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

Daisy Philtron, Yafei Lyu, Qunhua Li, Debashis Ghosh

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)1028-1039
Number of pages12
JournalJournal of the American Statistical Association
Volume113
Issue number523
DOIs
StatePublished - Jul 3 2018

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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