onlineFDR: an R package to control the false discovery rate for growing data repositories
onlineFDR: an R package to control the false discovery rate for growing data repositories
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DOI：
10.1093/bioinformatics/btz191
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发表时间：
2019-10-15
期刊：
影响因子：
5.8
通讯作者：
Karp, Natasha A.
中科院分区：
文献类型：
--
作者：
Robertson, David S.;Wildenhain, Jan;Karp, Natasha A.
在生物学研究的许多领域中，假设是以顺序的方式进行测试的，无法获得未来的p值，甚至无法获得待测试假设的数量。这种在线假设检验发生的一个关键环境是在公开可用的数据存储库的背景下，随着时间的推移，新数据的积累，需要测试的假设家族不断增长。最近，Javanmard和Montanari提出了第一个控制FDR的在线假设检验程序。我们提供了一个R包，onlineFDR，它实现了这些过程，并提供了包装器函数将它们应用到历史数据集或不断增长的数据存储库中。
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.