx980P755x8rXbq0DRs2zPLmzWvXXnutTZo0yfLly+eW465du9yyuvTSS23MmDG2f/9+e/jhh+3gwYNuPCpnkyZNrHPnzta7d2/3maafO3duW79+vVvuzz33nBvfnDlzXJaTsptq1apl69ats8mTJ8dbPgWbunXrZv3797frr78+UeuboBQAAAAAIK3Q0XkGowCTmot5AQpPnTp1XFBDTdAUgNJLgR7Zs2ePvfPOOzZr1iwXGFJg5KeffrJly5bZtGnT7LPPPrOTJ0/GBLM2btxow4cPjxOQSuxv1ORMgSKvvGqyp/Io+HP//ffHBKuOHTvmmvRpfK+99podOXLEfa4AlIJTCs4888wzLvilQNhLL73kgjEKYnkUQBo9erS99957blxHjx5NcNnt3bvXbr/9dvv8889dYGf8+PEBw9U87sILL3TjUTBJZVfWmaZduXJlVyZNa9GiRXHGvXz5cheg0zLOmjWr+82+fftsyJAhNnHiRBdo8/l8CZZN607NCxWwa9q0aaLXt4J3AAAAAACkBYJSGVBUVFScz06dOuX+jx28EGUWKVNHLwVjlPnz888/2x9//GHt27e3Nm3auEDL33//7b5foEAB973YEvsbZflomIJMCsbo+/FRtpGyqhSI0e+9TKRq1apZeHi4e6/Mqptvvtm9V3bTJZdc4gJA/k0ClYFUuHBhl0nlNXmLj8qnYI60bt3aZSH5U9aRlpUCeAqGaTmpP6f69eu7IJOyopYsWRJv/1zKUCtRooR7X7VqVTcvixcvtpo1a7rsLbnlllsSLJuCYIMGDXIZaLGbEyZ1fQMAAAAAEAo038tgFJRR8y9lKal/Ic/SpUtd0EXN0WJTsMajIIYydhToUGfa6r9IDh8+7IYp80adaccnsb9R8Ktx48b21Vdfuewj9Y8Un8yZM8cpl/iPK3Z2kf4+ffp0vL8TL5ilDKOBAwfGBLnuvfdeFwBLaLmIlqvKrKyuyy+/3NauXevGXaVKFZs9e7Zrbvjdd9/ZW2+95b7nT9lRscuk6SnIlBh33HGHyyQrX768y3BT31sKaCVnfQMAAAAAEApkSmUwyvRREzM9LU6BClEfSXranJrGecEe/8BNfBo0aOCa4Km5m4JN6qtp+vTpQfuNmvCp3ycFV9TXk1eu+LJ+zjbNjz/+2L3fsmWLy1RSsMZTu3Zt16Rwx44dLgDkBYvU5E1l1cvrkFxNDFesWOHeq8mgAk+xM8HUgbqyqBRYWr16tRvn66+/7prgNW/e3AYPHuya5Skgdzbqk0rBMTVHFK9JY3y8gJOCUuq/SoE8TSMx6xsAAAAAgLRAplQGpGCP+ldSUzVl+ygzSUGMG264wQ1Xc7NHHnnE8ufPn+A4lJWzZs0a1zROgSIFj9QZuTotD8ZvFBRS9lC7du1iPlPH5eqTSX1MJZY6OX/qqadccEnUrE79ZnkKFSpkTz75pMveUgfnlSpVSnBcZcqUcX1XKTil5nYKqsXOVlLQSZ2Ka1wKKikQpgBbv379Ypa3mu/lyZPnrGVXk0R1Kn/PPfe4DC4FnLSuzkaBMWVkab5eeOGFs65vAAAAAADSQpjvTL0nI9HU/5GybpSJ42X2yPHjx23Dhg0xAYWo3btsT89bzU79l7USVOERVnjCB5a5yP+CLucjbZLK5nnggQdcX0wZtd8j9Sulpn4KYimY9O6777oAnjpwD6XY2zAAAAAA4H/KFy9mfQvG/xT35Hhub6Rt2JFwwsf5HCOJjUypEFPASIGj6EPBf+pZpjz5zvuAlCj4ouZuY8aMybABKVE2lQJCag6ooJT6gFIzPAAAAAAA0gOCUmlAgaP0EDxKLeoMXa+MTgG5AQMGuBcAAAAAAOkNHZ0DAAAAAAAg5AhKAQAAAAAAIOQISgEAAAAAACDk6FMqDew8fMoOHI8K+njzZctsxXKHB328AAAAAAAAwUZQKg0CUu0/2mwno3xBH3dE5jCbfkuZswamIiMj7fnnn7fvv//esmfPbtmyZbMePXrYdddd54bPmzfPNm3aZHfeeae9/PLL7rM+ffrYuei3336zcePG2eTJkxP8zosvvmjVqlWzxo0bB3ye0LydaZw33nijffbZZ0kup5avnpxXtGjKOrh/7LHHrF69etauXTv39/Lly+2+++6zYcOG2ZVXXmn79u2zUaNG2eLFiy1HjhyWN29eN3/6DQAAAAAA5xKCUiGmDKnUCEiJxqvxny0o1atXL6tYsaJ9+eWXFhER4QJQd999t504ccJatmxpf/31l6UnDzzwQNDGlZyAlLzxxhsWbApI9e7d20aPHm2XXXaZW39dunSx1q1buyBVpkyZ7M8//3TrW0HIOnXqBL0MAAAAAAAkF0GpDGbhwoW2efNme/vtty0sLMx9VrZsWXv88cft2WeftQsuuMCmTJniPi9WrJj7X4GNjh072u7du61Ro0b29NNPu8/ffPNN++KLLyw6Otrq1q1rAwYMsJ07d1r37t2tYMGCbvzvv/9+QAbS+PHjLUuWLLZx40Y3LmUOffvtt24cr7/+uv38888ug0vZTd40Dhw4YA8++KCNGTPGfv31Vzt9+rTdcMMNLkPI34YNG+ypp55y31eW0BNPPGE1atQIyC6aOHGiffzxx5Y/f37LkyePGx6f/fv321133eXmR98ZPHiwC+BVrlzZ1qxZY7t27XLL7PDhw265NGvWzPr37+8CfPr/5MmTljlzZrdMLr30Urv22mtt0qRJbvnPnz/fjhw5Ylu2bLGqVau6zCaN+4MPPrB3333XlV2ZXadOnbIRI0YkGJBSsM0/2DRr1izLly+fCzB6qlevbj179nTLXescAAAAAIBzBR2dZzAKMClQ4QWkPApsKKCiIJECUHp16NDBDduzZ4+98847Luihpn3r1q2zn376yZYtW2bTpk1z2UMKwnjBLAWchg8fHhCQ8p++mrEpS+vzzz+3AgUK2CeffOKCM1999ZULNi1atMgOHTrkvj9jxgxr3769m46CNPquXpr2d999FzDuRx55xG699VYXKFMwSEEblct/2lOnTnW/1/wo4JQQBYwGDhzoxqXmjh9++GHA8JkzZ7qyKsCl9/pfTec0zy1atHDT6Nevny1ZsiTOuH///XcXTNLyVIBQy1KBrrfeess++ugjV8Z///03wbIpIKXA3yWXXBKQ/aT5iy/IpoChfgMAAAAAwLmEoFQGFBUVt5N1BXwkdrBK1FeR+p3SS1lVyiJSRtMff/zhAkZt2rRxgaS///7bfV+BJn0vPso0KlGihOvLStlKanYm+kyBKH3epEkTF6BSkCV37txWrly5mAwqTeumm25yAbS1a9fGjPfo0aPuM2UsSc2aNV1/Sv/880/Md5SldPXVV1uuXLncdBRUSkjt2rXddLU8WrVq5X7rT0Gh0qVLu0wuBdkU/Dp27JjL/lJW0sMPP+yyqdScLjZlTmm+lEmlzLSDBw/aL7/84vq8UqaTMsm0XBPy9ddfuz6vtHwU3EvMuo1vvQIAAAAAkJZovpfBKLtGHXgriKImY56lS5e6QJICObEpSOJRcMPn87ngR9euXV1n6KJmbBqmpnMK+CQkPDywvysFZmJTQEb9JK1fvz6mQ29N79FHH7WmTZu6vxUYU5BMgTFRmWLTZ2rqF7vs/mVRP0xz5861l156yX2mZnYNGjRw/TH5j8d/GYia1SkIpv6bFERTUEnfU1BKGVAKoOn/Tz/9NE6zuaxZs8Ypk6anJoyJ8dBDD7kyjhw50jVhVHaUAmT6X00AY9O6TaiZIgAAAAAAaYVMqQxGzb0uvPDCmOweUTaRgiz3339/TKDIP5gTHwVF1GxPGUoKGClQMn369KAFzpQ19cMPP8RkPml6aiKnrB9lJCkgpuwpj7KfFJhRhpWoeZ/6etK8epSVpeaHGrfmXX1ZiTKUNC96eZ2iq4ndtm3bXKBITQgvv/zygDJq2nqinsq3Y8cOlxWl7w4aNMgFuRRMU/9WK1euTNQ8a/wKZKlsGo+aDSaU3eQFE9Us7+abb7a+ffu65dK8eXM3X8rU8gJcarY3YcKEOP1vAQAAAACQ1siUyoBefvlllxmkZmnKAFLGkQIbXnO2+vXru/6Z1LwuIcooUj9ICoooKKWOxG+77bYz9tOUFCqL+nXKmTOn+1t9XCkzqW3bti4Ao36blKGkztM9yq5Sh+SvvPKKy4LSfPpng6nfKmV2qfmfMsKKFy+e4PTVrO7JJ590wSYtD/3G3z333OMyt9RZuporqp8ulbdbt26uo3P1LaXsJ5UnMTQ9Bbk6derkMqlKlSoVkFGVEAUStQ7UR5XKo47Sx44d64JlCi5qHepvnrwHAAAAADjXhPnia/eEJNPT1NQPkTq2VtaO5/jx4+6pcOXLl3fBn52HT1n7jzbbyajgL/aIzGE2/ZYyVix3YBO584k2RwWd9OQ7PXFP/S9lBAq4zZkzxwWmZNiwYa45pQJ9aS32NgwAAAAA+J/yxYtZ34I5gja+5/ZG2oYdwUn4ONdiJBkuU0p9Bj399NPugl8X1Mpk0etMFi9e7LJd1Awr2BQwUuDowPG4HVKnVL5smc/rgJT3pD81Q1OH5hklICXK2lLH7coAU4aV+oDynn4IAAAAAEB6lO6DUqNGjbIVK1a4Zk3bt293wSY96S2hJ6+pSZr6FUpM06nkUuDofA8epZYiRYq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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved → /Users/bernaljp/Documents/SCHData/checkpoints/perturbation_extended/ko_ranking.png\n", "Top 2 erythroid-biasing KOs:\n", " - Stat3 (0.1648)\n", " - Irf8 (0.0676)\n", "\n", "Top 2 myeloid-biasing KOs:\n", " - E2f4 (-0.2743)\n", " - Gata1 (-0.2637)\n" ] } ], "source": [ "# ── TF KO ranking bar chart ───────────────────────────────────────────────────\n", "df_ko = single_ko_bias[['lineage_bias']].sort_values('lineage_bias').reset_index()\n", "df_ko.columns = ['gene', 'lineage_bias']\n", "\n", "# Auto-detect top 2 hits on each side (since it's sorted, head is bottom, tail is top)\n", "top_neg_indices = df_ko.head(2).index.tolist()\n", "top_pos_indices = df_ko.tail(2).index.tolist()\n", "\n", "top_neg_genes = df_ko.loc[top_neg_indices, 'gene'].tolist()\n", "top_pos_genes = df_ko.loc[top_pos_indices, 'gene'].tolist()\n", "\n", "def bar_color(i, bias):\n", " if i in top_pos_indices: return '#922B21' # Dark Red\n", " if i in top_neg_indices: return '#1A5276' # Dark Blue\n", " return '#E74C3C' if bias > 0 else '#3498DB'\n", "\n", "colors = [bar_color(i, v) for i, v in zip(df_ko.index, df_ko['lineage_bias'])]\n", "\n", "fig, ax = plt.subplots(figsize=(12, 5))\n", "ax.bar(range(len(df_ko)), df_ko['lineage_bias'],\n", " color=colors, edgecolor='k', linewidth=0.6)\n", "ax.axhline(0, color='k', lw=1, ls='--')\n", "\n", "# Label the top 2 on both sides\n", "for i, row in df_ko.iterrows():\n", " if i in top_pos_indices or i in top_neg_indices:\n", " ax.text(i, row['lineage_bias'] + 0.005 * np.sign(row['lineage_bias']),\n", " row['gene'], ha='center',\n", " va='bottom' if row['lineage_bias'] > 0 else 'top',\n", " fontsize=9, fontweight='bold')\n", "\n", "ax.set_xticks(range(len(df_ko)))\n", "ax.set_xticklabels(df_ko['gene'], rotation=45, ha='right', fontsize=9)\n", "ax.set_ylabel('Lineage bias of KO\\n(+ = erythroid-biasing, − = myeloid-biasing)', fontsize=10)\n", "ax.set_title('Single-KO lineage bias across Pareto-selected candidate TFs', fontsize=11)\n", "ax.grid(True, axis='y', alpha=0.3)\n", "\n", "# Updated legend mapping to match the bar_color function correctly\n", "from matplotlib.patches import Patch\n", "legend_handles = [\n", " Patch(color='#922B21', label='Top 2 erythroid-biasing KOs'),\n", " Patch(color='#1A5276', label='Top 2 myeloid-biasing KOs'),\n", " Patch(color='#E74C3C', label='Other erythroid-biasing KO'),\n", " Patch(color='#3498DB', label='Other myeloid-biasing KO'),\n", "]\n", "ax.legend(handles=legend_handles, fontsize=8, loc='upper left')\n", "\n", "plt.tight_layout()\n", "plt.savefig(EXT_SAVE_DIR / 'ko_ranking.png', dpi=300, bbox_inches='tight')\n", "plt.show()\n", "\n", "# Print statements formatted for multiple genes\n", "print(f\"Saved → {EXT_SAVE_DIR / 'ko_ranking.png'}\")\n", "print(\"Top 2 erythroid-biasing KOs:\")\n", "for