ArticleBioinformatics (Oxford, England)2014
Identification of important regressor groups, subgroups and individuals via regularization methods: application to gut microbiome data.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- DeepBiome: A Phylogenetic Tree Informed Deep Neural Network for Microbiome Data Analysis.Statistics in biosciences · 2025Article
- DART: Distance Assisted Recursive Testing.Journal of machine learning research : JMLR · 2023Article
- Article
- Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization.BMC bioinformatics · 2021Article
- MicroBVS: Dirichlet-tree multinomial regression models with Bayesian variable selection - an R package.BMC bioinformatics · 2020Article
- Incorporating Phylogenetic Information in Microbiome Differential Abundance Studies Has No Effect on Detection Power and FDR Control.Frontiers in microbiology · 2020Article
- Variance Component Selection With Applications to Microbiome Taxonomic Data.Frontiers in microbiology · 2018Article
- Predictive Modeling of Microbiome Data Using a Phylogeny-Regularized Generalized Linear Mixed Model.Frontiers in microbiology · 2018Article
- A Phylogeny-Regularized Sparse Regression Model for Predictive Modeling of Microbial Community Data.Frontiers in microbiology · 2018Article
- A Dirichlet-tree multinomial regression model for associating dietary nutrients with gut microorganisms.Biometrics · 2017Article
- COX REGRESSION WITH EXCLUSION FREQUENCY-BASED WEIGHTS TO IDENTIFY NEUROIMAGING MARKERS RELEVANT TO HUNTINGTON'S DISEASE ONSET.The annals of applied statistics · 2016Article
- Modeling the Cholesky factors of covariance matrices of multivariate longitudinal data.Journal of multivariate analysis · 2016Article
- Selection of models for the analysis of risk-factor trees: leveraging biological knowledge to mine large sets of risk factors with application to microbiome data.Bioinformatics (Oxford, England) · 2015Article
- A systematic evaluation of high-dimensional, ensemble-based regression for exploring large model spaces in microbiome analyses.BMC bioinformatics · 2015Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
motivationGut microbiota can be classified at multiple taxonomy levels. Strategies to use changes in microbiota composition to effect health improvements require knowing at which taxonomy level interventions should be aimed. Identifying these important levels is difficult, however, because most statistical methods only consider when the microbiota are classified at one taxonomy level, not multiple.
resultsUsing L1 and L2 regularizations, we developed a new variable selection method that identifies important features at multiple taxonomy levels. The regularization parameters are chosen by a new, data-adaptive, repeated cross-validation approach, which performed well. In simulation studies, our method outperformed competing methods: it more often selected significant variables, and had small false discovery rates and acceptable false-positive rates. Applying our method to gut microbiota data, we found which taxonomic levels were most altered by specific interventions or physiological status. AVAILABILITY: The new approach is implemented in an R package, which is freely available from the corresponding author. CONTACT: tpgarcia@srph.tamhsc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
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