| import itertools | |
| import numpy as np | |
| import pandas as pd | |
| from scipy import stats | |
| from statsmodels.stats.multitest import multipletests | |
| from common import RES, OX, AGENTS, hom, axes, centre, effects, agent_effects | |
| Z = hom() | |
| ag, ox, os_, ref = axes(Z.columns) | |
| Zc = centre(Z) | |
| E_ox, E_os = effects(Zc, ox, os_) | |
| cd = (E_ox - E_os).abs() | |
| A = agent_effects(Zc, ag) | |
| A.round(4).rename_axis("gene").to_csv(RES / "agent_effects.csv") | |
| stress_cols = np.where(ox | os_)[0] | |
| R = Zc.iloc[:, stress_cols].corr(method="spearman") | |
| lab = ag[stress_cols] | |
| pairs = [] | |
| for i, j in itertools.combinations(range(len(stress_cols)), 2): | |
| same = lab[i] == lab[j] | |
| axis_i = "oxidative" if lab[i] in OX else "osmotic" | |
| axis_j = "oxidative" if lab[j] in OX else "osmotic" | |
| kind = "same agent" if same else ("same axis, different agent" if axis_i == axis_j else "different axis") | |
| pairs.append((lab[i], lab[j], kind, axis_i if axis_i == axis_j else "mixed", R.iloc[i, j])) | |
| P = pd.DataFrame(pairs, columns=["agent1", "agent2", "kind", "axis", "rho"]) | |
| P.to_csv(RES / "condition_pair_correlations.csv", index=False) | |
| names = [f"{lab[i]}|{Zc.columns[stress_cols[i]].split(':')[0]}" for i in range(len(stress_cols))] | |
| pd.DataFrame(R.values, index=names, columns=names).round(4).rename_axis("condition").to_csv( | |
| RES / "condition_correlation_matrix.csv") | |
| anova = [] | |
| for axis, members in (("oxidative", ["h2o2", "paraquat", "4nqo"]), ("osmotic", ["nacl", "sorbitol"])): | |
| X = [Zc.loc[:, ag == a].values for a in members] | |
| n = np.stack([np.isfinite(x).sum(axis=1) for x in X], axis=1) | |
| tot = np.stack([np.nansum(x, axis=1) for x in X], axis=1) | |
| ok = (n > 0).all(axis=1) | |
| with np.errstate(invalid="ignore", divide="ignore"): | |
| means = tot / n | |
| grand = tot.sum(axis=1) / n.sum(axis=1) | |
| ssb = np.nansum(n * (means - grand[:, None]) ** 2, axis=1) | |
| ssw = sum(np.nansum((x - m[:, None]) ** 2, axis=1) for x, m in zip(X, means.T)) | |
| dfb, dfw = len(members) - 1, n.sum(axis=1) - len(members) | |
| F = np.where(ok, (ssb / dfb) / (ssw / dfw), np.nan) | |
| eta = np.where(ok, ssb / (ssb + ssw), np.nan) | |
| p = stats.f.sf(F, dfb, dfw) | |
| padj = np.full(len(p), np.nan) | |
| padj[ok] = multipletests(p[ok], method="fdr_bh")[1] | |
| anova.append((axis, len(members), sum(x.shape[1] for x in X), int(ok.sum()), float(np.nanmedian(eta)), | |
| float(np.mean(padj[ok] < 0.05)), float(np.mean(p[ok] < 0.05)))) | |
| if axis == "oxidative": | |
| het = pd.DataFrame({"F": F, "p": p, "p_bh": padj, "eta2_agent": eta}, index=Zc.index) | |
| het.round(5).rename_axis("gene").to_csv(RES / "oxidative_agent_heterogeneity.csv") | |
| anova = pd.DataFrame(anova, columns=["axis", "agents", "conditions", "genes", "median_eta2_agent", "frac_bh_lt_0.05", "frac_p_lt_0.05"]) | |
| anova.to_csv(RES / "agent_heterogeneity_summary.csv", index=False) | |
| std = A / A.std() | |
| S_ox = std[["h2o2", "paraquat", "4nqo"]].mean(axis=1) | |
| S_os = std[["nacl", "sorbitol"]].mean(axis=1) | |
| cd_bal = (S_ox - S_os).abs() | |
| pd.DataFrame({"ox_balanced": S_ox, "osm_balanced": S_os, "cd_balanced": cd_bal}).round(4).rename_axis("gene").to_csv( | |
| RES / "agent_balanced_effects.csv") | |
| loao = [] | |
| for drop in AGENTS: | |
| keep_ox = [a for a in ("h2o2", "paraquat", "4nqo") if a != drop] | |
| keep_os = [a for a in ("nacl", "sorbitol") if a != drop] | |
| d2 = (std[keep_ox].mean(axis=1) - std[keep_os].mean(axis=1)).abs() | |
| keep = ag != drop | |
| a2, b2 = effects(Zc, ox & keep, os_ & keep) | |
| loao.append((drop, stats.spearmanr(cd, (a2 - b2).abs(), nan_policy="omit").statistic, | |
| stats.spearmanr(cd_bal, d2, nan_policy="omit").statistic)) | |
| L = pd.DataFrame(loao, columns=["omitted_agent", "rho_pooled", "rho_agent_balanced"]) | |
| L.to_csv(RES / "leave_one_agent_out.csv", index=False) | |
| pc = {} | |
| for a, b in itertools.product(("h2o2", "paraquat", "4nqo"), ("nacl", "sorbitol")): | |
| pc[f"{a}|{b}"] = (A[a] - A[b]).abs() | |
| PC = pd.DataFrame(pc) | |
| PC.round(4).rename_axis("gene").to_csv(RES / "agent_pair_cd.csv") | |
| top = PC.rank(ascending=False) <= np.ceil(0.1 * len(PC)) | |
| k = top.sum(axis=1) | |
| pd.DataFrame({"n_pairs_top_decile": k}).rename_axis("gene").to_csv(RES / "agent_robust_cd.csv") | |