Measuring reproducibility of high-throughput experiments

TL;DR

This paper introduces a new method to measure reproducibility in high-throughput experiments using a curve fitted by a copula mixture model, providing a quantitative score called IDR for assessing and thresholding reproducibility.

Contribution

It proposes a unified, curve-based reproducibility measure with a copula mixture model, enabling principled reproducibility assessment and combining replicates across arbitrary scales.

Findings

The method effectively assesses reproducibility in simulations.

It provides a quantitative score (IDR) for reproducibility.

Demonstrated success 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.