Generate Charts Easily from Study Data

You downloaded a cBioPortal study. Now you want to see what's in it — survival differences, mutation patterns, expression distributions, co-mutation relationships. Normally that means opening R, writing ggplot code, managing dependencies, and iterating in a notebook.

BioMCP ships 12 chart types compiled directly into the binary. No Python, no R, no subprocess calls, no package installs. One command produces a chart as a PNG, SVG, or directly in the terminal.

The charting engine is Kuva, an open-source Rust library linked at compile time. It's part of the binary — not a dependency you install.

Chart types

Bar

Mutation class counts for a single gene. The first thing you run to understand a gene's mutation landscape.

$ biomcp study query --study msk_impact_2017 --gene TP53 \
    --type mutations --chart bar --terminal

3,157 missense mutations dominate TP53 in MSK-IMPACT, followed by 683 nonsense and 517 frameshift deletions.

Pie

Same mutation data as proportions. Useful when the relative share matters more than absolute counts.

$ biomcp study query --study brca_tcga_pan_can_atlas_2018 \
    --gene TP53 --type mutations --chart pie -o tp53-pie.png

Missense 61.3%, frameshift deletion 13%, nonsense 12.4%.

Histogram

Expression distribution for a single gene. Reveals bimodal patterns, outliers, and subgroups.

$ biomcp study query --study brca_tcga_pan_can_atlas_2018 \
    --gene ERBB2 --type expression --chart histogram -o erbb2-histogram.png

The right-hand bump is the HER2-amplified subgroup — roughly 15-20% of breast cancers.

Density

Smoothed kernel density estimate of the same expression data. Cleaner view of the distribution shape.

$ biomcp study query --study brca_tcga_pan_can_atlas_2018 \
    --gene ERBB2 --type expression --chart density --terminal

Box

Expression grouped by mutation status. Whiskers, quartiles, outliers — the standard statistical summary.

$ biomcp study compare --study brca_tcga_pan_can_atlas_2018 \
    --gene TP53 --target ERBB2 --type expression \
    --chart box -o erbb2-by-tp53-box.png

ERBB2 expression stratified by TP53 mutation status.

Violin

Same grouped comparison, but showing the full distribution shape instead of just quartiles.

$ biomcp study compare --study brca_tcga_pan_can_atlas_2018 \
    --gene TP53 --target ERBB2 --type expression \
    --chart violin -o erbb2-by-tp53-violin.png

The bimodal HER2 pattern is visible in both groups.

Ridgeline

Overlapping density curves stacked vertically. Best for comparing two distributions at a glance.

$ biomcp study compare --study brca_tcga_pan_can_atlas_2018 \
    --gene TP53 --target ERBB2 --type expression \
    --chart ridgeline -o erbb2-by-tp53-ridgeline.png

TP53-mutant (blue) vs wildtype (orange). The wildtype group has a wider right tail.

Survival (Kaplan-Meier)

Time-to-event curves split by mutation status. The chart most clinicians look at first.

$ biomcp study survival --study brca_tcga_pan_can_atlas_2018 \
    --gene TP53 --chart survival --terminal

TP53-mutant patients have worse overall survival, with curves diverging over 300 months.

Heatmap

Co-mutation matrix across multiple genes. Viridis colormap — the most visually striking chart type.

$ biomcp study co-occurrence \
    --study brca_tcga_pan_can_atlas_2018 \
    --genes TP53,PIK3CA,GATA3,CDH1 \
    --chart heatmap --terminal

4x4 co-mutation matrix. TP53/PIK3CA has the strongest off-diagonal signal — the two most commonly co-mutated genes in breast cancer.

Stacked Bar

Mutation counts grouped and stacked by mutation status. Shows both the total count and the composition.

$ biomcp study compare --study brca_tcga_pan_can_atlas_2018 \
    --gene TP53 --target ERBB2 --type mutations \
    --chart stacked-bar --terminal

ERBB2 mutation counts stacked by TP53 status: mutated (blue) vs not-mutated (orange).

Waterfall

Sorted values across samples, commonly used for mutation burden or expression ranking.

$ biomcp study query --study brca_tcga_pan_can_atlas_2018 \
    --gene ERBB2 --type expression --chart waterfall --terminal

Scatter

Two-variable scatter plot for expression comparisons across genes.

$ biomcp study compare --study brca_tcga_pan_can_atlas_2018 \
    --gene TP53 --target ERBB2 --type expression \
    --chart scatter --terminal

Three formats, one flag

Every chart command accepts the same output options:

# Terminal — inline, no files
biomcp study survival ... --chart survival --terminal

# SVG — structured, parseable, lightweight
biomcp study survival ... --chart survival -o survival.svg

# PNG — for presentations and sharing
biomcp study survival ... --chart survival -o survival.png

Themes and palettes

Four themes and twelve color palettes, including three designed for colorblind accessibility:

biomcp study query ... --chart bar --theme dark --palette wong

Themes	Colorblind-accessible palettes
light, dark, solarized, minimal	deuteranopia, protanopia, tritanopia

All 12 palettes: wong, okabe-ito, tol-bright, tol-muted, tol-light, ibm, deuteranopia, protanopia, tritanopia, category10, pastel, bold.

Try it

uv tool install biomcp-cli
biomcp study download msk_impact_2017
biomcp study survival --study msk_impact_2017 --gene TP53 \
  --chart survival --terminal
biomcp study co-occurrence --study msk_impact_2017 \
  --genes TP53,KRAS,PIK3CA,BRAF --chart heatmap --terminal

Download a study, chart it. No setup, no dependencies, no code.