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

• 成果类型: Article
• 署名作者: Philtron, Daisy; Lyu, Yafei; Li, Qunhua; Ghosh, Debashis
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Colorado School of Public Health; University of Colorado System; University of Colorado Anschutz Medical Campus
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2017.1397521
• 发表日期: 2018
• 页码: 1028-1039
• 关键词: false discovery rate

摘要

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.