The limma pipeline includes linear modeling to analyze complex experiments with multiple treatment factors, quantitative weights to account for variations in precision between different observations, and empirical Bayes statistical methods to borrow strength between genes.īorrowing information between genes is a crucial feature of the genome-wide statistical methods, as it allows for gene-specific variation while still providing reliable inference with small sample sizes. One popular differential expression pipeline is that provided by the limma software package. This includes methods for differential expression analysis, random effects, gene set enrichment, gene set testing and so on. For most of the past 16 years or more, DNA microarrays have been the premier technology for genome-wide gene expression experiments, and a large body of mature statistical methods and tools has been developed to analyze intensity data from microarrays. Gene expression profiling is one of the most commonly used genomic techniques in biological research.
