Abstract
The high-dimensional and heterogeneous nature of transcriptomics data from RNA sequencing (RNA-Seq) experiments poses a challenge to routine downstream analysis steps, such as differential expression analysis and enrichment analysis. Additionally, due to practical and financial constraints, RNA-Seq experiments are often limited to a small number of biological replicates. In light of recent studies on the low replicability of preclinical cancer research, it is essential to understand how the combination of population heterogeneity and underpowered cohort sizes affects the replicability of RNA-Seq research. Using 18'000 subsampled RNA-Seq experiments based on real gene expression data from 18 different data sets, we find that differential expression and enrichment analysis results from underpowered experiments are unlikely to replicate well. However, low replicability does not necessarily imply low precision of results, as data sets exhibit a wide range of possible outcomes. In fact, 10 out of 18 data sets achieve high median precision despite low recall and replicability for cohorts with more than five replicates. To assist researchers constrained by small cohort sizes in estimating the expected performance regime of their data sets, we provide a simple bootstrapping procedure that correlates strongly with the observed replicability and precision metrics. We conclude with practical recommendations to alleviate problems with underpowered RNA-Seq studies.