# CNV Visualization Usage Guide > Effective CNV visualization helps identify patterns, validate calls, and communicate findings. This guide covers genome-wide profiles, region-specific plots, and cohort-level comparisons. - Skill: `tools-only/cnv-visualization-usage-guide` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add tools-only/cnv-visualization-usage-guide` - Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/cnv-visualization-usage-guide/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tools-only (https://skillmd.com/u/tools-only) - Updated: 2026-09-29 - Page: https://skillmd.com/skills/tools-only/cnv-visualization-usage-guide --- # CNV Visualization Usage Guide ## Overview Effective CNV visualization helps identify patterns, validate calls, and communicate findings. This guide covers genome-wide profiles, region-specific plots, and cohort-level comparisons. ## Prerequisites ```bash conda install -c bioconda cnvkit pip install matplotlib seaborn pandas plotly ``` ## Quick Start Tell your AI agent what you want to do: - "Create a genome-wide CNV scatter plot from my CNVkit results" - "Generate a heatmap comparing CNV profiles across all samples" - "Plot chromosome 17 with TP53 and BRCA1 gene annotations" - "Create an interactive CNV plot for collaborators" ## Key Plot Types | Plot Type | Use Case | |-----------|----------| | Genome-wide scatter | Overview of all CNVs | | Chromosome zoom | Detailed view of specific regions | | Heatmap | Compare CNVs across samples | | Ideogram/diagram | Publication-ready summary | | Circos | Whole-genome circular view | ## Quick CNVkit Plotting ```bash # All-in-one plots cnvkit.py scatter sample.cnr -s sample.cns -o sample_scatter.png cnvkit.py diagram sample.cnr -s sample.cns -o sample_diagram.pdf # Cohort heatmap cnvkit.py heatmap *.cns -o cohort_heatmap.pdf ``` ## Publication-Quality Plots ```python import pandas as pd import matplotlib.pyplot as plt import matplotlib.patches as mpatches import numpy as np # Style settings plt.rcParams['font.family'] = 'Arial' plt.rcParams['font.size'] = 10 plt.rcParams['axes.linewidth'] = 0.5 class CNVPlotter: def __init__(self, cnr_file, cns_file): self.cnr = pd.read_csv(cnr_file, sep='\t') self.cns = pd.read_csv(cns_file, sep='\t') self.setup_genome() def setup_genome(self): '''Calculate cumulative chromosome positions.''' self.chroms = [f'chr{i}' for i in range(1, 23)] + ['chrX'] self.chrom_sizes = self.cnr.groupby('chromosome')['end'].max().to_dict() cumsum = 0 self.chrom_starts = {} self.chrom_mids = {} for chrom in self.chroms: if chrom in self.chrom_sizes: self.chrom_starts[chrom] = cumsum self.chrom_mids[chrom] = cumsum + self.chrom_sizes[chrom] / 2 cumsum += self.chrom_sizes[chrom] self.genome_size = cumsum def genome_plot(self, figsize=(16, 3.5)): '''Create genome-wide CNV plot.''' fig, ax = plt.subplots(figsize=figsize) # Add cumulative position self.cnr['cumpos'] = self.cnr.apply( lambda x: self.chrom_starts.get(x['chromosome'], 0) + x['start'], axis=1) # Plot points ax.scatter(self.cnr['cumpos'] / 1e6, self.cnr['log2'], s=0.5, c='#888888', alpha=0.4, rasterized=True) # Plot segments for _, seg in self.cns.iterrows(): if seg['chromosome'] in self.chrom_starts: start = (self.chrom_starts[seg['chromosome']] + seg['start']) / 1e6 end = (self.chrom_starts[seg['chromosome']] + seg['end']) / 1e6 color = '#c0392b' if seg['log2'] > 0.2 else ('#2980b9' if seg['log2'] < -0.2 else '#27ae60') ax.plot([start, end], [seg['log2'], seg['log2']], c=color, lw=2, solid_capstyle='butt') # Chromosome shading and labels for i, chrom in enumerate(self.chroms): if chrom in self.chrom_starts: if i % 2: ax.axvspan(self.chrom_starts[chrom]/1e6, (self.chrom_starts[chrom] + self.chrom_sizes[chrom])/1e6, alpha=0.05, color='black') ax.text(self.chrom_mids[chrom]/1e6, -2.3, chrom.replace('chr', ''), ha='center', fontsize=8) ax.set_xlim(0, self.genome_size/1e6) ax.set_ylim(-2.5, 