idr: Irreproducible discovery rate. This is a package for estimating the copula mixture model and plotting correspondence curves in ”Measuring reproducibility of high-throughput experiments” (2011), Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779, by Li, Brown, Huang, and Bickel: Reproducibility is essential to reliable scientific discovery in high-throughput experiments. 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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References in zbMATH (referenced in 7 articles , 1 standard article )
Showing results 1 to 7 of 7.
- Arias-Castro, Ery; Huang, Rong; Verzelen, Nicolas: Detection of sparse positive dependence (2020)
- Liang, Yulan; Kelemen, Adam; Kelemen, Arpad: Reproducibility of biomarker identifications from mass spectrometry proteomic data in cancer studies (2019)
- Philtron, Daisy; Lyu, Yafei; Li, Qunhua; Ghosh, Debashis: Maximum rank reproducibility: a nonparametric approach to assessing reproducibility in replicate experiments (2018)
- Anders Bilgrau; Poul Eriksen; Jakob Rasmussen; Hans Johnsen; Karen Dybkaer; Martin Boegsted: GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models (2016) not zbMATH
- Segers, Johan; van den Akker, Ramon; Werker, Bas J. M.: Semiparametric Gaussian copula models: geometry and efficient rank-based estimation (2014)
- Zhang, Qian; Zhang, Junping; Xue, Chenghai: Measuring reproducibility of high-throughput deep-sequencing experiments based on self-adaptive mixture copula (2013) ioport
- Li, Qunhua; Brown, James B.; Huang, Haiyan; Bickel, Peter J.: Measuring reproducibility of high-throughput experiments (2011)