Identify differential expression in bulk RNA-seq count matrices using DESeq2.
npx skills add https://github.com/gexijin/vitiligo --skill rnaseq-analysis
Please help me install this Agent Skill. Skill: rnaseq-analysis Source: https://github.com/gexijin/vitiligo/tree/main/.claude/skills/rnaseq-analysis Command: npx skills add https://github.com/gexijin/vitiligo --skill rnaseq-analysis
Analyze bulk RNA-seq count data to perform QC, normalization, differential expression testing, and gene ID mapping using DESeq2, enabling reproducible end-to-end analysis workflows.
Analyze the provided RNA-seq count matrix by performing QC, normalization, DESeq2 modeling, and annotating results.
High-intent search queries and answers about installing and using this skill.
Differential expression analysis on RNA-seq count data using DESeq2 involves applying the tool to a count matrix to perform QC, normalization, and modeling, yielding results with shrunken LFC and annotated gene IDs for downstream interpretation.
Yes, paired design bulk RNA-seq data with batch effects can be analyzed using DESeq2 multi-factor models. The workflow supports both unpaired and paired designs, outputting DE results with shrunken LFC via the apeglm or normal method.
RNA-seq differential expression supports count matrices from raw counts, featureCounts output, RSEM counts, or GEO-derived series like GSE####, requiring proper data formatting and the here package for portable paths.
GO and KEGG enrichment after RNA-seq differential expression is performed using clusterProfiler on annotated gene IDs. The workflow outputs DE results with shrunken LFC, annotated genes, PCA plots, and heatmaps for downstream interpretation.
Mapping gene IDs for RNA-seq DESeq2 results is handled via AnnotationDbi, org.Hs.eg.db, and biomaRt. The workflow annotates differential expression results, providing annotated gene IDs required for downstream visualizations and enrichment analysis.
Yes, DESeq2 works with GEO series for bulk RNA-seq normalization by processing GEO-derived series like GSE####. The workflow applies QC, normalization, and DESeq2 modeling to generate DE results with shrunken LFC and plots.
omicverseConverts bulk RNA-seq FASTQ or SRA inputs into gene-level count matrices.
StarlitnightlyGenerate publication-quality plots with matplotlib and seaborn.
ya-wayGenerate MA plots, heatmaps, and PCA from DESeq2 or edgeR results.
HolobiomicsLabPerform end-to-end differential expression analysis on bulk RNA-seq count data with PyDESeq2 workflows.
