bio-copy-number-cnv-visualization
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.
npx skills add majiayu000/claude-skill-registry --skill cnv-visualization-gptomics-bioskills-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# CNV Visualization ## CNVkit Built-in Plots ```bash # 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 ```python 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 chr
- CNVkit Built-in Plots
- Python: Genome-wide Profile
- Python: Single Chromosome Plot
- Python: Cohort Heatmap
- R: ggplot2 Visualization
- Circos-style Plot
- GATK Plot Commands
- Related Skills
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
What does the bio-copy-number-cnv-visualization skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill cnv-visualization-gptomics-bioskills-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.