How does it handle batch effects in biological data?

The skill provides implementation patterns for ComBat (from the sva package) and limma's removeBatchEffect function to correct for experimental variation.

Can this skill handle single-cell RNA-seq data?

Yes, it includes full Seurat workflows for QC, normalization, feature selection, dimensionality reduction, clustering, and marker identification.

Which R packages are supported for differential expression?

The skill supports industry-standard packages including DESeq2, edgeR, and limma (including limma-voom for RNA-seq and traditional microarray analysis).

Does it provide functional enrichment analysis?

Absolutely. It covers Gene Ontology (GO), KEGG pathways, and GSEA using the clusterProfiler and org.Hs.eg.db packages.

What visualization tools are included?

It generates code for publication-quality plots using EnhancedVolcano, ComplexHeatmap, and ggplot2-based PCA and MA plots.

R Genomics & Bioinformatics Analysis

Name: R Genomics & Bioinformatics Analysis
Author: choxos

bychoxos

0•

Data Science & ML

Performs comprehensive genomics and bioinformatics statistical analysis using Bioconductor and R tidy modeling workflows.

This skill empowers Claude to conduct sophisticated genomic data analysis within the R environment, focusing on standardized Bioconductor workflows. It provides implementation patterns for differential expression (DESeq2, edgeR, limma), functional enrichment (GO, KEGG, GSEA), and high-dimensional data visualization. Whether handling bulk RNA-seq, single-cell datasets via Seurat, or microarray data, the skill ensures statistically sound methods, proper batch effect correction, and publication-quality plotting for biological research and computational biology.

Key Features

01Functional enrichment including GO, KEGG, and Gene Set Enrichment Analysis (GSEA)

02Advanced biological visualization including Volcano plots, Heatmaps, and PCA

030 GitHub stars

04Single-cell RNA-seq workflows using Seurat for clustering and UMAP visualization

05Differential expression analysis with DESeq2, edgeR, and limma-voom

06Statistical methods for batch effect correction and GWAS association testing

Use Cases

01Interpreting biological mechanisms from gene lists through pathway and ontology enrichment

02Processing and clustering single-cell transcriptomics data to identify cell-type specific markers

03Identifying differentially expressed genes between treatment and control groups in bulk RNA-seq studies

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent genomics-analysis

For use in Claude.ai and ChatGPT

Key Features

01Functional enrichment including GO, KEGG, and Gene Set Enrichment Analysis (GSEA)

02Advanced biological visualization including Volcano plots, Heatmaps, and PCA

030 GitHub stars

04Single-cell RNA-seq workflows using Seurat for clustering and UMAP visualization

05Differential expression analysis with DESeq2, edgeR, and limma-voom

06Statistical methods for batch effect correction and GWAS association testing

Use Cases

01Interpreting biological mechanisms from gene lists through pathway and ontology enrichment

02Processing and clustering single-cell transcriptomics data to identify cell-type specific markers

03Identifying differentially expressed genes between treatment and control groups in bulk RNA-seq studies