"""main-text figures for PAPER.tex — cv per-class F1 bars, dahlin heatmap, veres stage stack. Uses shared canonical palette (scripts/figures/palette.py) so the same class gets the same color in every figure. """ from __future__ import annotations from pathlib import Path import warnings, json, sys warnings.filterwarnings("ignore") import numpy as np, pandas as pd import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt from pathlib import Path as _P_root ROOT = _P_root(__file__).resolve().parents[2] ROOT_STR = str(ROOT) FIG = Path(f"{ROOT_STR}/figures") FIG.mkdir(exist_ok=True) # shared canonical palette + style sys.path.insert(0, str(_P_root(__file__).resolve().parent)) from palette import color_for, apply_style, CLASS_COLORS apply_style() # ---- shared style constants (user style spec) ---- SUPTITLE_FS = 15 # figure suptitles TITLE_FS = 14 # panel / axes titles LABEL_FS = 12 # axis labels TICK_FS = 11 # tick labels LEGEND_FS = 10 # legend text LEGEND_TITLE_FS = 11 # legend titles ANNOT_FS = 11 # in-plot annotations CBAR_LABEL_FS = 12 # colorbar labels PANEL_FS = 13 # panel letters (a)/(b)/(c) # override rc defaults from apply_style() so implicit sizes also conform plt.rcParams.update({ "axes.titlesize": TITLE_FS, "axes.labelsize": LABEL_FS, "xtick.labelsize": TICK_FS, "ytick.labelsize": TICK_FS, "legend.fontsize": LEGEND_FS, "legend.title_fontsize": LEGEND_TITLE_FS, "figure.titlesize": SUPTITLE_FS, }) def _panel_letter(ax, letter, x=-0.14, y=1.06, fontsize=PANEL_FS): ax.text(x, y, f"({letter})", transform=ax.transAxes, fontsize=fontsize, fontweight="bold", va="bottom", ha="left") def fig1_perclass_f1(): """per-class F1 bars from 5-fold CV JSONs, one axis per system, class-colored.""" panels = [ ("Pan-skin", f"{ROOT_STR}/discovery/pan_skin/marker/cv_5fold.json"), ("Pan-hematopoiesis", f"{ROOT_STR}/discovery/hematopoiesis/marker/cv_5fold.json"), ("Pan-pancreas", f"{ROOT_STR}/discovery/pancreas/marker/cv_5fold.json"), ] fig, axes = plt.subplots(1, 3, figsize=(18, 6), constrained_layout=True) for i, (ax, (title, path)) in enumerate(zip(axes, panels)): r = json.load(open(path)) rep = r["per_class_report"] # collect (class, f1, support) rows = [(c, rep[c]["f1-score"], int(rep[c]["support"])) for c in rep.keys() if c not in ("accuracy", "macro avg", "weighted avg")] # sort descending by F1 (best first at top) rows.sort(key=lambda t: t[1], reverse=True) classes = [t[0] for t in rows] f1s = [t[1] for t in rows] supports = [t[2] for t in rows] colors = [color_for(c) for c in classes] y = np.arange(len(classes)) bars = ax.barh(y, f1s, color=colors, edgecolor="white", linewidth=0.6) # per-bar n annotations (outside) for b, s in zip(bars, supports): ax.text(b.get_width() + 0.012, b.get_y() + b.get_height() / 2, f"n={s:,}", va="center", fontsize=ANNOT_FS, color="#333") # scale ax.set_xlim(0, 1.22) ax.set_xticks([0.0, 0.25, 0.5, 0.75, 1.0]) ax.set_xticklabels(["0.0", "0.25", "0.50", "0.75", "1.00"], fontsize=TICK_FS) ax.set_xlabel("held-out F1", fontsize=LABEL_FS) # y-axis: shrink font slightly if many classes y_fs = TICK_FS + 1 if