Kenny

.gitignore

@@ -23,3 +23,4 @@ examples/data/
 # run logs
 logs/
 *.log
+*.sh

manuscript/alzheimers_coexpression_null.py

@@ -6,6 +6,12 @@
 Usage:
 	python alzheimers_coexpression_null.py [--out-dir DIR] [--n-shuffles N]
 		[--n-parallel N] [--cell-type TYPE] [--n-cells N] [--balance-groups]
+		[--first-shuffle K --batch-dir DIR]
+
+To add shuffles to an existing run, pass --first-shuffle (one past the last
+saved shuffle) and --batch-dir. The batch's runs, flip_rates and panels are
+written there and the saved outputs are left alone; combine them with
+alzheimers_coexpression_null_merge.py.
 
 Outputs (to DIR):
 - alzheimers_coexpression_null_runs.csv: Real and shuffled run summaries
@@ -80,6 +86,10 @@ def main() -> None:
 	parser.add_argument("--n-shuffles", type=int, default=25)
 	parser.add_argument("--n-parallel", type=int, default=None,
 						help="Shuffles run at once (default: about one per 4 cores)")
+	parser.add_argument("--first-shuffle", type=int, default=1,
+						help="Index of the first shuffle (seeds continue from here)")
+	parser.add_argument("--batch-dir", type=Path, default=None,
+						help="Write only this batch's raw tables here, for merging")
 	add_loading_args(parser)
 	args = parser.parse_args()
 	out_dir = args.out_dir
@@ -95,7 +105,13 @@ def main() -> None:
 
 	print(f"Running co-expression null ({args.n_shuffles} shuffles)...")
 	res = model.coexpression_null(n_shuffles=args.n_shuffles, n_parallel=args.n_parallel,
-								  verbose=10)
+								  verbose=10, first_shuffle=args.first_shuffle)
+	if args.batch_dir is not None:
+		args.batch_dir.mkdir(parents=True, exist_ok=True)
+		for key in ("runs", "flip_rates", "panels"):
+			res[key].to_csv(args.batch_dir / f"alzheimers_coexpression_null_{key}.csv", index=False)
+		print(f"Batch written to {args.batch_dir}")
+		return
 	for key in ("runs", "flip_rates", "panels", "gene_frequency", "pvalues"):
 		res[key].to_csv(out_dir / f"alzheimers_coexpression_null_{key}.csv", index=False)
 	plot_flip_rates(res["flip_rates"], model.flip_rate_percentage,

manuscript/alzheimers_coexpression_null_merge.py

@@ -0,0 +1,93 @@
+"""Add batches of extra shuffles to a saved co-expression null.
+
+Reads the saved outputs of alzheimers_coexpression_null.py in DIR and every
+batch under DIR/null_batches/ (written with --first-shuffle and --batch-dir),
+checks that each batch refit the same observed panel, then rewrites the
+outputs from all shuffles together. Per-gene p-values get a Benjamini-Hochberg
+adjusted column (p_adj) across the observed panel.
+
+Usage:
+	python alzheimers_coexpression_null_merge.py --out-dir DIR
+
+Outputs (to DIR, overwritten):
+- alzheimers_coexpression_null_runs.csv
+- alzheimers_coexpression_null_flip_rates.csv
+- alzheimers_coexpression_null_panels.csv
+- alzheimers_coexpression_null_gene_frequency.csv (with p_adj)
+- alzheimers_coexpression_null_pvalues.csv
+- alzheimers_coexpression_null_flip_rates.png / .pdf
+"""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import pandas as pd
+from scipy.stats import false_discovery_control
+
+import recursieve
+from alzheimers_coexpression_null import plot_flip_rates
+
+PREFIX = "alzheimers_coexpression_null_"
+TABLES = ("runs", "flip_rates", "panels")
+
+
+def read_tables(folder: Path) -> dict:
+	return {key: pd.read_csv(folder / f"{PREFIX}{key}.csv") for key in TABLES}
+
+
+def observed_panel(panels: pd.DataFrame) -> list:
+	obs = panels[panels["run"] == "observed"].sort_values("rank")
+	return obs["gene"].tolist()
+
+
+def main() -> None:
+	plt.rcParams.update({"font.family": "Arial", "font.size": 12,
+						 "pdf.fonttype": 42, "ps.fonttype": 42})
+	parser = argparse.ArgumentParser(description=__doc__)
+	parser.add_argument("--out-dir", type=Path, required=True)
+	parser.add_argument("--flip-threshold", type=float, default=0.01,
+						help="Stop threshold drawn on the flip-rate plot")
+	args = parser.parse_args()
+	out_dir = args.out_dir
+
+	saved = read_tables(out_dir)
+	panel = observed_panel(saved["panels"])
+	batches = sorted((out_dir / "null_batches").glob("*/"))
+	if not batches:
+		raise SystemExit(f"No batches in {out_dir / 'null_batches'}")
+
+	merged = {key: [saved[key]] for key in TABLES}
+	for batch in batches:
+		tables = read_tables(batch)
+		if observed_panel(tables["panels"]) != panel:
+			raise SystemExit(f"{batch.name}: observed panel differs from the saved one")
+		for key in TABLES:
+			df = tables[key]
+			merged[key].append(df[df["run"] != "observed"])
+		print(f"Added {batch.name}")
+
+	# rerunning the merge must not count a shuffle twice
+	runs = pd.concat(merged["runs"], ignore_index=True).drop_duplicates("run", keep="first")
+	flips = pd.concat(merged["flip_rates"], ignore_index=True).drop_duplicates(["run", "iteration"])
+	panels = pd.concat(merged["panels"], ignore_index=True).drop_duplicates(["run", "rank"])
+
+	freq = recursieve.recursieve._null_gene_frequency(panels)
+	freq["p_adj"] = false_discovery_control(freq["p_value"], method="bh")
+	pvalues = recursieve.recursieve._null_pvalues(runs)
+
+	outputs = {"runs": runs, "flip_rates": flips, "panels": panels,
+			   "gene_frequency": freq, "pvalues": pvalues}
+	for key, df in outputs.items():
+		df.to_csv(out_dir / f"{PREFIX}{key}.csv", index=False)
+	plot_flip_rates(flips, args.flip_threshold, out_dir / f"{PREFIX}flip_rates")
+
+	n = runs["run"].ne("observed").sum()
+	print(f"{n} shuffles; {int((freq['p_adj'] < 0.05).sum())}/{len(freq)} genes with p_adj < 0.05")
+	print(pvalues.round(4).to_string(index=False))
+
+
+if __name__ == "__main__":
+	main()