bio-hi-c-analysis-compartment-analysis

npx skills add majiayu000/claude-skill-registry --skill compartment-analysis-gptomics-bioskills --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# Compartment Analysis Detect A/B compartments from Hi-C contact matrices. ## Required Imports ```python import cooler import cooltools import cooltools.lib.plotting import numpy as np import pandas as pd import matplotlib.pyplot as plt import bioframe ``` ## Compute Compartment Eigenvectors ```python clr = cooler.Cooler('matrix.mcool::resolutions/100000') # Get reference genome info view_df = bioframe.make_viewframe(clr.chromsizes) # 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, ) # Results are returned as a tuple (eigenvalues, eigenvectors) eigenvalues, eigenvectors = eigenvector_track print(f'Eigenvalues shape: {eigenvalues.shape}') print(eigenvectors.head()) ``` ## Use GC Content for Phasing ```python # GC content helps orient A/B compartments correctly # (A compartments typically have higher GC) # Fetch GC content gc_track = bioframe.frac_gc( bioframe.make_viewframe(clr.chromsizes), bioframe.load_fasta('genome.fa'), ) # Compute eigenvectors with GC pha