Investigating microbial co-occurrence patterns based on metagenomic compositional data.

ABSTRACT:

Motivation

The high-throughput sequencing technologies have provided a powerful tool to study the microbial organisms living in various environments. Characterizing microbial interactions can give us insights into how they live and work together as a community. Metagonomic data are usually summarized in a compositional fashion due to varying sampling/sequencing depths from one sample to another. We study the co-occurrence patterns of microbial organisms using their relative abundance information. Analyzing compositional data using conventional correlation methods has been shown prone to bias that leads to artifactual correlations.

Results

We propose a novel method, regularized estimation of the basis covariance based on compositional data (REBACCA), to identify significant c

SUBMITTER: Ban Y 

PROVIDER: S-EPMC4795632 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature