In biomedical research, multiple endpoints are commonly analyzed in “omics” fields like genomics, proteomics and metabolomics. Traditional methods designed for low-dimensional data either perform poorly or are not applicable when analyzing high-dimensional data whose dimension is generally similar to, or even much larger than, the number of subjects. The complex biochemical interplay between hundreds (or thousands) of endpoints is reflected by complex dependence relations. The aim of the paper is to propose tests that are very suitable for analyzing omics data because they do not require the normality assumption, are powerful also for small sample sizes, in the presence of complex dependence relations among endpoints, and when the number of endpoints is much larger than the number of subjects. Unbiasedness and consistency of the tests are proved and their size and power are assessed numerically. It is shown that the proposed approach based on the nonparametric combination of dependent interpoint distance tests is very effective. Applications to genomics and metabolomics are discussed.

中文翻译：

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用于比较多个端点与组学数据应用的测试

在生物医学研究中，通常在基因组学、蛋白质组学和代谢组学等“组学”领域分析多个端点。为低维数据设计的传统方法在分析维度通常与主题数量相似甚至远大于主题数量的高维数据时表现不佳或不适用。数百个（或数千个）端点之间复杂的生化相互作用反映在复杂的依赖关系上。本文的目的是提出非常适合分析组学数据的测试，因为它们不需要正态性假设，对于小样本量也很强大，在端点之间存在复杂的依赖关系的情况下，以及当端点的数量远远大于科目的数量。证明了测试的无偏性和一致性，并对它们的大小和功效进行了数值评估。结果表明，所提出的基于相关点间距离测试的非参数组合的方法非常有效。讨论了基因组学和代谢组学的应用。