Figure 1.

Schematic of the major components that are central to any limma analysis. For each gene g, we have a vector of gene expression values (y_g) and a design matrix X that relates these values to some coefficients of interest (β_g). The limma package includes statistical methods that (i) facilitate information borrowing using empirical Bayes methods to obtain posterior variance estimators (), (ii) incorporate observation weights (w_gj where j refers to sample) to allow for variations in data quality, (iii) allow variance modelling to accommodate technical or biological heterogeneity that may be present and (iv) pre-processing methods such as variance stabilization to reduce noise. These methods all help improve inference at both the gene and gene set level in small experiments.