LIMMAPaket

LIMMAPaket is a software package for the statistical analysis of gene expression data using linear modeling techniques. It implements the LIMMA framework to identify differentially expressed genes across experimental conditions and is designed for researchers in genomics and bioinformatics working in the R environment.

Key features include support for diverse experimental designs (paired samples, factorial designs, batch effects), data normalization and transformation options (including voom for RNA-Seq count data), linear model fitting, empirical Bayes moderation of gene-wise variances, and specification of contrasts. The package provides tools for multiple testing correction and for summarizing results.

Input consists of an expression matrix with genes as rows and samples as columns, along with a sample metadata table describing conditions, blocks, and other factors. Output includes per-gene statistics such as log-fold changes, standard errors, moderated t-statistics, p-values, adjusted p-values, as well as summary tables and diagnostic plots (MA plots, volcano plots, heatmaps).

Workflow: prepare data, define a design matrix, fit the linear model, apply contrasts of interest, use empirical Bayes moderation, and extract top results. LIMMAPaket emphasizes reproducibility and interoperability with other Bioconductor tools.

Implementation and availability: LIMMAPaket is open-source and maintained on a public repository. It is implemented in R and integrates with common Bioconductor workflows, with documentation and tutorials to support users. The package serves as a reference implementation for LIMMA-based differential expression analysis and is used in teaching and research contexts.