A direct approach to estimating false discovery rates conditional on covariates
A direct approach to estimating false discovery rates conditional on covariates
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DOI：
10.7717/peerj.6035
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发表时间：
2018-12-10
期刊：
影响因子：
2.7
通讯作者：
Leek, Jeffrey T.
中科院分区：
文献类型：
--
作者：
Boca, Simina M.;Leek, Jeffrey T.
来自许多不同研究领域的现代科学研究充斥着多个假设检验问题。错误发现率（FDR）是在执行多个测试时测量和控制错误率的最常用方法之一。自适应FDR依赖于对所有被测试的假设中的零假设的比例的估计。这个比例通常对每个假设集合估计一次。在这里，我们提出了一个回归框架来估计零假设的比例条件观察到的协变量。然后，这可以用作Benjamini-Hochberg调整的p值的乘法因子，从而得到插入式FDR估计器。我们将我们的方法应用于体重指数的基因组关联荟萃分析。在我们的框架中，我们能够使用单个基因组位点的样本量和次要等位基因频率作为协变量。我们进一步评估我们的方法通过一些模拟场景。我们提供了一个实现这种新的方法，估计零假设的比例在一个回归框架的一部分，Bioconductor包swfdr。
Modern scientific studies from many diverse areas of research abound with multiple hypothesis testing concerns. The false discovery rate (FDR) is one of the most commonly used approaches for measuring and controlling error rates when performing multiple tests. Adaptive FDRs rely on an estimate of the proportion of null hypotheses among all the hypotheses being tested. This proportion is typically estimated once for each collection of hypotheses. Here, we propose a regression framework to estimate the proportion of null hypotheses conditional on observed covariates. This may then be used as a multiplication factor with the Benjamini-Hochberg adjusted p-values, leading to a plug-in FDR estimator. We apply our method to a genome-wise association meta-analysis for body mass index. In our framework, we are able to use the sample sizes for the individual genomic loci and the minor allele frequencies as covariates. We further evaluate our approach via a number of simulation scenarios. We provide an implementation of this novel method for estimating the proportion of null hypotheses in a regression framework as part of the Bioconductor package swfdr.