Agent skill

bio-hi-c-analysis-hic-visualization

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.

View SKILL.md on GitHub Repository
Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/hic-visualization

SKILL.md

Hi-C Visualization

Visualize Hi-C contact matrices and genomic features.

Required Imports

python
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

python
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

python
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

python
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

python
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

python
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)

python
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

python
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

python
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

bash
# 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

python
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

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

majiayu000/claude-skill-registry

agent-ops-spec

Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-state

Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-spec

Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-testing

Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-testing

Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-state

Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.

163 31
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results