LAUSR

Ensuring reproducibility of results in high-throughput experiments is crucial for biomedical research. Here, we propose a set of computational methods, INTRIGUE, to evaluate and control reproducibility in high-throughput settings. Our approaches are built on a new definition of reproducibility that emphasizes directional consistency when experimental units are assessed with signed effect size estimates. The proposed methods are designed to (1) assess the overall reproducible quality of multiple studies and (2) evaluate reproducibility at the individual experimental unit levels. We demonstrate the proposed methods in detecting unobserved batch effects via simulations. We further illustrate the versatility of the proposed methods in transcriptome-wide association studies: in addition to reproducible quality control, they are also suited to investigating genuine biological heterogeneity. Finally, we discuss the potential extensions of the proposed methods in other vital areas of reproducible research (for example, publication bias and conceptual replications).