The most common approach in the comparative analysis of transcriptomics data is to test the null hypothesis that the logarithmic fold change (LFC) between treatment and control for a gene’s expression is exactly zero, i.e., that the gene is not at all affected by the treatment. Often the goal of differential analysis is to produce a list of genes passing multiple-test adjustment, ranked by P value. However, small changes, even if statistically highly significant, might not be the most interesting candidates for further investigation. Ranking by fold change, on the other hand, is complicated by the noisiness of LFC estimates for genes with low counts. Furthermore, the number of genes called significantly differentially expressed depends as much on the sample size and other aspects of experimental design as it does on the biology of the experiment – and well-powered experiments often generate an overwhelmingly long list of hits [9]. We, therefore, developed a statistical framework to facilitate gene ranking and visualization based on stable estimation of effect sizes (LFCs), as well as testing of differential expression with respect to user-defined thresholds of biological significance.
