ArticleScientific reports2021
Tree-aggregated predictive modeling of microbiome data.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- A phylogeny-guided framework for decoding mechanisms of human endogenous retrovirus regulation in health and disease.bioRxiv : the preprint server for biology · 2026Article
- A Tree-Based Model for Addressing Sparsity and Taxa Covariance in Microbiome Compositional Count Data.Statistics in medicine · 2026Article
- Article
- TAGINE: fast taxonomy-based feature engineering for microbiome analysis.Bioinformatics advances · 2026Article
- Tree-based differential testing using inferential uncertainty for RNA-seq.Genome research · 2025Article
- Tree-based differential testing using inferential uncertainty for RNA-Seq.bioRxiv : the preprint server for biology · 2025Article
- Controlling the False Split Rate in Tree-Based Aggregation.Journal of the American Statistical Association · 2025Article
- Variational inference for microbiome survey data with application to global ocean data.ISME communications · 2025Article
- A novel and robust feature selection method with FDR control for omics-wide association analysis.PloS one · 2025Article
- Constructing phylogenetic trees for microbiome data analysis: A mini-review.Computational and structural biotechnology journal · 2024Review
- TARO: tree-aggregated factor regression for microbiome data integration.Bioinformatics (Oxford, England) · 2024Article
- Analysis of Microbiome Data.Annual review of statistics and its application · 2024Article
- Article
- Supervised learning and model analysis with compositional data.PLoS computational biology · 2023Article
- Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering.PLoS computational biology · 2023Article
- Multi-Task Learning for Compositional Data via Sparse Network Lasso.Entropy (Basel, Switzerland) · 2022Article
- A tree-based gene-environment interaction analysis with rare features.Statistical analysis and data mining · 2022Article
- Tree-based Node Aggregation in Sparse Graphical Models.Journal of machine learning research : JMLR · 2022Article
- scCODA is a Bayesian model for compositional single-cell data analysis.Nature communications · 2021Article
- tascCODA: Bayesian Tree-Aggregated Analysis of Compositional Amplicon and Single-Cell Data.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Modern high-throughput sequencing technologies provide low-cost microbiome survey data across all habitats of life at unprecedented scale. At the most granular level, the primary data consist of sparse counts of amplicon sequence variants or operational taxonomic units that are associated with taxonomic and phylogenetic group information. In this contribution, we leverage the hierarchical structure of amplicon data and propose a data-driven and scalable tree-guided aggregation framework to associate microbial subcompositions with response variables of interest. The excess number of zero or low count measurements at the read level forces traditional microbiome data analysis workflows to remove rare sequencing variants or group them by a fixed taxonomic rank, such as genus or phylum, or by phylogenetic similarity. By contrast, our framework, which we call trac (tree-aggregation of compositional data), learns data-adaptive taxon aggregation levels for predictive modeling, greatly reducing the need for user-defined aggregation in preprocessing while simultaneously integrating seamlessly into the compositional data analysis framework. We illustrate the versatility of our framework in the context of large-scale regression problems in human gut, soil, and marine microbial ecosystems. We posit that the inferred aggregation levels provide highly interpretable taxon groupings that can help microbiome researchers gain insights into the structure and functioning of the underlying ecosystem of interest.
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Registered trials
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.