在线 FDR：一个用于控制增长型数据存储库的假发现率的 R 包。
onlineFDR: an R package to control the false discovery rate for growing data repositories.
机构信息
MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Quantitative Biology, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Cambridge, UK.
出版信息
Bioinformatics. 2019 Oct 15;35(20):4196-4199. doi: 10.1093/bioinformatics/btz191.
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.
摘要
在许多生物学研究领域，假设都是以连续的方式进行检验的，无法获得未来的 P 值，甚至无法获得要检验的假设数量。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中，随着时间的推移，随着新数据的积累，要检验的假设族不断增加。最近，Javanmard 和 Montanari 提出了第一个控制 FDR 的在线假设检验程序。我们提出了一个 R 包 onlineFDR，它实现了这些程序，并提供了包装函数，以便将它们应用于历史数据集或不断增长的数据存储库。
可用性和实现
R 包可通过 Bioconductor（http://www.bioconductor.org/packages/onlineFDR）免费获得。
补充信息
补充数据可在 Bioinformatics 在线获得。
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