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Pathway-directed weighted testing procedures for the integrative analysis of gene expression and metabolomic data

机译：用于基因表达和代谢组学数据综合分析的通路导向加权测试程序

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摘要

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.
机译：我们探索p值加权的实用程序，以增强在两个样本的设置中检测差异代谢物的能力。相关的基因表达信息用于为每个待测代谢物分配先验重要性水平。我们通过途径将基因表达映射到代谢产物，然后使用基因集富集测试按途径总结基因表达信息。通过仿真，我们探索了四种富集测试样式和四个权重函数，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p值加权。匹配样品上的基因表达用于构建权重。在某些监管条件下，除非在高相关情况下，否则使用加权p值不会使I型错误高于未加权测试所看到的值。当通路成员重叠时，在通路不相交的情况下，检测差异代谢物的能力显着提高，并且相对于富集通路的比例而言，其显示出适度的改善。

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