Pascovici Dana, Handler David C L, Wu Jemma X, Haynes Paul A
Australian Proteome Analysis Facility, Macquarie University, Sydney, Australia.
Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, Australia.
Proteomics. 2016 Sep;16(18):2448-53. doi: 10.1002/pmic.201600044.
Multiple testing corrections are a useful tool for restricting the FDR, but can be blunt in the context of low power, as we demonstrate by a series of simple simulations. Unfortunately, in proteomics experiments low power can be common, driven by proteomics-specific issues like small effects due to ratio compression, and few replicates due to reagent high cost, instrument time availability and other issues; in such situations, most multiple testing corrections methods, if used with conventional thresholds, will fail to detect any true positives even when many exist. In this low power, medium scale situation, other methods such as effect size considerations or peptide-level calculations may be a more effective option, even if they do not offer the same theoretical guarantee of a low FDR. Thus, we aim to highlight in this article that proteomics presents some specific challenges to the standard multiple testing corrections methods, which should be employed as a useful tool but not be regarded as a required rubber stamp.
多重检验校正对于控制错误发现率(FDR)是一种有用的工具,但正如我们通过一系列简单模拟所表明的那样,在功效较低的情况下可能不够精准。不幸的是,在蛋白质组学实验中,由于蛋白质组学特有的问题,如因比率压缩导致的效应较小,以及由于试剂成本高、仪器时间有限和其他问题导致的重复样本较少,低功效情况可能很常见;在这种情况下,大多数多重检验校正方法,如果使用传统阈值,即使存在许多真正的阳性结果,也将无法检测到任何一个。在这种低功效、中等规模的情况下,其他方法,如效应大小考量或肽水平计算,可能是更有效的选择,即使它们不能提供与低错误发现率相同的理论保证。因此,我们旨在在本文中强调,蛋白质组学对标准多重检验校正方法提出了一些特定挑战,这些方法应作为一种有用的工具使用,但不应被视为必需的橡皮图章。