Improving false discovery rate estimation
Improving false discovery rate estimation
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DOI：
10.1093/bioinformatics/bth160
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发表时间：
2004-07-22
期刊：
影响因子：
5.8
通讯作者：
Cheng, C
中科院分区：
文献类型：
--
作者：
Pounds, S;Cheng, C
动机：最近在微阵列数据分析中考虑多重测试的尝试主要集中在控制错误发现率（FDR）上。然而，将 FDR 严格控制在预选水平通常是不切实际的。因此，有人建议使用 q 值来估计一组重要发现中错误发现的比例。然而，考虑到 q 值基于正 FDR (pFDR) 的不稳定估计量，这样的 q 值解释可能是没有根据的。另一种方法建议通过对源自 beta 均匀混合 (BUM) 分布的 p 值进行建模来估计 FDR。不幸的是，BUM 方法仅在假设模型准确表示 p 值实际分布的情况下才可靠。 方法：提出了一种称为间距 LOESS 直方图 (SPLOSH) 的方法，用于估计条件 FDR (cFDR)，即以具有 k 个“显着”结果为条件的误报的预期比例。 SPLOSH 被设计为比 q 值更稳定，并且适用于比 BUM 更广泛的设置。结果：在模拟研究和数据分析示例中，SPLOSH 表现出相对于 q 值和 BUM 的所需特性。
Motivation: Recent attempts to account for multiple testing in the analysis of microarray data have focused on controlling the false discovery rate (FDR). However, rigorous control of the FDR at a preselected level is often impractical. Consequently, it has been suggested to use the q-value as an estimate of the proportion of false discoveries among a set of significant findings. However, such an interpretation of the q-value may be unwarranted considering that the q-value is based on an unstable estimator of the positive FDR (pFDR). Another method proposes estimating the FDR by modeling p-values as arising from a beta-uniform mixture (BUM) distribution. Unfortunately, the BUM approach is reliable only in settings where the assumed model accurately represents the actual distribution of p-values.Methods: A method called the spacings LOESS histogram (SPLOSH) is proposed for estimating the conditional FDR (cFDR), the expected proportion of false positives conditioned on having k 'significant' findings. SPLOSH is designed to be more stable than the q-value and applicable in a wider variety of settings than BUM.Results: In a simulation study and data analysis example, SPLOSH exhibits the desired characteristics relative to the q-value and BUM.