While absolute quantification is challenging in high-throughput measurements, changes of features between conditions can often be determined with high precision. Differential analyses of such changes, or even of "changes of changes" are therefore crucial. Differential alternative splicing is an example of a doubly differential analysis, i.e. fold changes between conditions for different isoforms of a gene.
EmpiRe is a general approach for various kinds of omics data based on fold changes for appropriate features of biological objects. Empirical error distributions for these fold changes are estimated from Replicate measurements. Using these distributions feature changes and their directions are quantified and made comparable. We assess the performance of EmpiRe to detect differentially expressed genes applied to RNA-Seq using simulated data.
It achieved higher precision than established tools at nearly the same recall level. Furthermore, we assess the detection of alternatively Spliced genes via changes of isoform fold changes (EmpiReS). Again simulations, in addition to a few experimentally validated splicing events, are used. EmpiReS achieves the best precision-recall values for simulations based on different biological datasets. We propose EmpiRe(S) as a general, simple and fast approach, which makes the most out of object fold changes for all (doubly) differential analyses.