Robust Microbial Signature Discovery via Post-Selection Inference for Microbiome Compositions

• 成果类型: Article; Early Access
• 署名作者: Wang, Weihao; Xu, Xiangnan; Zhao, Hongyu; Wang, Tao
• 署名单位: Shanghai Jiao Tong University; University of Sydney; Yale University; Shanghai Jiao Tong University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2671446
• 发表日期: 2026-06-19
• 关键词: Compositional bias; data splitting; Mean-shift models; Post-selection Inference; Summary statistics; MODEL

摘要

Identifying taxa associated with host phenotypes is crucial for understanding host-microbe interactions and their underlying molecular mechanisms. However, analyzing microbiome data presents unique challenges, as the observed abundances of taxa are high-dimensional, compositional, and subject to both sample-specific and taxon-specific biases. Many existing methods for differential abundance testing struggle to balance false discovery rate control with statistical power. In this article, we propose PoDA, a post-selection inference method for differential abundance analysis to address the limitations of the existing methods. PoDA begins by selecting a subset of taxa likely associated with the phenotype using penalized regression under a mean-shift model. It then leverages the unselected taxa to correct for sample-specific bias and assign p-values to the selected taxa. To ensure valid inference after the selection process, PoDA employs an information-splitting procedure, which is repeated to enhance stability and power. Comprehensive simulation studies demonstrate the superiority of PoDA over existing methods. We further applied PoDA to two microbiome case-control studies of Parkinson's disease (PD). The method identified a set of candidate microbial signatures associated with PD and showed improved replicability across the two independent datasets. PoDA is implemented in R and available at . Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.