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Poisson hurdle model-based method for clustering microbiome features

Journal Article · · Bioinformatics
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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 conduct the cluster analysis. Moreover, we provide algorithms for initialization and choosing the number of clusters. Simulation results demonstrate that our proposed methods provide better clustering results than alternative methods under a variety of settings. We also apply the proposed method to a sorghum rhizosphere microbiome dataset that results in interesting biological findings.

Availability and implementation

R package is freely available for download at https://cran.r-project.org/package=PHclust.

Supplementary information

Supplementary data are available at Bioinformatics online.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
AC02-05CH11231; SC0014395
OSTI ID:
1908292
Alternate ID(s):
OSTI ID: 1906225
OSTI ID: 1986020
Journal Information:
Bioinformatics, Journal Name: Bioinformatics Journal Issue: 1 Vol. 39; ISSN 1367-4811
Publisher:
Oxford University PressCopyright Statement
Country of Publication:
United Kingdom
Language:
English

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