Musfiqur Sazal1, Kalai Mathee2,3, Daniel Ruiz-Perez1, Trevor Cickovski1, Giri Narasimhan4,5. 1. Bioinformatics Research Group (BioRG), School of Computing and Information Sciences, Florida International University, Miami, 33199, FL, USA. 2. Herbert Wertheim College of Medicine, Florida International University, Miami, 33199, FL, USA. 3. Biomolecular Sciences Institute (BSI), Florida International University, Miami, 33199, FL, USA. 4. Bioinformatics Research Group (BioRG), School of Computing and Information Sciences, Florida International University, Miami, 33199, FL, USA. giri@fiu.edu. 5. Biomolecular Sciences Institute (BSI), Florida International University, Miami, 33199, FL, USA. giri@fiu.edu.
Abstract
BACKGROUND: Microbe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by a Bayesian Network (BN) about a microbial community. Unlike the previously proposed Co-occurrence Networks (CoNs), BNs are based on conditional dependencies and can help in revealing complex associations. RESULTS: In this paper, we propose a way of combining a BN and a CoN to construct a signed Bayesian Network (sBN). We report a surprising association between directed edges in signed BNs and known colonization orders. CONCLUSIONS: BNs are powerful tools for community analysis and extracting influences and colonization patterns, even though the analysis only uses an abundance matrix with no temporal information. We conclude that directed edges in sBNs when combined with negative correlations are consistent with and strongly suggestive of colonization order.
BACKGROUND: Microbe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by a Bayesian Network (BN) about a microbial community. Unlike the previously proposed Co-occurrence Networks (CoNs), BNs are based on conditional dependencies and can help in revealing complex associations. RESULTS: In this paper, we propose a way of combining a BN and a CoN to construct a signed Bayesian Network (sBN). We report a surprising association between directed edges in signed BNs and known colonization orders. CONCLUSIONS:BNs are powerful tools for community analysis and extracting influences and colonization patterns, even though the analysis only uses an abundance matrix with no temporal information. We conclude that directed edges in sBNs when combined with negative correlations are consistent with and strongly suggestive of colonization order.
Authors: Paul E Kolenbrander; Roxanna N Andersen; David S Blehert; Paul G Egland; Jamie S Foster; Robert J Palmer Journal: Microbiol Mol Biol Rev Date: 2002-09 Impact factor: 11.056
Authors: Pawel Gajer; Rebecca M Brotman; Guoyun Bai; Joyce Sakamoto; Ursel M E Schütte; Xue Zhong; Sara S K Koenig; Li Fu; Zhanshan Sam Ma; Xia Zhou; Zaid Abdo; Larry J Forney; Jacques Ravel Journal: Sci Transl Med Date: 2012-05-02 Impact factor: 17.956