Measuring reproducibility of high-throughput experiments

TLDR

Reproducibility is essential to reliable scientific discovery in high‑throughput experiments. The study proposes a unified method to measure reproducibility of findings from replicate high‑throughput experiments and to identify putative discoveries. The method constructs a reproducibility curve fitted with a copula mixture model, yielding an irreproducible discovery rate (IDR) score that can be applied to paired replicate ranks to set principled thresholds, and its performance is evaluated through simulations and theoretical analysis. The approach yields a flexible reproducibility score applicable across diverse settings and demonstrates effectiveness in a ChIP‑seq experiment.

Abstract

Reproducibility is essential to reliable scientific discovery in high-throughput experiments. In this work we propose a unified approach to measure the reproducibility of findings identified from replicate experiments and identify putative discoveries using reproducibility. Unlike the usual scalar measures of reproducibility, our approach creates a curve, which quantitatively assesses when the findings are no longer consistent across replicates. Our curve is fitted by a copula mixture model, from which we derive a quantitative reproducibility score, which we call the "irreproducible discovery rate" (IDR) analogous to the FDR. This score can be computed at each set of paired replicate ranks and permits the principled setting of thresholds both for assessing reproducibility and combining replicates. Since our approach permits an arbitrary scale for each replicate, it provides useful descriptive measures in a wide variety of situations to be explored. We study the performance of the algorithm using simulations and give a heuristic analysis of its theoretical properties. We demonstrate the effectiveness of our method in a ChIP-seq experiment.