Identification of microbial features in multivariate regression under false discovery rate control

Arun Srinivasan, Lingzhou Xue and Xiang Zhan

Computational Statistics & Data Analysis, 2023, vol. 181, issue C

Abstract: In many microbiome studies, researchers often aim at detecting statistical associations between microbial taxa and multiple disease-related secondary phenotypes of interest, which are further investigated in downstream functional studies. Most existing approaches tackle this aim by analyzing one taxon at a time and then followed by multiple testing correction. However, the large number of microbial taxa poses a heavy multiple correction burden which often limits the power of discovery of the aforementioned individual taxon-based analyses. Moreover, complicated correlation structures among taxa poses grand challenges for multiple testing correction procedures to achieve a satisfactory performance (e.g., false discovery rate control). To address these potential limitations, a new approach is proposed to detect statistical associations between multiple responses and microbial features in a multivariate regression model, which models the correlations among responses to boost power of association discovery. By utilizing the knockoff filter technique, the proposed procedure also enjoys the property of finite-sample false discovery rate control. It is demonstrated through a comprehensive simulation study to show the validity and usefulness of our new method and apply the methodology to a data set collected from microbiome studies to gain additional biological insights.

Keywords: False discovery rate control; Knockoff filter; Log-ratio transformation; Logistic-normal distribution; Microbial feature selection; Multivariate regression (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:181:y:2023:i:c:s0167947322002018

DOI: 10.1016/j.csda.2022.107621

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