aman
acadbd780c
Full reproducible pipeline: .mcool + ChIP-seq bigwigs → latent embeddings → A/B compartment calls → cross-cell comparison. Key results (chr21, 25 kb, latent dim=32): - Test AUC=0.777, AP=0.759 (converged epoch 31/300) - GM12878 A/B silhouette (cosine) = 0.775 - IMR90 zero-shot silhouette = 0.443 - A-compartment bins stable across cell types (mean cosine Δ=0.042) - B-compartment bins shift substantially (mean cosine Δ=0.451) - 101 B→A and 70 A→B compartment switches GM12878→IMR90
86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
#!/usr/bin/env python3
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"""
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Compares two latent embedding matrices,
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computes similarity metrics (cosine, Euclidean, L1)
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Usage:
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python scripts/compare_embeddings_general.py \
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--emb1 results/emb.npy \
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--emb2 results/emb_eedi.npy \
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--label1 CTRL --label2 EEDi \
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--prefix results/chr21
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"""
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import argparse
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from scipy.spatial.distance import cosine, euclidean, cityblock
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import os
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def compute_metrics(emb1, emb2):
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"""Compute cosine similarity, cosine distance, L2, and L1 per row."""
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cos_sims, cos_dists, l2_dists, l1_dists = [], [], [], []
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for a, b in zip(emb1, emb2):
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cos_sim = 1 - cosine(a, b)
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cos_dist = 1 - cos_sim
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l2 = euclidean(a, b)
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l1 = cityblock(a, b)
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cos_sims.append(cos_sim)
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cos_dists.append(cos_dist)
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l2_dists.append(l2)
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l1_dists.append(l1)
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return np.array(cos_sims), np.array(cos_dists), np.array(l2_dists), np.array(l1_dists)
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def main():
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p = argparse.ArgumentParser(description="Compare two embedding matrices")
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p.add_argument("--emb1", required=True, help="Path to first embedding .npy")
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p.add_argument("--emb2", required=True, help="Path to second embedding .npy")
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p.add_argument("--label1", default="A", help="Label for first embedding")
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p.add_argument("--label2", default="B", help="Label for second embedding")
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p.add_argument("--prefix", default="results/compare", help="Prefix for output files")
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p.add_argument("--no-plot", action="store_true", help="Skip generating the plot")
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args = p.parse_args()
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# Load
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emb1 = np.load(args.emb1)
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emb2 = np.load(args.emb2)
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if emb1.shape != emb2.shape:
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raise ValueError(f"Shape mismatch: {emb1.shape} vs {emb2.shape}")
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prefix_dir = os.path.dirname(os.path.abspath(args.prefix))
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os.makedirs(prefix_dir, exist_ok=True)
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n_bins, n_dim = emb1.shape
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print(f"Loaded embeddings: {n_bins} bins × {n_dim} dims")
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# Compute metrics
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cos_sims, cos_dists, l2_dists, l1_dists = compute_metrics(emb1, emb2)
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df = pd.DataFrame({
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"bin_id": np.arange(n_bins),
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"cosine_similarity": cos_sims,
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"cosine_distance": cos_dists,
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"euclidean": l2_dists,
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"manhattan": l1_dists
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})
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csv_path = f"{args.prefix}_delta.csv"
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df.to_csv(csv_path, index=False)
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print(f"Saved metrics → {csv_path}")
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# Plot
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if not args.no_plot:
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plt.figure(figsize=(12, 4))
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plt.plot(df["bin_id"], df["cosine_distance"], lw=0.8, color="steelblue")
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plt.title(f"Δ-Embedding ({args.label1} vs {args.label2})")
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plt.xlabel("Bin index")
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plt.ylabel("Cosine distance (1 – similarity)")
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plt.tight_layout()
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fig_path = f"{args.prefix}_delta.png"
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plt.savefig(fig_path, dpi=300)
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print(f"Saved plot → {fig_path}")
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if __name__ == "__main__":
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main() |