Can I use this for single-cell RNA-seq data?

Absolutely. It provides a complete Seurat workflow covering quality control, normalization, dimensionality reduction (PCA/UMAP), and cluster marker identification.

What types of genomic visualization are supported?

The skill includes templates for EnhancedVolcano plots, ComplexHeatmaps, MA plots, and Manhattan plots for GWAS results.

Does this skill support Bioconductor package management?

Yes, it includes standardized snippets for setting up BiocManager and installing essential packages like DESeq2, edgeR, and clusterProfiler.

How does it handle batch effects in genomic datasets?

It provides implementation patterns for ComBat (sva package) and limma's removeBatchEffect to correct for technical variation across different experimental runs.

Genomics Analysis in R

Name: Genomics Analysis in R
Author: choxos

bychoxos

Data Science & ML

Simplifies complex bioinformatics workflows in R using Bioconductor for RNA-seq, microarray, and single-cell genomic analysis.

About

This skill provides a comprehensive framework for performing genomics and bioinformatics statistical analysis within R. It equips Claude with the domain-specific implementation patterns needed to execute differential expression analysis using industry-standard tools like DESeq2 and edgeR, perform pathway enrichment via clusterProfiler, and visualize biological data with sophisticated plotting libraries. Whether handling bulk RNA-seq, processing single-cell datasets with Seurat, or applying batch effect correction with ComBat, this skill ensures scientifically rigorous and reproducible computational biology workflows.

Key Features

End-to-end single-cell RNA-seq processing using the Seurat workflow
0 GitHub stars
Automated RNA-seq differential expression workflows using DESeq2, edgeR, and limma-voom
Advanced genomic visualizations including EnhancedVolcano, ComplexHeatmap, and PCA plots
Functional enrichment analysis including GO, KEGG, and Gene Set Enrichment Analysis (GSEA)
Statistical methods for batch effect correction and microarray preprocessing

Use Cases

Identifying differentially expressed genes and biomarkers from high-throughput sequencing data
Interpreting biological significance through automated pathway mapping and ontology enrichment
Clustering and visualizing single-cell transcriptomics to identify cell-type specific expression patterns

About

Key Features

End-to-end single-cell RNA-seq processing using the Seurat workflow
0 GitHub stars
Automated RNA-seq differential expression workflows using DESeq2, edgeR, and limma-voom
Advanced genomic visualizations including EnhancedVolcano, ComplexHeatmap, and PCA plots
Functional enrichment analysis including GO, KEGG, and Gene Set Enrichment Analysis (GSEA)
Statistical methods for batch effect correction and microarray preprocessing

Use Cases

Identifying differentially expressed genes and biomarkers from high-throughput sequencing data
Interpreting biological significance through automated pathway mapping and ontology enrichment
Clustering and visualizing single-cell transcriptomics to identify cell-type specific expression patterns