Bioinformatics Plot Generator

Other

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.

Install

openclaw skills install bioinformatics-plot-generator

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:

# Scatter
python plot-scatter-bar/scripts/plot_scatter_bar.py \
  --input data.tsv --plot-type scatter \
  --x-col sample1 --y-col sample2 --output scatter.png

# Bar chart
python plot-scatter-bar/scripts/plot_scatter_bar.py \
  --input counts.tsv --plot-type bar \
  --value-col count --group-col condition --output bar.png

# MA plot
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

# Correlation matrix
python plot-scatter-bar/scripts/plot_scatter_bar.py \
  --input expr_matrix.tsv --plot-type corrmat \
  --index-col gene --output corrmat.png

# Bubble chart
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.