DEPower: approximate power analysis with DESeq2

Abstract

Rigorous experimental design, including formal power analysis, is a cornerstone of reproducible RNA sequencing (RNA-seq) research. The design of RNA-seq experiments requires computing the minimum sample number required to identify an effect of a particular size at a predefined significance level. Ideally, the statistical test used for the analysis of experimental data should match the test used for sample size determination; however, few tools use the assumptions of the popular differential expression testing framework DESeq2, and most opt for simulation-based rather than analytical approaches. Grounded in the DESeq2 model framework, we derive sample size requirements for both single-cell and bulk RNA-seq experiments delivered as a web-based tool for power analysis, DEPower, available at https://poweranalysis-fb.streamlit.app/ that makes rigorous RNA-seq study design accessible to all researchers.