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

Hi-C Visualization

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

def virtual_4c(clr, viewpoint_chrom, viewpoint_pos, resolution=10000):
    '''Extract virtual 4C from Hi-C'''
    # Get row of matrix at viewpoint
    viewpoint_bin = viewpoint_pos // resolution

    # Get contacts from this bin to all others on same chromosome
    matrix = clr.matrix(balance=True).fetch(viewpoint_chrom)
    v4c = matrix[viewpoint_bin, :]

    # Create coordinates
    bins = clr.bins().fetch(viewpoint_chrom)
    coords = bins['start'].values

    return coords, v4c

coords, v4c = virtual_4c(clr, 'chr1', 55000000)

fig, ax = plt.subplots(figsize=(12, 3))
ax.fill_between(coords / 1e6, 0, v4c, alpha=0.5)
ax.axvline(55, color='red', linestyle='--', label='Viewpoint')
ax.set_xlabel('Position (Mb)')
ax.set_ylabel('Contact frequency')
ax.set_title('Virtual 4C from chr1:55Mb')
ax.legend()
plt.savefig('virtual_4c.png', dpi=150)

Multi-Track Figure

fig = plt.figure(figsize=(12, 10))

# Hi-C matrix (triangle)
ax1 = fig.add_axes([0.1, 0.5, 0.8, 0.4])
matrix = clr.matrix(balance=True).fetch('chr1:50000000-60000000')
plot_triangle(matrix, ax1, vmin=0.001, vmax=0.1)
ax1.set_ylabel('Hi-C')

# Insulation score
ax2 = fig.add_axes([0.1, 0.35, 0.8, 0.1])
insulation = pd.read_csv('insulation.bedgraph', sep='\t',
                         names=['chrom', 'start', 'end', 'score'])
ins_region = insulation[(insulation['chrom'] == 'chr1') &
                        (insulation['start'] >= 50000000) &
                        (insulation['end'] <= 60000000)]
ax2.plot(ins_region['start'] / 1e6, ins_region['score'])
ax2.set_ylabel('Insulation')
ax2.set_xlim(50, 60)

# Gene track (placeholder)
ax3 = fig.add_axes([0.1, 0.2, 0.8, 0.1])
ax3.set_ylabel('Genes')
ax3.set_xlim(50, 60)

# CTCF ChIP-seq (placeholder)
ax4 = fig.add_axes([0.1, 0.05, 0.8, 0.1])
ax4.set_xlabel()
ax4.set_ylabel()
ax4.set_xlim(, )

plt.savefig(, dpi=)

Using HiCExplorer Visualization

# Plot matrix with HiCExplorer
hicPlotMatrix \
    -m matrix.cool \
    --region chr1:50000000-60000000 \
    --log1p \
    --colorMap Reds \
    -o hic_plot.png

# Plot with TADs
hicPlotTADs \
    --tracks tracks.ini \
    --region chr1:50000000-60000000 \
    -o tad_plot.png

Cooltools Pileup Plot

import cooltools

# Pileup at features (e.g., loop anchors)
pileup = cooltools.pileup(
    clr,
    features=loops[['chrom1', 'start1', 'end1', 'chrom2', 'start2', 'end2']],
    view_df=view_df,
    expected=expected,
    flank=100000,
)

# Average pileup
avg_pileup = np.nanmean(pileup, axis=2)

fig, ax = plt.subplots(figsize=(6, 6))
im = ax.imshow(avg_pileup, cmap='Reds')
ax.set_title('Average pileup at loops')
plt.colorbar(im, ax=ax)
plt.savefig('pileup.png', dpi=150)

Related Skills

• hic-data-io - Load contact matrices
• tad-detection - Generate TADs to visualize
• loop-calling - Generate loops to visualize
• compartment-analysis - Visualize compartments