onlineFDR: an R package to control the false discovery rate for growing data repositories

Author:

Robertson David S1ORCID,Wildenhain Jan2,Javanmard Adel3,Karp Natasha A2

Affiliation:

1. MRC Biostatistics Unit, University of Cambridge, Cambridge, UK

2. Quantitative Biology, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Cambridge, UK

3. Department of Data Sciences and Operations, University of Southern California, Los Angeles, CA, USA

Abstract

Abstract Summary In many areas of biological research, hypotheses are tested in a sequential manner, without having access to future P-values or even the number of hypotheses to be tested. A key setting where this online hypothesis testing occurs is in the context of publicly available data repositories, where the family of hypotheses to be tested is continually growing as new data is accumulated over time. Recently, Javanmard and Montanari proposed the first procedures that control the FDR for online hypothesis testing. We present an R package, onlineFDR, which implements these procedures and provides wrapper functions to apply them to a historic dataset or a growing data repository. Availability and implementation The R package is freely available through Bioconductor (http://www.bioconductor.org/packages/onlineFDR). Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Medical Research Council

Biometrika Trust

Outlier Research in Business

USC Marshall School of Business

Google Faculty Research

NSF-CAREER

AstraZeneca

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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