File size: 2,033 Bytes
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 | #!/usr/bin/env python
"""Sparsity analysis: how does unspliced detection rate affect gamma quality?"""
from _common import *
OUT = output_dir("05_sparsity")
def run(name, loader, _):
print(f"\n{'=' * 60}\n{name.upper()}\n{'=' * 60}")
adata = run_analytical(loader)
_, hl_human = load_halflife_refs()
from scipy.sparse import issparse
u = adata.layers["unspliced"]
if issparse(u):
u = np.asarray(u.todense())
frac_det = (np.asarray(u) > 0).mean(axis=0)
g, h, names = match_halflife(adata, hl_human)
# Get detection rate for matched genes
det = np.array([frac_det[list(adata.var_names).index(n)] for n in names])
quartiles = np.percentile(det, [25, 50, 75])
bins = [
("Q1 (sparse)", det <= quartiles[0]),
("Q2", (det > quartiles[0]) & (det <= quartiles[1])),
("Q3", (det > quartiles[1]) & (det <= quartiles[2])),
("Q4 (dense)", det > quartiles[2]),
]
records = []
for label, mask in bins:
if mask.sum() < 10:
continue
r, _ = stats.spearmanr(g[mask], h[mask])
records.append({"quartile": label, "n": int(mask.sum()),
"r": float(r), "med_det": float(np.median(det[mask]))})
print(f" {label}: r={r:.4f} (n={mask.sum()}, det={np.median(det[mask]):.3f})")
save_json({"dataset": name, "stratified": records}, f"{name}_sparsity", OUT)
fig, ax = plt.subplots(figsize=(7, 4))
ax.bar([r["quartile"] for r in records], [abs(r["r"]) for r in records],
color="steelblue", alpha=0.7)
for i, r in enumerate(records):
ax.text(i, abs(r["r"]) + 0.005, f"n={r['n']}", ha="center", fontsize=8)
ax.set_ylabel("|Spearman r| with half-life")
ax.set_title(f"{name}: Half-life r by unspliced detection rate")
fig.tight_layout()
save_fig(fig, f"{name}_sparsity", OUT)
return records
def main():
set_figure_style()
for name, loader, ck in DATASETS:
run(name, loader, ck)
if __name__ == "__main__":
main()
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