Maximum Rank Reproducibility: A Nonparametric Approach to Assessing Reproducibility in Replicate Experiments

Daisy Philtron, Yafei Lyu, Qunhua Li and Debashis Ghosh

Journal of the American Statistical Association, 2018, vol. 113, issue 523, 1028-1039

Abstract: The identification of reproducible signals from the results of replicate high-throughput experiments is an important part of modern biological research. Often little is known about the dependence structure and the marginal distribution of the data, motivating the development of a nonparametric approach to assess reproducibility. The procedure, which we call the maximum rank reproducibility (MaRR) procedure, uses a maximum rank statistic to parse reproducible signals from noise without making assumptions about the distribution of reproducible signals. Because it uses the rank scale this procedure can be easily applied to a variety of data types. One application is to assess the reproducibility of RNA-seq technology using data produced by the sequencing quality control (SEQC) consortium, which coordinated a multi-laboratory effort to assess reproducibility across three RNA-seq platforms. Our results on simulations and SEQC data show that the MaRR procedure effectively controls false discovery rates, has desirable power properties, and compares well to existing methods. Supplementary materials for this article are available online.

Date: 2018
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DOI: 10.1080/01621459.2017.1397521

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