Li Danning, Xue Lingzhou, Yang Haoyi, Yu Xiufan
KLAS and School of Mathematics & Statistics, Northeast Normal University, Changchun, Jilin 130024, China.
Department of Statistics, The Pennsylvania State University, University Park, PA 16802, USA.
Biometrics. 2025 Apr 2;81(2). doi: 10.1093/biomtc/ujaf034.
Testing differences in mean vectors is a fundamental task in the analysis of high-dimensional microbiome compositional data. Existing methods may suffer from low power if the underlying signal pattern is in a situation that does not favor the deployed test. In this work, we develop 2-sample power-enhanced mean tests for high-dimensional compositional data based on the combination of $P$-values, which integrates strengths from 2 popular types of tests: the maximum-type test and the quadratic-type test. We provide rigorous theoretical guarantees on the proposed tests, showing accurate Type-I error rate control and enhanced testing power. Our method boosts the testing power toward a broader alternative space, which yields robust performance across a wide range of signal pattern settings. Our methodology and theory also contribute to the literature on power enhancement and Gaussian approximation for high-dimensional hypothesis testing. We demonstrate the performance of our method on both simulated data and real-world microbiome data, showing that our proposed approach improves the testing power substantially compared to existing methods.
检验均值向量的差异是高维微生物群落组成数据分析中的一项基本任务。如果潜在信号模式处于不利于所采用检验的情形，现有方法可能会出现检验功效较低的问题。在这项工作中，我们基于(P)值的组合，为高维组成数据开发了双样本功效增强均值检验，该检验整合了两种常用检验类型（最大型检验和二次型检验）的优势。我们为所提出的检验提供了严格的理论保证，表明其能准确控制第一类错误率并增强检验功效。我们的方法将检验功效提升至更广泛的备择空间，在广泛的信号模式设置下都能产生稳健的性能。我们的方法和理论也为高维假设检验中功效增强和高斯近似的文献做出了贡献。我们在模拟数据和真实世界的微生物群落数据上展示了我们方法的性能，结果表明与现有方法相比，我们提出的方法显著提高了检验功效。
