HiCAnalysis

Pure NumPy/SciPy Hi-C Chromatin 3D Genome Analysis Engine

No cooler, no cooltools, no Juicer, no HiCExplorer, no R HiTC.

What it does

Module	Description
ICE Normalization	Iterative Correction and Eigenvector decomposition
TAD Detection	Insulation score + Directionality Index
A/B Compartments	PCA on O/E matrix, GC-content guided orientation
Loop Detection	HICCUPS-inspired: enrichment + Poisson p-value
Differential TAD	Gained/lost boundaries, permutation significance
Visualization	6-panel Plotly interactive HTML

Quick Start

pip install numpy scipy pandas plotly scikit-learn
python -m hic_analysis
# outputs: hic_output/hic_analysis.html

As a Library

from hic_analysis import run_hic_analysis

# Demo with synthetic data
summary = run_hic_analysis()

# Real .cool file
summary = run_hic_analysis(
    cool_path="data.mcool",
    chrom="chr17",
    resolution=25000,
)

Output Files

File	Description
hic_analysis.html	6-panel interactive visualization
insulation_scores.csv	Per-bin insulation score + DI
tad_boundaries.csv	TAD boundaries with strength
ab_compartments.csv	PC1 eigenvector + A/B labels
loops.csv	Detected loops with enrichment
summary.json	Machine-readable summary

References

1. Lieberman-Aiden et al. (2009) Science — A/B compartments
2. Dixon et al. (2012) Nature — TADs + Directionality Index
3. Rao et al. (2014) Cell — HICCUPS loops
4. Crane et al. (2015) Nature — Insulation score
5. Imakaev et al. (2012) Nature Methods — ICE normalization