Compositional zero-inflated network estimation for microbiome data.

ABSTRACT:

Background

The estimation of microbial networks can provide important insight into the ecological relationships among the organisms that comprise the microbiome. However, there are a number of critical statistical challenges in the inference of such networks from high-throughput data. Since the abundances in each sample are constrained to have a fixed sum and there is incomplete overlap in microbial populations across subjects, the data are both compositional and zero-inflated.

Results

We propose the COmpositional Zero-Inflated Network Estimation (COZINE) method for inference of microbial networks which addresses these critical aspects of the data while maintaining computational scalability. COZINE relies on the multivariate Hurdle model to infer a sparse set of conditional

SUBMITTER: Ha MJ 

PROVIDER: S-EPMC7768662 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature