A Generalized Bayesian Stochastic Block Model for Microbiome Community Detection.

ABSTRACT: Advances in next-generation sequencing technology have enabled the high-throughput profiling of metagenomes and accelerated microbiome studies. Recently, there has been a rise in quantitative studies that aim to decipher the microbiome co-occurrence network and its underlying community structure based on metagenomic sequence data. Uncovering the complex microbiome community structure is essential to understanding the role of the microbiome in disease progression and susceptibility. Taxonomic abundance data generated from metagenomic sequencing technologies are high-dimensional and compositional, suffering from uneven sampling depth, over-dispersion, and zero-inflation. These characteristics often challenge the reliability of the current methods for microbiome community detection. To study

SUBMITTER: Lutz KC 

PROVIDER: S-EPMC11760646 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature