| name | bioinformatics-plot-generator |
| description | Route to the correct publication-quality plot sub-skill for volcano plots, heatmaps, box/violin plots, scatter plots, bar charts, MA plots, correlation matrices, and bubble charts from bioinformatics data. |
Bioinformatics Plot Generator
Purpose
This is the router skill for all bioinformatics plotting tasks. It selects and delegates to the appropriate sub-skill based on the type of plot requested. Each sub-skill is a fully self-contained, publication-quality plotting tool with 40–70 user-configurable parameters.
Sub-skills
| Sub-skill | Location | Plot types |
|---|
plot-volcano | plot-volcano/ | Volcano plots (DE/CRISPR/GWAS results) |
plot-heatmap | plot-heatmap/ | Heatmaps with clustering and annotations |
plot-box-violin | plot-box-violin/ | Box plots, violin plots, raincloud plots |
plot-scatter-bar | plot-scatter-bar/ | Scatter, bar, MA, correlation matrix, bubble |
Routing guide
Use this table to pick the correct sub-skill:
| User request signal | Sub-skill to use |
|---|
| "volcano plot", "differential expression", "CRISPR screen hits", "-log10 p-value vs fold change" | plot-volcano |
| "heatmap", "expression matrix", "gene expression heatmap", "z-score heatmap", "clustered heatmap" | plot-heatmap |
| "boxplot", "violin plot", "box and whisker", "raincloud", "distribution comparison", "group comparison" | plot-box-violin |
| "scatter plot", "correlation plot", "bar chart", "bar graph", "MA plot", "correlation matrix", "bubble chart", "bubble plot" | plot-scatter-bar |
Use when
- The user wants any of the supported plot types from tabular data or a numeric matrix
- The user wants a figure suitable for publication (300 DPI PNG + SVG)
- The user has a result table (differential expression, CRISPR screen, proteomics, etc.) and wants to visualize it
Do not use when
- The user wants genome browser tracks or signal plots from BAM/bigWig/BEDGraph files
- The user wants protein 3D structure visualization
- The user wants single-cell UMAP/tSNE trajectory plots requiring Scanpy/Seurat
- The user wants interactive plots (use a Plotly skill instead)
Sub-skill details
plot-volcano
Use for: Volcano plots from differential expression, CRISPR screens, GWAS, proteomics, or any table with a fold-change column and a p-value column.
Key features:
- Symmetric or asymmetric fold-change cutoffs (
--fc-cutoff, --fc-cutoff-neg)
- Color by discrete group (up/down/ns) or by continuous column with a colormap
- Three highlight layers: up-regulated, down-regulated, other (custom gene lists or files)
- Top-N auto-annotation with
adjustText label collision avoidance
- Quadrant counts displayed on plot
- 300 DPI PNG + SVG dual output
- Annotated TSV output table with assigned group per feature
Script: plot-volcano/scripts/plot_volcano.py
Minimal run:
python plot-volcano/scripts/plot_volcano.py \
--input results.tsv \
--feature-col gene \
--x-col log2FoldChange \
--p-col padj \
--output volcano.png
plot-heatmap
Use for: Heatmaps from any numeric matrix — gene expression, protein abundance, methylation, pathway scores, etc.
Key features:
- Hierarchical clustering with dendrogram display (scipy linkage methods: ward, complete, average, single)
- Row and column annotation bars from separate TSV files with custom color palettes
- Variance-based row filtering (keep top N most variable rows)
- Z-score normalization per row or column with optional clipping
- Cell value annotation (show numbers inside cells)
- Auto figure sizing based on matrix dimensions
- Flexible colormap, vmin/vmax, missing value handling
Script: plot-heatmap/scripts/plot_heatmap.py
Minimal run:
python plot-heatmap/scripts/plot_heatmap.py \
--input expression_matrix.tsv \
--index-col gene \
--output heatmap.png
plot-box-violin
Use for: Comparing distributions across groups — box plots, violin plots, combined box+violin, or raincloud plots.
Key features:
- Four plot types:
box, violin, both (violin with inner box), raincloud
- Jittered individual points with customizable size and alpha
- Multi-group statistical testing: auto, all_pairs, vs_first, vs_last
- Tests: Mann–Whitney U, Welch t-test, Dunn's test (pure numpy, no scipy dependency)
- Multiple testing correction: Bonferroni or Benjamini–Hochberg FDR
- Significance bracket annotations drawn above each pair
- Horizontal orientation option
- Custom group ordering and color palettes
Script: plot-box-violin/scripts/plot_box_violin.py
Minimal run:
python plot-box-violin/scripts/plot_box_violin.py \
--input data.tsv \
--value-col expression \
--group-col condition \
--plot-type violin \
--output violin.png
plot-scatter-bar
Use for: Five plot types in one script — scatter plots, bar charts, MA plots, correlation matrices, and bubble charts.
Key features:
Scatter:
- Linear regression line with 95% CI band (bootstrap)
- Pearson/Spearman correlation annotation
- Optional marginal histograms
- Highlight gene list with separate color/label
Bar:
- Grouped or stacked bar charts
- Error bars: SEM, SD, or 95% CI
- Value labels on bars
- Horizontal orientation
MA (ratio vs. mean):
- M-A plot (log-ratio vs. average intensity)
- LOESS smoothing line (tricubic kernel, pure numpy)
- Significant feature highlighting
Correlation matrix:
- Pairwise correlation heatmap (Pearson or Spearman)
- Hierarchical clustering of samples
- Correlation values displayed in cells
Bubble:
- Bubble chart with x/y/size/color columns
- Log-scale size normalization
- Separate size and color legends
Script: plot-scatter-bar/scripts/plot_scatter_bar.py
Minimal runs:
python plot-scatter-bar/scripts/plot_scatter_bar.py \
--input data.tsv --plot-type scatter \
--x-col sample1 --y-col sample2 --output scatter.png
python plot-scatter-bar/scripts/plot_scatter_bar.py \
--input counts.tsv --plot-type bar \
--value-col count --group-col condition --output bar.png
python plot-scatter-bar/scripts/plot_scatter_bar.py \
--input de_results.tsv --plot-type ma \
--mean-col baseMean --ratio-col log2FC --output ma.png
python plot-scatter-bar/scripts/plot_scatter_bar.py \
--input expr_matrix.tsv --plot-type corrmat \
--index-col gene --output corrmat.png
python plot-scatter-bar/scripts/plot_scatter_bar.py \
--input enrichment.tsv --plot-type bubble \
--x-col NES --y-col pathway \
--size-col gene_count --color-col padj --output bubble.png
Common style parameters (all sub-skills)
| Parameter | Default | Description |
|---|
--fig-width | auto | Figure width in inches |
--fig-height | auto | Figure height in inches |
--dpi | 300 | Output resolution |
--font-family | sans-serif | Font family |
--base-fontsize | 11 | Base font size (pt) |
--title | — | Plot title |
--xlabel | auto | X-axis label |
--ylabel | auto | Y-axis label |
--output | required | Output PNG path |
--output-svg | off | Also save SVG alongside PNG |
Procedure for routing
- Identify the plot type from the user's request.
- Read the corresponding sub-skill SKILL.md (e.g.,
plot-volcano/SKILL.md) for the full parameter reference.
- Identify required input columns from the user's data or description.
- Construct the command with appropriate parameters from the parameter decision guide in the sub-skill SKILL.md.
- Run the script and return the output path.
- If the user requests customization beyond the defaults, consult the sub-skill's parameter decision guide and full argument list.
