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bio-hi-c-analysis-compartment-analysis

Maintainer FreedomIntelligence · Last updated April 1, 2026

Detect A/B compartments from Hi-C data using cooltools and eigenvector decomposition. Identify active (A) and inactive (B) chromatin compartments from contact matrices. Use when identifying A/B compartments from Hi-C data.

OpenClawNanoClawAnalysisReproductionbio-hi-c-analysis-compartment-analysis🧬 bioinformatics (gptomics bio-* suite)bioinformatics — epigenomics & chromatindetect

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

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

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

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

Python: cooltools.eigs_cis(clr, gc_cov) for eigenvector decomposition.
Identify A/B compartments from my Hi-C data" → Decompose the contact matrix via eigenvector analysis to classify chromatin into active (A) and inactive (B) compartments. Python: cooltools.eigs_cis(clr, gc_cov) for eigenvector decomposition.
Detect A/B compartments from Hi-C contact matrices.
view_df = bioframe.make_viewframe(clr.chromsizes).

Source Doc

Excerpt From SKILL.md

Compute Compartment Eigenvectors

clr = cooler.Cooler('matrix.mcool::resolutions/100000')

## Compute expected values first

expected = cooltools.expected_cis(clr, view_df=view_df, ignore_diags=2)

## Compute eigenvector decomposition (compartments)

eigenvector_track = cooltools.eigs_cis(
    clr,
    view_df=view_df,
    phasing_track=None,  # Or provide GC content track
    n_eigs=3,
)