Data & ReproScientific VisualizationFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-hi-c-analysis-hic-visualization

Maintainer FreedomIntelligence · Last updated April 1, 2026

Visualize Hi-C contact matrices, TADs, loops, and genomic features using matplotlib, cooltools, and HiCExplorer. Create triangle plots, virtual 4C, and multi-track figures. Use when visualizing contact matrices or genomic features.

OpenClawNanoClawAnalysisWritingbio-hi-c-analysis-hic-visualization🧬 bioinformatics (gptomics bio-* suite)bioinformatics — epigenomics & chromatinvisualize

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

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

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

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

Python: matplotlib.pyplot.imshow() on cooler matrices, cooltools for aggregate plots.
CLI: hicPlotMatrix (HiCExplorer).
Plot my Hi-C contact matrix" → Create triangle heatmaps, virtual 4C profiles, and multi-track figures combining contact maps with genomic annotations. Python: matplotlib.pyplot.imshow() on cooler matrices, cooltools for aggregate plots CLI: hicPlotMatrix (HiCExplorer).
Visualize Hi-C contact matrices and genomic features.
matrix = clr.matrix(balance=True).fetch('chr1:50000000-60000000').

Source Doc

Excerpt From SKILL.md

Basic Contact Matrix Plot

clr = cooler.Cooler('matrix.mcool::resolutions/10000')

## Plot with TADs

```python
import pandas as pd

matrix = clr.matrix(balance=True).fetch('chr1:50000000-60000000')
tads = pd.read_csv('tads.bed', sep='\t', names=['chrom', 'start', 'end'])

fig, ax = plt.subplots(figsize=(8, 8))
im = ax.imshow(matrix, cmap='Reds', norm=LogNorm(vmin=0.001, vmax=0.1))

## Overlay TAD boundaries

region_start = 50000000
bin_size = clr.binsize
for _, tad in tads[tads['chrom'] == 'chr1'].iterrows():
    if region_start <= tad['start'] < 60000000:
        pos = (tad['start'] - region_start) / bin_size
        ax.axhline(pos, color='blue', linewidth=0.5, alpha=0.5)
        ax.axvline(pos, color='blue', linewidth=0.5, alpha=0.5)

plt.colorbar(im, ax=ax)
plt.savefig('matrix_with_tads.png', dpi=150)