Data & ReproStatistics & Data AnalysisFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-hi-c-analysis-hic-differential

Maintainer FreedomIntelligence · Last updated April 1, 2026

Compare Hi-C contact matrices between conditions to identify differential chromatin interactions. Compute log2 fold changes, statistical significance, and visualize differential contact maps. Use when comparing Hi-C contacts between conditions.

OpenClawNanoClawAnalysisReproductionbio-hi-c-analysis-hic-differential🧬 bioinformatics (gptomics bio-* suite)bioinformatics — epigenomics & chromatincompare

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-hi-c-analysis-hic-differential

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

Python: cooltools for expected values, custom differential analysis with scipy.stats.
Compare Hi-C contacts between my conditions" → Compute log2 fold-change contact maps, identify statistically significant differential interactions, and visualize changes in 3D genome organization. Python: cooltools for expected values, custom differential analysis with scipy.stats.
Compare Hi-C contact matrices between conditions.
clr1 = cooler.Cooler('condition1.mcool::resolutions/10000') clr2 = cooler.Cooler('condition2.mcool::resolutions/10000').
print(f'Condition 1: {clr1.info["sum"]:,} contacts') print(f'Condition 2: {clr2.info["sum"]:,} contacts').

Source Doc

Excerpt From SKILL.md

Plot Differential Contact Map

fig, axes = plt.subplots(1, 3, figsize=(15, 5))

## Condition 1

mat1 = clr1.matrix(balance=True).fetch(region)
im1 = axes[0].imshow(np.log2(mat1 + 1), cmap='Reds', vmin=-10, vmax=-3)
axes[0].set_title('Condition 1')
plt.colorbar(im1, ax=axes[0])

## Condition 2

mat2 = clr2.matrix(balance=True).fetch(region)
im2 = axes[1].imshow(np.log2(mat2 + 1), cmap='Reds', vmin=-10, vmax=-3)
axes[1].set_title('Condition 2')
plt.colorbar(im2, ax=axes[1])