len(classes) <= 13 else TICK_FS ax.set_yticks(y) ax.set_yticklabels(classes, fontsize=y_fs) # panel title with n_cells; small acc/AUROC subtitle below n_cells = int(r["n_cells"]) acc = r["mean_acc"]; auc = r["mean_auc"] ax.set_title(f"{title} (n_cells={n_cells:,})", fontsize=TITLE_FS, pad=32) ax.text(0.5, 1.01, f"acc = {acc:.3f} · macro AUROC = {auc:.3f}", transform=ax.transAxes, ha="center", va="bottom", fontsize=ANNOT_FS, color="#555555") # F1=0.9 marker ax.axvline(0.9, color="#888888", linestyle="--", linewidth=1.1, alpha=0.4, zorder=0) ax.grid(axis="x", alpha=0.25, linestyle=":", zorder=0) ax.invert_yaxis() _panel_letter(ax, "abc"[i], x=-0.32, y=1.02, fontsize=PANEL_FS) plt.savefig(FIG / "fig1_perclass_f1.pdf", bbox_inches="tight") plt.close() print(f"[fig1] wrote {FIG}/fig1_perclass_f1.pdf") def fig3_dahlin_heatmap(): """dahlin within-class module-score heatmap (Kit_W41 minus WT).""" p = Path(f"{ROOT_STR}/discovery/hematopoiesis/marker/57_pathway_analysis.csv") if not p.exists(): print(f"[fig3] {p} not found"); return df = pd.read_csv(p) pivot = df.pivot(index="module_name", columns="class", values="delta") pivot_p = df.pivot(index="module_name", columns="class", values="mannu_p_adj_bonferroni") row_order = ["Kit_signaling", "Kit_ligand", "MYC_targets", "Integrated_stress", "Apoptosis_pro", "Apoptosis_anti", "Cell_cycle", "Erythropoiesis_early", "Erythropoiesis_late", "OXPHOS_ETC", "Glycolysis", "Redox_glutathione", "LT_HSC_quiescence"] row_order = [r for r in row_order if r in pivot.index] col_order = ["LT-HSC", "MPP", "erythroid", "myeloid", "megakaryocyte", "lymphoid", "basophil-mast", "monocyte", "macrophage"] col_order = [c for c in col_order if c in pivot.columns] P = pivot.loc[row_order, col_order] Pp = pivot_p.loc[row_order, col_order] fig, ax = plt.subplots(figsize=(11, 8), constrained_layout=True) vmax = np.nanmax(np.abs(P.values)) im = ax.imshow(P.values, cmap="RdBu_r", vmin=-vmax, vmax=vmax, aspect="auto") # cell annotations for i in range(P.shape[0]): for j in range(P.shape[1]): v = P.values[i, j]; pv = Pp.values[i, j] if np.isnan(v): continue star = "**" if pv < 1e-3 else "*" if pv < 0.05 else "" if abs(v) > 0.1: label = f"{v:+.2f}" if star: label = f"{label} {star}" color = "white" if abs(v) >= vmax * 0.6 else "black" ax.text(j, i, label, ha="center", va="center", fontsize=ANNOT_FS, color=color) elif star: ax.text(j, i, star, ha="center", va="center", fontsize=ANNOT_FS, color="black") # col labels (rotated 45) ax.set_xticks(range(len(col_order))) ax.set_xticklabels(col_order, rotation=45, ha="right", fontsize=TICK_FS) # row labels (module names) ax.set_yticks(range(len(row_order))) ax.set_yticklabels(row_order, fontsize=TICK_FS) # color the x tick labels (class names) using canonical palette for tl, cls in zip(ax.get_xticklabels(), col_order): tl.set_color(color_for(cls)) tl.set_fontweight("bold") cbar = plt.colorbar(im, ax=ax, shrink=0.85, pad=0.02) cbar.set_label("Δ module score (Kit_W41 − WT)", fontsize=CBAR_LABEL_FS) cbar.ax.tick_params(labelsize=TICK_FS) ax.set_title("Dahlin: Kit-W41 vs WT within-class