Version Compatibility

Reference examples tested with: cooler 0.9+, cooltools 0.6+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scipy 1.12+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Compartment Analysis

"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.

Required Imports

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

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

# 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 phasing
eigenvalues, eigenvectors = cooltools.eigs_cis(
    clr,
    view_df=view_df,
    phasing_track=gc_track,
    n_eigs=1,
)