Poisson hurdle model-based method for clustering microbiome features.

ABSTRACT:

Motivation

High-throughput sequencing technologies have greatly facilitated microbiome research and have generated a large volume of microbiome data with the potential to answer key questions regarding microbiome assembly, structure and function. Cluster analysis aims to group features that behave similarly across treatments, and such grouping helps to highlight the functional relationships among features and may provide biological insights into microbiome networks. However, clustering microbiome data are challenging due to the sparsity and high dimensionality.

Results

We propose a model-based clustering method based on Poisson hurdle models for sparse microbiome count data. We describe an expectation-maximization algorithm and a modified version using simulated annealing to

SUBMITTER: Qiao Z 

PROVIDER: S-EPMC9825753 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature