Open Access
September 2011 Measuring reproducibility of high-throughput experiments
Qunhua Li, James B. Brown, Haiyan Huang, Peter J. Bickel
Ann. Appl. Stat. 5(3): 1752-1779 (September 2011). DOI: 10.1214/11-AOAS466


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.


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Qunhua Li. James B. Brown. Haiyan Huang. Peter J. Bickel. "Measuring reproducibility of high-throughput experiments." Ann. Appl. Stat. 5 (3) 1752 - 1779, September 2011.


Published: September 2011
First available in Project Euclid: 13 October 2011

zbMATH: 1231.62124
MathSciNet: MR2884921
Digital Object Identifier: 10.1214/11-AOAS466

Keywords: association , copula , genomics , high-throughput experiment , irreproducible discovery rate , iterative algorithm , mixture model , reproducibility

Rights: Copyright © 2011 Institute of Mathematical Statistics

Vol.5 • No. 3 • September 2011
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