The article reviews the limma software package, which is an R/Bioconductor tool for analyzing gene expression data from various platforms, including microarrays and RNA sequencing (RNA-seq). Limma is designed to handle complex experimental designs and small sample sizes by leveraging information borrowing through empirical Bayes methods. Recent enhancements include the ability to perform differential splicing analyses for RNA-seq data and higher-level analyses of gene expression signatures. The package integrates statistical principles such as linear modeling, empirical Bayes, and variance modeling to improve inference in small experiments. It also supports pre-processing methods for background correction and normalization, and provides tools for visualizing and interpreting results, including volcano plots, Venn diagrams, and gene set enrichment analyses. The article emphasizes the flexibility and robustness of limma in handling various types of data and experimental designs, making it a valuable resource for researchers in molecular biology and genomics.The article reviews the limma software package, which is an R/Bioconductor tool for analyzing gene expression data from various platforms, including microarrays and RNA sequencing (RNA-seq). Limma is designed to handle complex experimental designs and small sample sizes by leveraging information borrowing through empirical Bayes methods. Recent enhancements include the ability to perform differential splicing analyses for RNA-seq data and higher-level analyses of gene expression signatures. The package integrates statistical principles such as linear modeling, empirical Bayes, and variance modeling to improve inference in small experiments. It also supports pre-processing methods for background correction and normalization, and provides tools for visualizing and interpreting results, including volcano plots, Venn diagrams, and gene set enrichment analyses. The article emphasizes the flexibility and robustness of limma in handling various types of data and experimental designs, making it a valuable resource for researchers in molecular biology and genomics.