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bio-data-visualization-specialized-omics-plots

Generate publication-ready omics plots from differential expression and enrichment results.

Updated Aug 23, 2026
Search Tags:#plotting#omics#enrichment#volcano#survival#pca#ma
One-click install
npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-data-visualization-specialized-omics-plots-stellaromics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-data-visualization-specialized-omics-plots
Source: https://github.com/stellaromics/fast-bioinfo/tree/main/.claude/agents/spatial-analysis/skills/bio-data-visualization-specialized-omics-plots
Command: npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-data-visualization-specialized-omics-plots-stellaromics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Omics researchers often need publication-ready visualizations across multiple analysis types but must cobble together plots from different libraries. This Skill provides reusable plotting functions for common omics visuals, including volcano, MA, PCA, enrichment dotplots, boxplots, survival curves, and more, with both R (ggplot2) and Python (matplotlib, seaborn, scikit-learn) examples.

Core Features & Use Cases

  • Volcano plots, MA plots, and PCA plots with consistent styling and cross-language examples.
  • Enrichment dotplots and expression boxplots with statistical overlays for clear interpretation.
  • UMAP/tSNE visualizations and survival curves for multidimensional and time-to-event data.
  • Quick-start prompts to reproduce visuals from differential expression results, enrichment outputs, or embedding coordinates.

Quick Start

Create a volcano plot from differential expression results using default thresholds.

Frequently Asked Questions about bio-data-visualization-specialized-omics-plots

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate publication-ready volcano plots from differential expression results?▼

Volcano plots can be generated from differential expression results using standardized plotting utilities with customizable aesthetics and default thresholds. The Skill provides reusable functions with both R and Python examples for publication-ready output.

Can I create PCA and UMAP visualizations using both R and Python?▼

PCA and UMAP visualizations support cross-language compatibility with R using ggplot2 and Python using matplotlib, seaborn, and scikit-learn. Consistent styling is applied across implementations for dimensionality reduction and multidimensional data plotting.

What's the best way to plot enrichment dotplots with statistical overlays?▼

Enrichment dotplots with statistical overlays can be created using reusable plotting functions designed for clear interpretation of enrichment results. The Skill applies standardized aesthetics to ensure publication-ready visualizations.

Does this Skill provide survival curve plotting for time-to-event omics data?▼

Survival curves for time-to-event data are supported alongside other omics visuals like MA plots and boxplots. The plotting utilities integrate into existing analysis pipelines with straightforward implementation.

Can I customize the aesthetics of MA plots generated through these plotting utilities?▼

MA plots generated through these utilities support customizable aesthetics with consistent styling across R and Python implementations. Standardized plotting functions ensure cross-language compatibility while maintaining publication-ready visual quality.

Do I need specific dependencies to integrate these omics plots into my analysis pipeline?▼

The plotting utilities integrate into analysis pipelines with R implementations using ggplot2 and Python using matplotlib, seaborn, and scikit-learn. No additional dependencies beyond these standard libraries are required for straightforward integration.

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