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

Authors: Ban, Yuguang; An, Lingling; Jiang, Hongmei

Published In Bioinformatics, (2015 Oct 15)

Abstract: 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.We propose a novel method, regularized estimation of the basis covariance based on compositional data (REBACCA), to identify significant co-occurrence patterns by finding sparse solutions to a system with a deficient rank. To be specific, we construct the system using log ratios of count or proportion data and solve the system using the l1-norm shrinkage method. Our comprehensive simulation studies show that REBACCA (i) achieves higher accuracy in general than the existing methods when a sparse condition is satisfied; (ii) controls the false positives at a pre-specified level, while other methods fail in various cases and (iii) runs considerably faster than the existing comparable method. REBACCA is also applied to several real metagenomic datasets.The R codes for the proposed method are available at http://faculty.wcas.northwestern.edu/∼hji403/REBACCA.htmhongmei@northwestern.eduSupplementary data are available at Bioinformatics online.

PubMed ID: 26079350

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