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Abstract A critical task in microbiome data analysis is to explore the association between a scalar response of interest and a large number of microbial taxa that are summarized as compositional data at different taxonomic levels. Motivated by fine‐mapping of the microbiome, we propose a two‐step compositional knockoff filter to provide the effective finite‐sample false discovery rate (FDR) control in high‐dimensional linear log‐contrast regression analysis of microbiome compositional data. In the first step, we propose a new compositional screening procedure to remove insignificant microbial taxa while retaining the essential sum‐to‐zero constraint. In the second step, we extend the knockoff filter to identify the significant microbial taxa in the sparse regression model for compositional data. Thereby, a subset of the microbes is selected from the high‐dimensional microbial taxa as related to the response under a prespecified FDR threshold. We study the theoretical properties of the proposed two‐step procedure, including both sure screening and effective false discovery control. We demonstrate these properties in numerical simulation studies to compare our methods to some existing ones and show power gain of the new method while controlling the nominal FDR. The potential usefulness of the proposed method is also illustrated with application to an inflammatory bowel disease data set to identify microbial taxa that influence host gene expressions.
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This content will become publicly available on January 6, 2027
Ultra-high-dimensional threshold selection for quantile feature screening with false discovery rate error rate control: a case study on high blood pressure analysis
Abstract Identifying important genetic risk factors for high blood pressure is paramount for targeted interventions and comprehensive management strategies for this prevalent health condition. With ultra-high-dimensional genetic factors, feature screening plays a vital role in retaining the important risk factors and screening out the unimportant ones. However, its efficacy depends on selecting the appropriate threshold in different data contexts. We propose a novel quantile mirror (QM) approach for data-adaptive threshold selection with false discovery rate (FDR) control in quantile feature screening, where abnormally high blood pressure levels are of the most interest. The symmetric QM statistics enable the estimation of the FDR. Multiple data splitting is employed for result stability, and different thresholds are allowed for different quantiles. Additionally, we introduce the Quantile REflection via Data Splitting (QREDS) and the Hard Threshold with QREDS feature screening procedures. We apply our proposed methods to the Framingham Heart Study data. While many of the retained high-blood-pressure-related genetic features are validated from medical literature, some new risk factors are discovered. We show that our proposed procedures asymptotically control the FDR under suitable conditions. We further demonstrate the performance of the proposed methods through extensive numerical analysis and simulation studies.
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- Award ID(s):
- 2311216
- PAR ID:
- 10701760
- Publisher / Repository:
- Oxford Academic
- Date Published:
- Journal Name:
- Biometrics
- Volume:
- 82
- Issue:
- 1
- ISSN:
- 0006-341X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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