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('-'))
            cns = cns[(cns['chromosome'] == chrom) &
                     (cns['start'] >= start) & (cns['end'] <= end)]
        data[sample] = cns.set_index(['chromosome', 'start', 'end'])['log2']

    df = pd.DataFrame(data)

    fig, ax = plt.subplots(figsize=(12, max(4, len(cns_files) * 0.3)))
    sns.heatmap(df.T, cmap='RdBu_r', center=0, vmin=-2, vmax=2,
        xticklabels=False, ax=ax)
    ax.set_xlabel('Genomic Position')
    ax.set_ylabel('Sample')

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

R: ggplot2 Visualization

Goal: Create a faceted genome-wide CNV profile using ggplot2.

Approach: Plot bins as points with segment overlays, faceted by chromosome with free x-scales.

library(ggplot2)
library(dplyr)

plot_cnv_profile <- function(cnr_file, cns_file) {
    cnr <- read.delim(cnr_file)
    cns <- read.delim(cns_file)

    # Order chromosomes
    chr_order <- c(paste0('chr', 1:22), 'chrX', 'chrY')
    cnr$chromosome <- factor(cnr$chromosome, levels=chr_order)
    cns$chromosome <- factor(cns$chromosome, levels=chr_order)

    p <- ggplot() +
        geom_point(data=cnr, aes(x=start, y=log2), size=0.1, alpha=0.3) +
        geom_segment(data=cns, aes(x=start, xend=end, y=log2, yend=log2,
            color=ifelse(log2 > 0.3, 'Gain', ifelse(log2 < -0.3, 'Loss', 'Neutral'))),
            size=1) +
        facet_grid(~chromosome, scales='free_x', space='free_x') +
        scale_color_manual(values=c('Gain'='red', 'Loss'='blue', 'Neutral'='green')) +
        geom_hline(yintercept=0, linetype='dashed') +
        ylim(-2, 2) +
        theme_minimal() +
        theme(axis.text.x=element_blank(),
              panel.spacing=unit(0, 'lines'),
              strip.text=element_text(size=6)) +
        labs(x='', y='Log2 Copy Ratio', color='')

    return(p)
}

Circos-style Plot

Goal: Display CNV data as a circular genome plot.

Approach: Map segments to polar coordinates, render gain/loss bars around the genome circle using matplotlib polar projection.

def plot_circos_cnv(cns_file, output=None):
    '''Create circular CNV plot.'''
    import matplotlib.patches as mpatches

    cns = pd.read_csv(cns_file, sep='\t')

    fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={'projection': 'polar'})

    chroms = [f'chr{i}' for i in range(1, 23)] + ['chrX', 'chrY']
    chrom_sizes = {'chr1': 249e6, 'chr2': 243e6, 'chr3': 198e6}  # Add all sizes

    total_size = sum(chrom_sizes.get(c, 50e6) for c in chroms)
    cumsum = 0

    for chrom in chroms:
        size = chrom_sizes.get(chrom, 50e6)
        cns_chr = cns[cns['chromosome'] == chrom]

        for _, seg in cns_chr.iterrows():
            theta_start = 2 * np.pi * (cumsum + seg['start']) / total_size
            theta_end = 2 * np.pi * (cumsum + seg['end']) / total_size
            r = 0.5 + seg['log2'] * 0.3

            color = 'red' if seg['log2'] > 0.3 else ('blue' if seg['log2'] < -0.3 else 'gray')
            ax.bar((theta_start + theta_end) / 2, r - 0.3, width=theta_end - theta_start,
                bottom=0.3, color=color, alpha=0.7)

        cumsum += size

    ax.set_ylim(0, 1)
    ax.axis('off')

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

GATK Plot Commands

Goal: Generate GATK-native CNV plots showing denoised ratios and modeled segments.

Approach: Run PlotDenoisedCopyRatios and PlotModeledSegments on GATK CNV output files.

# Denoised copy ratios
gatk PlotDenoisedCopyRatios \
    --standardized-copy-ratios sample.standardized.tsv \
    --denoised-copy-ratios sample.denoised.tsv \
    --sequence-dictionary reference.dict \
    --output-prefix sample \
    -O plots/

# Modeled segments with allelic info
gatk PlotModeledSegments \
    --denoised-copy-ratios sample.denoised.tsv \
    --allelic-counts sample.hets.tsv \
    --segments sample.modelFinal.seg \
    --sequence-dictionary reference.dict \
    --output-prefix sample \
    -O plots/

Related Skills

• copy-number/cnvkit-analysis - Generate CNV calls
• copy-number/gatk-cnv - GATK CNV workflow
• copy-number/cnv-annotation - Add gene annotations