ArticleBiometrics2021
Compositional knockoff filter for high-dimensional regression analysis of microbiome data.
Article in Biometrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 32 citations in OpenAlex.
- A benchmarking study of feature screening approaches across type 1 diabetes omics studies classification settings.PLoS computational biology · 2026Article
- Controlling FDR in selecting group-level simultaneous signals from multiple data sources with application to the National COVID Collaborative Cohort data.Journal of applied statistics · 2026Article
- TCVS: tree-guided compositional variable selection analysis of microbiome data.Bioinformatics (Oxford, England) · 2025Article
- Robust multivariate regression controlling false discoveries for microbiome data.Bioinformatics (Oxford, England) · 2025Article
- A systematic benchmark of integrative strategies for microbiome-metabolome data.Communications biology · 2025Article
- Incorporating scale uncertainty in microbiome and gene expression analysis as an extension of normalization.Genome biology · 2025Article
- Power-enhanced two-sample mean tests for high-dimensional microbiome compositional data.Biometrics · 2025Article
- A LATENT VARIABLE MIXTURE MODEL FOR COMPOSITION-ON-COMPOSITION REGRESSION WITH APPLICATION TO CHEMICAL RECYCLING.The annals of applied statistics · 2024Article
- Beyond Normalization: Incorporating Scale Uncertainty in Microbiome and Gene Expression Analysis.bioRxiv : the preprint server for biology · 2024Article
- Analysis of Microbiome Data.Annual review of statistics and its application · 2024Article
- Robust Covariance Matrix Estimation for High-Dimensional Compositional Data with Application to Sales Data Analysis.Journal of business & economic statistics : a publication of the American Statistical Association · 2023Article
- Adaptive and powerful microbiome multivariate association analysis via feature selection.NAR genomics and bioinformatics · 2022Article
- Opportunities and limits of combining microbiome and genome data for complex trait prediction.Genetics, selection, evolution : GSE · 2021Article
- Aggregating Knockoffs for False Discovery Rate Control with an Application to Gut Microbiome Data.Entropy (Basel, Switzerland) · 2021Article
- Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning.Bioinformatics (Oxford, England) · 2020Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.