C-T.40: Are small RNA-Seq cohorts reliable? Navigating replicability and precision
Peter Degen
University of Zurich
Keywords
RNA-Seq; differential expression analysis; enrichment analysis; population heterogeneity; benchmarking
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The high-dimensional and heterogeneous nature of transcriptomics data from RNA sequencing (RNA-Seq) experiments presents a persistent challenge for standard downstream pipelines, such as differential expression and enrichment analysis. In preclinical research, these challenges are often compounded by small cohort sizes dictated by financial and practical constraints. 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 gene expression data from 18 high-quality real-world datasets, we systematically measured the impact of sample size on replicability, precision, and recall. We find that results from underpowered experiments are unlikely to replicate well. However, low replicability does not inherently imply low precision, as the analyzed datasets exhibit a wide range of possible outcomes. In fact, 10 out of 18 datasets yield high median precision despite low recall and replicability when the cohort size is five or more. To assist researchers constrained by small cohort sizes in estimating the expected performance regime of their datasets, we introduce a simple bootstrapping procedure that accurately predicts observed replicability and precision metrics. We conclude with actionable recommendations to improve the robustness of underpowered RNA-Seq studies.
Co-authors: Peter Degen
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