Paper Info
Title
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 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 l(1)-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.
Year
DOI
Venue
2015
10.1093/bioinformatics/btv364
BIOINFORMATICS
Field
DocType
Volume
Data mining,Computer science,Compositional data,Co-occurrence,Metagenomics,Correlation,Relative species abundance,Sampling (statistics),Bioinformatics,Covariance,False positive paradox
Journal
31
Issue
ISSN
Citations 
20
1367-4803
6
PageRank 
References 
Authors
0.63
3
3
Name
Order
Citations
PageRank
Yuguang Ban160.63
Lingling An227511.69
Hongmei Jiang392.74