pathway module contrast", fontsize=TITLE_FS, pad=14) fig.text(0.5, -0.01, "* p<0.05 ** p<10$^{-3}$ (Mann–Whitney, Bonferroni)", ha="center", fontsize=ANNOT_FS, color="#555555") plt.savefig(FIG / "fig3_dahlin_heatmap.pdf", bbox_inches="tight") plt.close() print(f"[fig3] wrote {FIG}/fig3_dahlin_heatmap.pdf") def fig4_sharon_stage_stack(): """stacked-bar class fractions across veres stages 3-6, class-colored via palette.""" import re p = Path(f"{ROOT_STR}/discovery/pancreas/marker/veres_predictions.csv") if not p.exists(): print(f"[fig4] {p} not found"); return pred = pd.read_csv(p) stg_re = re.compile(r"_S(\d)c_") stages = pred["cell_id"].astype(str).apply( lambda s: int(stg_re.search(s).group(1)) if stg_re.search(s) else np.nan) pred = pred.assign(stage=stages).dropna(subset=["stage"]) pred["stage"] = pred["stage"].astype(int) n_staged = len(pred); n_pre = len(stages); n_drop = n_pre - n_staged print(f"[fig4] staged cells: {n_staged:,} (dropped {n_drop:,} primary-islet cells)") ct = (pred.groupby(["stage", "pred_label"]).size() .unstack("pred_label", fill_value=0)) frac = ct.div(ct.sum(axis=1), axis=0) priority = ["pancreatic-progenitor", "proliferating", "endocrine-progenitor", "endocrine-progenitor-primed", "Fev-EP", "beta_progenitor", "beta", "alpha_progenitor", "alpha", "delta", "gamma", "epsilon", "acinar", "ductal", "exocrine", "mesenchyme", "endothelial", "immune"] present = list(frac.columns) ordered = [c for c in priority if c in present] + \ [c for c in sorted(present, key=lambda x: -frac[x].sum()) if c not in priority] frac = frac[ordered] fig, ax = plt.subplots(figsize=(12, 7), constrained_layout=True) bottom = np.zeros(frac.shape[0]) x = np.arange(frac.shape[0]) for cls in ordered: vals = frac[cls].values ax.bar(x, vals, bottom=bottom, label=cls, color=color_for(cls), edgecolor="white", linewidth=0.6) bottom += vals for xi, s in zip(x, frac.index): n_stage = int(ct.loc[s].sum()) ax.text(xi, 1.03, f"n = {n_stage:,}", ha="center", va="bottom", fontsize=ANNOT_FS, color="#222222") ax.set_xticks(x) ax.set_xticklabels([f"Stage {int(s)}" for s in frac.index], fontsize=TICK_FS) ax.set_xlabel("Veres protocol stage", fontsize=LABEL_FS) ax.set_ylabel("Predicted class fraction", fontsize=LABEL_FS) ax.set_title("Veres 2019 pancreatic differentiation", fontsize=TITLE_FS, pad=12) # legend reversed so it reads top-of-stack first (matches the visual) handles, labels = ax.get_legend_handles_labels() ax.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.02, 1), loc="upper left", fontsize=LEGEND_FS, frameon=False, title="Predicted class", title_fontsize=LEGEND_TITLE_FS, handlelength=1.6, borderaxespad=0.4) ax.set_ylim(0, 1.12) ax.set_yticks([0.0, 0.25, 0.5, 0.75, 1.0]) ax.tick_params(axis="y", labelsize=TICK_FS) plt.savefig(FIG / "fig4_veres_stage_stack.pdf", bbox_inches="tight") plt.close() print(f"[fig4] wrote {FIG}/fig4_veres_stage_stack.pdf " f"({len(ordered)} classes over stages {list(frac.index)})") if __name__ == "__main__": fig1_perclass_f1() fig3_dahlin_heatmap() fig4_sharon_stage_stack() print(f"\nAll figures in {FIG}/")