tools-only 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('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