Identification of microbial features in multivariate regression under false discovery rate control.
Comput. Stat. Data Anal.(2023)
摘要
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.(c) 2022 Elsevier B.V. All rights reserved.
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关键词
False discovery rate control,Knockoff filter,Log -ratio transformation,Logistic -normal distribution,Microbial feature selection,Multivariate regression
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