/bio-hi-c-analysis-hic-visualization

name: bio-hi-c-analysis-hic-visualization
description: 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.
tool_type: python
primary_tool: cooltools

Version Compatibility

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

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

• Python: `pip show <package>` then `help(module.function)` to check signatures
• CLI: `<tool> --version` then `<tool> --help` to confirm flags

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

Hi-C Visualization

**"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.

Required Imports

import cooler
import cooltools
import cooltools.lib.plotting
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import bioframe

Basic Contact Matrix Plot

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

# Get matrix for a region
matrix = clr.matrix(balance=True).fetch('chr1:50000000-60000000')

fig, ax = plt.subplots(figsize=(8, 8))
im = ax.imshow(matrix, cmap='Reds', norm=LogNorm(vmin=0.001, vmax=0.1))
plt.colorbar(im, ax=ax, label='Balanced contacts')
ax.set_title('chr1:50-60Mb')
plt.savefig('contact_matrix.png', dpi=150)

Triangle (Upper Triangle) Plot

def plot_triangle(matrix, ax, cmap='Reds', vmin=None, vmax=None):
    '''Plot Hi-C matrix as triangle (rotated 45 degrees)'''
    n = matrix.shape[0]

    # Create rotated matrix
    rotated = np.zeros((n, 2*n))
    for i in range(n):
        for j in range(i, n):
            y = j - i
            x = i + j
            rotated[y, x] = matrix[i, j]

    # Plot
    im = ax.imshow(rotated[:n//2, :], cmap=cmap, aspect='auto',
                   norm=LogNorm(vmin=vmin, vmax=vmax) if vmin else None)
    ax.set_ylim(n//2, 0)
    return im

matrix = clr.matrix(balance=True).fetch('chr1:50000000-60000000')
fig, ax = plt.subplots(figsize=(12, 4))
im = plot_triangle(matrix, ax, vmin=0.001, vmax=0.1)
plt.colorbar(im, ax=ax)
plt.savefig('triangle_plot.png', dpi=150)

Plot with TADs

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)

Plot with Loops

matrix = clr.matrix(balance=True).fetch('chr1:50000000-60000000')
loops = pd.read_csv('loops.bedpe', sep='\t')

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

# Mark loops
region_start = 50000000
bin_size = clr.binsize
for _, loop in loops[loops['chrom1'] == 'chr1'].iterrows():
    if (region_start <= loop['start1'] < 60000000 and
        region_start <= loop['start2'] < 60000000):
        x = (loop['start1'] - region_start) / bin_size
        y = (loop['start2'] - region_start) / bin_size
        circle = plt.Circle((y, x), 3, fill=False, color='blue', linewidth=1)
        ax.add_patch(circle)

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

Compare Two Matrices

clr1 = cooler.Cooler('sample1.mcool::resolutions/10000')
clr2 = cooler.Cooler('sample2.mcool::resolutions/10000')

region = 'chr1:50000000-60000000'
mat1 = clr1.matrix(balance=True).fetch(region)
mat2 = clr2.matrix(balance=True).fetch(region)

fig, axes = plt.subplots(1, 3, figsize=(15, 5))

# Sample 1
im1 = axes[0].imshow(mat1, cmap='Reds', norm=LogNorm(vmin=0.001, vmax=0.1))
axes[0].set_title('Sample 1')
plt.colorbar(im1, ax=axes[0])

# Sample 2
im2 = axes[1].imshow(mat2, cmap='Reds', norm=LogNorm(vmin=0.001, vmax=0.1))
axes[1].set_title('Sample 2')
plt.colorbar(im2, ax=axes[1])

# Log2 fold change
log2fc = np.log2(mat2 / mat1)
log2fc[np.isinf(log2fc)] = np.nan
im3 = axes[2].imshow(log2fc, cmap='coolwarm', vmin=-2, vmax=2)
axes[2].set_title('Log2(Sample2/Sample1)')
plt.colorbar(im3, ax=axes[2])

plt.tight_layout()
plt.savefig('comparison.png', dpi=150)

Split View (Upper/Lower Triangle)

mat1 = clr1.matrix(balance=True).fetch(region)
mat2 = clr2.matrix(balance=True).fetch(region)

# Combine: upper triangle from mat1, lower from mat2
combined = np.triu(mat1) + np.tril(mat2, k=-1)

fig, ax = plt.subplots(figsize=(8, 8))
im = ax.imshow(combined, cmap='Reds', norm=LogNorm(vmin=0.001, vmax=0.1))
ax.axline((0, 0), slope=1, color='black', linewidth=0.5)
ax.set_title('Sample1 (upper) vs Sample2 (lower)')
plt.colorbar(im, ax=ax)
plt.savefig('split_view.png', dpi=150)

Virtual 4C

**Goal:** Extract a one-dimensional contact frequency profile from a single viewpoint locus, simulating a 4C experiment from Hi-C data.

**Approach:** Select the matrix row corresponding to the viewpoint bin, extract balanced contact values across the chromosome, and plot as a filled line graph.

def virtual_4c(clr, viewpoint_chrom, viewpoint_pos, resolution=1000