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limma-voom — linear models for differential expression analysis of RNA-seq and microarray data. The voom transformation converts RNA-seq read counts to log-CPM with precision weights derived from the
Use with AI
Install the MCP server or CLI to instantly fetch limma-voom documentation:
Install command
claude mcp add biocontext7 -- npx @biocontext7/mcpOr share this page: biocontext7.com/tools/limma
ballgown -- R/Bioconductor package for flexible isoform-level differential expression analysis of RNA-seq experiments. Works with StringTie output (.ctab files) to test for differential expression at
2 shared topics • 1 shared operation
Differential expression analysis for RNA-seq data using negative binomial generalized linear models with size factor normalization and empirical Bayes shrinkage.
2 shared topics • 1 shared operation
Use when performing differential gene expression analysis on RNA-seq count data using limma-voom. Covers the full workflow: count filtering with filterByExpr, TMM normalization via edgeR calcNormFacto
2 shared topics • 1 shared operation
Use when working with muscat — the Bioconductor R package for multi-sample multi-condition differential state (DS) analysis of single-cell RNA-seq data. Performs pseudo-bulk aggregation (aggregateData
2 shared topics • 1 shared operation
PyDESeq2 — Python implementation of DESeq2 for differential gene expression analysis from bulk RNA-seq count data. Performs size factor normalization, genewise dispersion estimation, Wald tests with B
2 shared topics • 1 shared operation