/bio-copy-number-cnv-visualization

name: bio-copy-number-cnv-visualization
description: Visualize copy number profiles, segments, and compare across samples. Create publication-quality plots of CNV data from CNVkit, GATK, or other callers. Use when creating genome-wide CNV plots, sample heatmaps, or chromosome-level visualizations.
tool_type: mixed
primary_tool: matplotlib

Version Compatibility

Reference examples tested with: GATK 4.5+, ggplot2 3.5+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, seaborn 0.13+

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

• Python: `pip show <package>` then `help(module.function)` to check signatures
• R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
• 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.

CNV Visualization

**"Plot my copy number profile"** → Create genome-wide scatter plots, segmentation views, and multi-sample heatmaps from CNV caller output.

• CLI: `cnvkit.py scatter`, `cnvkit.py diagram`, `cnvkit.py heatmap`
• Python: `matplotlib` for custom CNV plots
• R: `ggplot2` for publication figures

CNVkit Built-in Plots

**Goal:** Generate standard CNV visualizations directly from CNVkit output files.

**Approach:** Use CNVkit scatter, diagram, and heatmap commands for quick visual inspection.

# Scatter plot with segments
cnvkit.py scatter sample.cnr -s sample.cns -o scatter.png

# Scatter for specific chromosome
cnvkit.py scatter sample.cnr -s sample.cns -c chr17 -o chr17_scatter.png

# Ideogram diagram
cnvkit.py diagram sample.cnr -s sample.cns -o diagram.pdf

# Heatmap across samples
cnvkit.py heatmap *.cns -o cohort_heatmap.pdf

# Heatmap for specific region
cnvkit.py heatmap *.cns -c chr17:7500000-7700000 -o tp53_region.pdf

Python: Genome-wide Profile

**Goal:** Create a genome-wide CNV scatter plot with colored segments across all chromosomes.

**Approach:** Calculate cumulative genomic positions, plot log2 ratios as gray dots, and overlay colored segment lines.

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

def plot_cnv_profile(cnr_file, cns_file, output=None):
    '''Plot genome-wide CNV profile with segments.'''
    cnr = pd.read_csv(cnr_file, sep='\t')
    cns = pd.read_csv(cns_file, sep='\t')

    fig, ax = plt.subplots(figsize=(16, 4))

    # Chromosome positions
    chroms = [f'chr{i}' for i in range(1, 23)] + ['chrX', 'chrY']
    chrom_order = {c: i for i, c in enumerate(chroms)}
    cnr['chrom_num'] = cnr['chromosome'].map(chrom_order)
    cnr = cnr.dropna(subset=['chrom_num'])

    # Calculate cumulative position
    chrom_sizes = cnr.groupby('chromosome')['end'].max()
    cumsum = 0
    chrom_starts = {}
    for chrom in chroms:
        if chrom in chrom_sizes.index:
            chrom_starts[chrom] = cumsum
            cumsum += chrom_sizes[chrom]

    cnr['cumpos'] = cnr.apply(lambda x: chrom_starts.get(x['chromosome'], 0) + x['start'], axis=1)

    # Plot bins
    ax.scatter(cnr['cumpos'], cnr['log2'], s=1, c='gray', alpha=0.5)

    # Plot segments
    for _, seg in cns.iterrows():
        if seg['chromosome'] in chrom_starts:
            start = chrom_starts[seg['chromosome']] + seg['start']
            end = chrom_starts[seg['chromosome']] + seg['end']
            color = 'red' if seg['log2'] > 0.2 else ('blue' if seg['log2'] < -0.2 else 'green')
            ax.hlines(seg['log2'], start, end, colors=color, linewidth=2)

    # Chromosome boundaries
    for i, chrom in enumerate(chroms):
        if chrom in chrom_starts:
            ax.axvline(chrom_starts[chrom], color='lightgray', linewidth=0.5)
            if i % 2 == 0:
                ax.text(chrom_starts[chrom], ax.get_ylim()[1], chrom.replace('chr', ''),
                    fontsize=8, ha='left')

    ax.axhline(0, color='black', linewidth=0.5)
    ax.set_ylabel('Log2 Copy Ratio')
    ax.set_xlabel('Genomic Position')
    ax.set_ylim(-2, 2)
    plt.tight_layout()

    if output:
        plt.savefig(output, dpi=150)
    return fig, ax

Python: Single Chromosome Plot

**Goal:** Visualize the CNV profile of a single chromosome at higher resolution.

**Approach:** Filter bins and segments to one chromosome, plot with gain/loss/neutral color coding.

def plot_chromosome(cnr, cns, chrom, ax=None):
    '''Plot CNV profile for single chromosome.'''
    if ax is None:
        fig, ax = plt.subplots(figsize=(12, 3))

    cnr_chr = cnr[cnr['chromosome'] == chrom].copy()
    cns_chr = cns[cns['chromosome'] == chrom].copy()

    # Plot bins
    ax.scatter(cnr_chr['start'] / 1e6, cnr_chr['log2'], s=5, c='gray', alpha=0.5)

    # Plot segments
    for _, seg in cns_chr.iterrows():
        color = 'red' if seg['log2'] > 0.3 else ('blue' if seg['log2'] < -0.3 else 'darkgreen')
        ax.hlines(seg['log2'], seg['start']/1e6, seg['end']/1e6, colors=color, linewidth=3)

    ax.axhline(0, color='black', linewidth=0.5, linestyle='--')
    ax.axhline(0.5, color='red', linewidth=0.5, linestyle=':')
    ax.axhline(-0.5, color='blue', linewidth=0.5, linestyle=':')
    ax.set_xlabel(f'{chrom} Position (Mb)')
    ax.set_ylabel('Log2 Ratio')
    ax.set_title(chrom)
    return ax

Python: Cohort Heatmap

**Goal:** Compare CNV patterns across multiple samples in a single heatmap.

**Approach:** Load segment files for all samples, build a matrix of log2 ratios, and render with seaborn diverging colormap.

import seaborn as sns

def plot_cnv_heatmap(cns_files, region=None, output=None):
    '''Create heatmap of CNVs across samples.'''
    # Load all samples
    data = {}
    for f in cns_files:
        sample = f.replace('.cns', '').split('/')[-1]
        cns = pd.read_csv(f, sep='\t')
        if region:
            chrom, coords = region.split(':')
            start, end = map(int, coords.split('-'))