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=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('Position (Mb)')
ax4.set_ylabel('CTCF')
ax4.set_xlim(50, 60)
plt.savefig('multi_track.png', dpi=150)
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