Power-Enhanced Two-Sample Mean Tests for High-Dimensional Compositional Data with Application to Microbiome Data Analysis
arxiv(2024)
摘要
Testing differences in mean vectors is a fundamental task in the analysis of
high-dimensional 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 two-sample power-enhanced mean tests
for high-dimensional compositional data based on the combination of p-values,
which integrates strengths from two 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 towards a broader
alternative space, which yields robust performance across a wide range of
signal pattern settings. Our theory also contributes 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.
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