ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2023
Sparse tree-based clustering of microbiome data to characterize microbiome heterogeneity in pancreatic cancer.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- A Tree-Based Model for Addressing Sparsity and Taxa Covariance in Microbiome Compositional Count Data.Statistics in medicine · 2026Article
- Microbiota in cancer: current understandings and future perspectives.Signal transduction and targeted therapy · 2026Review
- TCVS: tree-guided compositional variable selection analysis of microbiome data.Bioinformatics (Oxford, England) · 2025Article
- Constructing phylogenetic trees for microbiome data analysis: A mini-review.Computational and structural biotechnology journal · 2024Review
- Analysis of Microbiome Data.Annual review of statistics and its application · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
There is a keen interest in characterizing variation in the microbiome across cancer patients, given increasing evidence of its important role in determining treatment outcomes. Here our goal is to discover subgroups of patients with similar microbiome profiles. We propose a novel unsupervised clustering approach in the Bayesian framework that innovates over existing model-based clustering approaches, such as the Dirichlet multinomial mixture model, in three key respects: we incorporate feature selection, learn the appropriate number of clusters from the data, and integrate information on the tree structure relating the observed features. We compare the performance of our proposed method to existing methods on simulated data designed to mimic real microbiome data. We then illustrate results obtained for our motivating data set, a clinical study aimed at characterizing the tumor microbiome of pancreatic cancer patients.
Identifiers
What OpenQuestion holds
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.