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4e2940e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | #!/usr/bin/env python
"""Half-life ceiling analysis: is r=-0.40 near the theoretical maximum?
Compares published half-life datasets against EACH OTHER to establish
inter-study agreement. If Herzog and Schofield agree at r=0.5-0.6,
then r=0.40 is 67-80% of the ceiling — near-optimal.
"""
from _common import *
OUT = output_dir("18_halflife_ceiling")
def main():
set_figure_style()
hl_mouse, hl_human = load_halflife_refs()
# ── Inter-study agreement: mouse vs human half-lives ─────────────
print("=" * 60)
print("INTER-STUDY HALF-LIFE AGREEMENT")
print("=" * 60)
hl_m = hl_mouse.set_index("gene_symbol")["half_life_hours"]
hl_h = hl_human.set_index("gene_symbol")["half_life_hours"]
# Case-insensitive match
m_upper = {g.upper(): g for g in hl_m.index if isinstance(g, str)}
h_upper = {g.upper(): g for g in hl_h.index if isinstance(g, str)}
shared = set(m_upper.keys()) & set(h_upper.keys())
m_vals = np.array([hl_m[m_upper[u]] for u in shared], dtype=float)
h_vals = np.array([hl_h[h_upper[u]] for u in shared], dtype=float)
valid = np.isfinite(m_vals) & np.isfinite(h_vals) & (m_vals > 0) & (h_vals > 0)
m_vals, h_vals = m_vals[valid], h_vals[valid]
r_inter, p_inter = stats.spearmanr(m_vals, h_vals)
r_inter_log, p_inter_log = stats.pearsonr(np.log(m_vals), np.log(h_vals))
print(f"\n Herzog (mouse) vs Schofield (human):")
print(f" Shared genes: {len(m_vals)}")
print(f" Spearman r = {r_inter:.4f} (p={p_inter:.2e})")
print(f" Pearson r (log) = {r_inter_log:.4f}")
# ── Ceiling fractions ─────────────────────────────────────────────
print(f"\n Ceiling analysis:")
print(f" Inter-study agreement (ceiling): |r| = {abs(r_inter):.4f}")
methods = {
"scPTR analytical (pancreas, human)": -0.4021,
"scPTR analytical (DG, human)": -0.3812,
"scVelo SS (pancreas, human)": -0.3730,
"scVelo SS (DG, human)": -0.3675,
"velVI (pancreas, human)": -0.2783,
"velVI (DG, human)": -0.3522,
"DeepPTR (pancreas, human)": -0.2767,
"DeepPTR (DG, human)": -0.3577,
}
print(f"\n {'Method':<45} {'|r|':>6} {'% ceiling':>10}")
print(" " + "-" * 65)
for name, r in sorted(methods.items(), key=lambda x: abs(x[1]), reverse=True):
pct = abs(r) / abs(r_inter) * 100
print(f" {name:<45} {abs(r):.4f} {pct:>9.1f}%")
# ── Bootstrap CI on inter-study agreement ─────────────────────────
rng = np.random.RandomState(42)
boot_rs = []
for _ in range(1000):
idx = rng.choice(len(m_vals), len(m_vals), replace=True)
boot_rs.append(stats.spearmanr(m_vals[idx], h_vals[idx]).statistic)
ci_lo, ci_hi = np.percentile(boot_rs, [2.5, 97.5])
print(f"\n Inter-study 95% CI: [{ci_lo:.4f}, {ci_hi:.4f}]")
# ── Split-half reliability of gamma itself ─────────────────────────
print(f"\n--- Split-half reliability of scPTR gamma ---")
for ds_name, loader, ck in DATASETS:
adata = run_analytical(loader)
gamma = adata.layers["gamma"]
rng = np.random.RandomState(0)
n = adata.n_obs
perm = rng.permutation(n)
half1 = perm[:n // 2]
half2 = perm[n // 2:]
med1 = np.median(gamma[half1], axis=0)
med2 = np.median(gamma[half2], axis=0)
valid = (med1 > 0) & (med2 > 0) & np.isfinite(med1) & np.isfinite(med2)
r_split, _ = stats.spearmanr(med1[valid], med2[valid])
# Spearman-Brown correction for full reliability
r_full = 2 * r_split / (1 + r_split)
print(f" {ds_name}: split-half r={r_split:.4f}, Spearman-Brown corrected={r_full:.4f}")
results = {
"inter_study_r": float(r_inter),
"inter_study_r_log": float(r_inter_log),
"inter_study_n": int(len(m_vals)),
"inter_study_ci": [float(ci_lo), float(ci_hi)],
"method_ceiling_pct": {k: abs(v) / abs(r_inter) * 100 for k, v in methods.items()},
}
save_json(results, "halflife_ceiling", OUT)
# Figure
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
# Panel 1: inter-study scatter
axes[0].scatter(np.log10(m_vals), np.log10(h_vals), alpha=0.1, s=5, c="gray")
axes[0].set_xlabel("log10(Herzog mouse half-life)")
axes[0].set_ylabel("log10(Schofield human half-life)")
axes[0].set_title(f"Inter-study agreement (r={r_inter:.3f}, n={len(m_vals)})")
# Panel 2: ceiling fraction bar chart
names_short = [n.split("(")[0].strip() for n in methods]
pcts = [abs(v) / abs(r_inter) * 100 for v in methods.values()]
colors = ["darkorange" if "scPTR" in n else "steelblue" if "scVelo" in n
else "seagreen" if "velVI" in n else "gray" for n in methods]
axes[1].barh(range(len(methods)), pcts, color=colors, alpha=0.7)
axes[1].set_yticks(range(len(methods)))
axes[1].set_yticklabels(list(methods.keys()), fontsize=7)
axes[1].set_xlabel("% of inter-study ceiling")
axes[1].set_title("Method performance relative to ceiling")
axes[1].axvline(100, color="red", ls="--", alpha=0.3, label="Ceiling")
axes[1].legend()
fig.tight_layout()
save_fig(fig, "halflife_ceiling", OUT)
if __name__ == "__main__":
main()
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