Precision of fold change estimates We benchmarked the DESeq2 approach of using an empirical prior to achieve shrinkage of LFC estimates against two competing approaches: the GFOLD method, which can analyze experiments without replication [20] and can also handle experiments with replicates, and the edgeR package, which provides a pseudocount-based shrinkage termed predictive LFCs. Results are summarized in Additional file 1: Figures S12–S16. DESeq2 had consistently low root-mean-square error and mean absolute error across a range of sample sizes and models for a distribution of true LFCs. GFOLD had similarly low error to DESeq2 over all genes; however, when focusing on differentially expressed genes, it performed worse for larger sample sizes. edgeR with default settings had similarly low error to DESeq2 when focusing only on the differentially expressed genes, but had higher error over all genes.
