ArticleThe annals of applied statistics2018
KERNEL-PENALIZED REGRESSION FOR ANALYSIS OF MICROBIOME DATA.
Article in The annals of applied statistics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Multidimensional scaling informed by F-statistic: Visualizing grouped microbiome data with inference.PLoS computational biology · 2026Article
- BCGLMs: Bayesian modeling for disease prediction using compositional microbiome features.Bioinformatics advances · 2026Article
- Doubly regularized generalized linear models for spatial observations with high-dimensional covariates.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- Linear Dimensionality Reduction Methods for Analyzing Structured Biomedical Data: Existing Research and Future Opportunities.Wiley interdisciplinary reviews. Computational statistics · 2025Review
- Recent advancements in artificial intelligence-powered cancer prediction from oral microbiome.Periodontology 2000 · 2025Review
- Bayesian compositional generalized linear mixed models for disease prediction using microbiome data.BMC bioinformatics · 2025Article
- Proportion-based normalizations outperform compositional data transformations in machine learning applications.Microbiome · 2024Article
- GENERALIZED MATRIX DECOMPOSITION REGRESSION: ESTIMATION AND INFERENCE FOR TWO-WAY STRUCTURED DATA.The annals of applied statistics · 2023Article
- Supervised learning and model analysis with compositional data.PLoS computational biology · 2023Article
- Article
- Microbial trend analysis for common dynamic trend, group comparison, and classification in longitudinal microbiome study.BMC genomics · 2021Article
- Tree-aggregated predictive modeling of microbiome data.Scientific reports · 2021Article
- Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization.BMC bioinformatics · 2021Article
- mbImpute: an accurate and robust imputation method for microbiome data.Genome biology · 2021Article
- Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods.PloS one · 2021Article
- Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.Frontiers in microbiology · 2021Review
- Use of Functional Linear Models to Detect Associations between Characteristics of Walking and Continuous Responses Using Accelerometry Data.Sensors (Basel, Switzerland) · 2020Article
- Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies.mSystems · 2019Article
- A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction.Frontiers in genetics · 2019Review
- Conditional Regression Based on a Multivariate Zero-Inflated Logistic-Normal Model for Microbiome Relative Abundance Data.Statistics in biosciences · 2018Article
Corrections and comments
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5 authors.
Funding
Abstract
The analysis of human microbiome data is often based on dimension-reduced graphical displays and clusterings derived from vectors of microbial abundances in each sample. Common to these ordination methods is the use of biologically motivated definitions of similarity. Principal coordinate analysis, in particular, is often performed using ecologically defined distances, allowing analyses to incorporate context-dependent, non-Euclidean structure. In this paper, we go beyond dimension-reduced ordination methods and describe a framework of high-dimensional regression models that extends these distance-based methods. In particular, we use kernel-based methods to show how to incorporate a variety of extrinsic information, such as phylogeny, into penalized regression models that estimate taxonspecific associations with a phenotype or clinical outcome. Further, we show how this regression framework can be used to address the compositional nature of multivariate predictors comprised of relative abundances; that is, vectors whose entries sum to a constant. We illustrate this approach with several simulations using data from two recent studies on gut and vaginal microbiomes. We conclude with an application to our own data, where we also incorporate a significance test for the estimated coefficients that represent associations between microbial abundance and a percent fat.
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