Department of Public Health Sciences, Henry Ford Hospital, Detroit, MI, USA.
Genomics. 2012 May;99(5):265-74. doi: 10.1016/j.ygeno.2012.03.004. Epub 2012 Apr 2.
We explore the utility of p-value weighting for enhancing the power to detect differential metabolites in a two-sample setting. Related gene expression information is used to assign an a priori importance level to each metabolite being tested. We map the gene expression to a metabolite through pathways and then gene expression information is summarized per-pathway using gene set enrichment tests. Through simulation we explore four styles of enrichment tests and four weight functions to convert the gene information into a meaningful p-value weight. We implement the p-value weighting on a prostate cancer metabolomic dataset. Gene expression on matched samples is used to construct the weights. Under certain regulatory conditions, the use of weighted p-values does not inflate the type I error above what we see for the un-weighted tests except in high correlation situations. The power to detect differential metabolites is notably increased in situations with disjoint pathways and shows moderate improvement, relative to the proportion of enriched pathways, when pathway membership overlaps.
我们探索了 p 值加权在提高双样本中检测差异代谢物的功效方面的应用。利用相关的基因表达信息，为每个被检测的代谢物预先分配一个重要性水平。我们通过途径将基因表达映射到代谢物上，然后使用基因集富集测试对每个途径的基因表达信息进行总结。通过模拟，我们探索了四种富集测试和四种权重函数，以将基因信息转换为有意义的 p 值权重。我们在前列腺癌代谢组学数据集上实现了 p 值加权。使用匹配样本的基因表达来构建权重。在某些调节条件下，除非在高相关性情况下，否则使用加权 p 值不会导致错误发现率（type I error）超过未加权测试的水平。在具有不相交途径的情况下，检测差异代谢物的功效显著提高，并且当途径成员重叠时，与富集途径的比例相比，有适度的提高。
