Many methods for differential expression analysis of RNA-seq data perform such information sharing across genes for variance (or, equivalently, dispersion) estimation. edgeR [2,3] moderates the dispersion estimate for each gene toward a common estimate across all genes, or toward a local estimate from genes with similar expression strength, using a weighted conditional likelihood. Our DESeq method [4] detects and corrects dispersion estimates that are too low through modeling of the dependence of the dispersion on the average expression strength over all samples. BBSeq [5] models the dispersion on the mean, with the mean absolute deviation of dispersion estimates used to reduce the influence of outliers. DSS [6] uses a Bayesian approach to provide an estimate for the dispersion for individual genes that accounts for the heterogeneity of dispersion values for different genes. baySeq [7] and ShrinkBayes [8] estimate priors for a Bayesian model over all genes, and then provide posterior probabilities or false discovery rates (FDRs) for differential expression.