idx, gene in zip(top_pos_indices[::-1], top_pos_genes[::-1]): # Reverse to print highest first\n", " print(f\" - {gene} ({df_ko.loc[idx, 'lineage_bias']:.4f})\")\n", " \n", "print(\"\\nTop 2 myeloid-biasing KOs:\")\n", "for idx, gene in zip(top_neg_indices, top_neg_genes):\n", " print(f\" - {gene} ({df_ko.loc[idx, 'lineage_bias']:.4f})\")" ] }, { "cell_type": "markdown", "id": "9397ea05", "metadata": {}, "source": [ "---\n", "## Section D (new): Cluster-level ΔX at Convergence\n", "\n", "Shows which clusters are most perturbed by Gata1 KO vs Stat3 KO at ODE convergence (t=500). \n", "Directly answers: *where in the lineage does each perturbation act?*" ] }, { "cell_type": "code", "execution_count": 66, "id": "9e5f0a94", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cluster summary at convergence:\n", "cluster 1Ery 2Ery 3Ery 4Ery 5Ery 6Ery 7MEP 8Mk 9GMP 10GMP 11DC 12Baso 13Baso 14Mo 15Mo 16Neu 17Neu 18Eos 19Lymph\n", "perturbation \n", "Gata1_KO 0.377660 0.064562 0.054434 0.056094 0.044744 0.026938 0.020095 0.039025 0.012549 0.010363 0.286095 0.018470 0.009530 0.012869 0.010133 0.010458 0.061029 0.051916 0.128055\n", "Stat3_KO 0.246151 0.009652 0.009543 0.009985 0.009997 0.010808 0.014368 0.042709 0.033182 0.012688 0.419256 0.024301 0.012419 0.018806 0.022313 0.021605 0.106496 0.128148 0.199317\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved → /Users/bernaljp/Documents/SCHData/checkpoints/perturbation_extended/cluster_effects_heatmap.png\n" ] } ], "source": [ "# ── Cluster ΔX heatmap at convergence (from existing timeseries checkpoint) ───\n", "# Use n_steps=100 (t=500) from existing timeseries_cluster_effects.csv\n", "# ts_df already loaded; aggregate mean|ΔX| per (perturbation, cluster) over all genes\n", "ts100 = ts_df[ts_df['n_steps'] == 100]\n", "cluster_summary = (ts100\n", " .groupby(['perturbation', 'cluster'])['mean_abs_delta']\n", " .mean()\n", " .unstack('cluster')\n", " .reindex(columns=CLUSTER_ORDER)\n", " .fillna(0))\n", "\n", "print(\"Cluster summary at convergence:\")\n", "print(cluster_summary.to_string())\n", "\n", "fig, axes = plt.subplots(2, 1, figsize=(14, 5), constrained_layout=True)\n", "fig.suptitle('Cluster-level mean |ΔX| at ODE convergence (t = 500)',\n", " fontsize=12)\n", "\n", "vmax = cluster_summary.values.max()\n", "\n", "for row_idx, pert in enumerate(['Gata1_KO', 'Stat3_KO']):\n", " ax = axes[row_idx]\n", " vals = cluster_summary.loc[pert].values.reshape(1, -1)\n", " im = ax.imshow(vals, aspect='auto', cmap='Reds', vmin=0, vmax=vmax)\n", " ax.set_xticks(range(len(CLUSTER_ORDER)))\n", " ax.set_xticklabels(CLUSTER_ORDER, rotation=90, fontsize=8)\n", " ax.set_yticks([0])\n", " ax.set_yticklabels([pert.replace('_', ' ')], fontsize=10, fontweight='bold')\n", " plt.colorbar(im, ax=ax, fraction=0.015, pad=0.01, label='Mean |ΔX|')\n", "\n", "plt.savefig(EXT_SAVE_DIR / 'cluster_effects_heatmap.png', dpi=300, bbox_inches='tight')\n", "plt.show()\n", "print(f\"Saved → {EXT_SAVE_DIR / 'cluster_effects_heatmap.png'}\")" ] }, { "cell_type": "markdown", "id": "b8910648", "metadata": {}, "source": [ "---\n", "## Figure Assembly\n", "\n", "Compose final multi-panel figures using figurelib and copy to paper figures folder." ] }, { "cell_type": "code", "execution_count": 52, "id": "247ce0df", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "figure_abridged5.png assembled\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "figure_abridged6.png assembled\n", "\n", "Figures saved to: /Users/bernaljp/Documents/scHopfieldPaper/paper/figures\n" ] } ], "source": [ "# ── Assemble composed figures ─────────────────────────────────────────────────\n", "import sys\n", "sys.path.insert(0, '/Users/bernaljp/Documents/scHopfieldPaper/.claude/skills/figure-editor/lib')\n", "from figurelib import Panel\n", "import figurelib\n", "\n", "PAPER_FIGS = Path('/Users/bernaljp/Documents/scHopfieldPaper/paper/figures')\n", "\n", "# Figure 5: Stat3 KO flow (A) + Dose-response (B)\n", "figurelib.assemble(\n", " panels=[\n", " Panel(image=str(EXT_SAVE_DIR / 'stat3_ko_flow.png'), label='A'),\n", " Panel(image=str(EXT_SAVE_DIR / 'dose_response.png'), label='B'),\n", " ],\n", " layout='1+1',\n", " output=str(PAPER_FIGS / 'figure_abridged5.png'),\n", " dpi=300,\n", ")\n", "print(\"figure_abridged5.png assembled\")\n", "\n", "# Figure 6: Cascade timing (A) + TF ranking (B) + Cluster heatmap (C)\n", "figurelib.assemble(\n", " panels=[\n", " Panel(image=str(EXT_SAVE_DIR / 'cascade_timing.png'), label='A'),\n", " Panel(image=str(EXT_SAVE_DIR / 'ko_ranking.png'), label='B'),\n", " Panel(image=str(EXT_SAVE_DIR / 'cluster_effects_heatmap.png'),label='C'),\n", " ],\n", " layout='1+2',\n", " output=str(PAPER_FIGS / 'figure_abridged6.png'),\n", " dpi=300,\n", ")\n", "print(\"figure_abridged6.png assembled\")\n", "print(f\"\\nFigures saved to: {PAPER_FIGS}\")" ] } ], "metadata": { "kernelspec": { "display_name": "SCH-Env", "language": "python", "name": "schopfield" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.14" } }, "nbformat": 4, "nbformat_minor": 5 }