2.5) ax.set_xlabel('Genomic position (Mb)') ax.set_ylabel('Log2 copy ratio') ax.axhline(0, c='black', lw=0.5, ls='--') ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) plt.tight_layout() return fig, ax def chromosome_plot(self, chrom, genes=None, figsize=(12, 3)): '''Plot single chromosome with optional gene annotations.''' fig, ax = plt.subplots(figsize=figsize) cnr_chr = self.cnr[self.cnr['chromosome'] == chrom] cns_chr = self.cns[self.cns['chromosome'] == chrom] ax.scatter(cnr_chr['start']/1e6, cnr_chr['log2'], s=3, c='#888888', alpha=0.5) for _, seg in cns_chr.iterrows(): color = '#c0392b' if seg['log2'] > 0.2 else ('#2980b9' if seg['log2'] < -0.2 else '#27ae60') ax.plot([seg['start']/1e6, seg['end']/1e6], [seg['log2'], seg['log2']], c=color, lw=3) if genes: for gene, pos in genes.items(): ax.axvline(pos/1e6, c='orange', lw=1, ls='--', alpha=0.7) ax.text(pos/1e6, 2, gene, rotation=90, ha='right', fontsize=8) ax.set_xlabel(f'{chrom} position (Mb)') ax.set_ylabel('Log2 copy ratio') ax.set_ylim(-2.5, 2.5) ax.axhline(0, c='black', lw=0.5, ls='--') plt.tight_layout() return fig, ax # Usage plotter = CNVPlotter('sample.cnr', 'sample.cns') fig, ax = plotter.genome_plot() fig.savefig('cnv_profile.pdf', dpi=300) fig, ax = plotter.chromosome_plot('chr17', genes={'TP53': 7687490, 'BRCA1': 43044295}) fig.savefig('chr17_cnv.pdf', dpi=300) ``` ## Heatmap for Cohort Analysis ```python import seaborn as sns def cohort_heatmap(cns_files, region=None, cluster=True): '''Create clustered heatmap of CNVs across samples.''' # Load and bin data bin_size = 1000000 # 1Mb bins data = {} for f in cns_files: sample = f.split('/')[-1].replace('.cns', '') cns = pd.read_csv(f, sep='\t') # Create binned representation bins = [] for _, seg in cns.iterrows(): n_bins = int((seg['end'] - seg['start']) / bin_size) + 1 bins.extend([seg['log2']] * n_bins) data[sample] = bins[:1000] # Truncate to same length df = pd.DataFrame(data).T # Cluster samples if cluster: from scipy.cluster.hierarchy import linkage, dendrogram linkage_matrix = linkage(df, method='ward') fig, ax = plt.subplots(figsize=(14, max(4, len(cns_files) * 0.4))) sns.heatmap(df, cmap='RdBu_r', center=0, vmin=-1, vmax=1, xticklabels=False, yticklabels=True, ax=ax, cbar_kws={'label': 'Log2 ratio'}) plt.tight_layout() return fig fig = cohort_heatmap(['sample1.cns', 'sample2.cns', 'sample3.cns']) fig.savefig('cohort_cnv_heatmap.pdf') ``` ## Interactive Visualization with Plotly ```python import plotly.graph_objects as go def interactive_cnv_plot(cnr, cns): '''Create interactive CNV plot.''' fig = go.Figure() # Add bins fig.add_trace(go.Scattergl( x=cnr['start'], y=cnr['log2'], mode='markers', marker=dict(size=2, color='gray', opacity=0.5), name='Bins' )) # Add segments for _, seg in cns.iterrows(): color = 'red' if seg['log2'] > 0.3 else ('blue' if seg['log2'] < -0.3 else 'green') fig.add_trace(go.Scatter( x=[seg['start'], seg['end']], y=[seg['log2'], seg['log2']], mode='lines', line=dict(color=color, width=3), name=f"{seg['chromosome']}:{seg['start']}-{seg['end']}" )) fig.update_layout( xaxis_title='Position', yaxis_title='Log2 Copy Ratio', yaxis_range=[-2, 2], showlegend=False ) return fig # Save as HTML for sharing fig = interactive_cnv_plot(cnr, cns) fig.write_html('cnv_interactive.html') ``` ## Example Prompts > "Create a genome-wide CNV scatter plot from my CNVkit results" > "Generate a heatmap comparing CNV profiles across all samples in my cohort" > "Plot chromosome 17 with TP53 and BRCA1 gene annotations highlighted" > "Create an interactive CNV plot I can share with collaborators" ## Tips for Publication Figures 1. **Resolution**: Use 300 DPI minimum 2. **Format**: PDF for vector graphics, PNG for presentations 3. **Colors**: Use colorblind-friendly palettes 4. **Labels**: Include chromosome boundaries clearly 5. **Scale**: Show Mb or kb consistently 6. **Legend**: Include log2 ratio scale interpretation