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bio-data-visualization-color-palettes

Maintainer FreedomIntelligence · Last updated April 1, 2026

Scientific color palettes: colorblind-safe, perceptually uniform, diverging.

OpenClawNanoClawAnalysisReproductionbio-data-visualization-color-palettes🧠 bioos extended suitebioos extended bioinformatics suitescientific

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-data-visualization-color-palettes

Maintainer
FreedomIntelligence
License
MIT
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • ggplot(df, aes(x, y, color = value)) + geom_point() + scale_color_viridis_c().
  • ggplot(df, aes(x, y, color = group)) + geom_point() + scale_color_viridis_d().

Source Doc

Excerpt From SKILL.md

Palette Types

TypeUse CaseExample
SequentialContinuous data (expression, coverage)viridis, Blues
DivergingCentered data (fold change, z-score)RdBu, coolwarm
QualitativeCategories (clusters, conditions)Set1, tab10

Options: viridis, magma, plasma, inferno, cividis, turbo

scale_color_viridis_c(option = 'magma') python import matplotlib.pyplot as plt

plt.scatter(x, y, c=values, cmap='viridis')

Sequential

scale_fill_brewer(palette = 'Blues') scale_color_distiller(palette = 'YlOrRd', direction = 1)

Use cases

  • Use bio-data-visualization-color-palettes for genomics and bioinformatics workflows.
  • Apply bio-data-visualization-color-palettes to sequencing, variant, or omics analysis tasks.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

Upstream Related Skills

  • data-visualization/ggplot2-fundamentals - Apply colors
  • data-visualization/heatmaps-clustering - Heatmap colors
  • data-visualization/specialized-omics-plots - Plot styling

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