Journal article
Inferring microbial interaction networks based on consensus similarity network fusion
Science China. Life sciences, v 57(11), pp 1115-1120
Nov 2014
PMID: 25326827
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
With the rapid accumulation of high-throughput metagenomic sequencing data, it is possible to infer microbial species relations in a microbial community systematically. In recent years, some approaches have been proposed for identifying microbial interaction network. These methods often focus on one dataset without considering the advantage of data integration. In this study, we propose to use a similarity network fusion (SNF) method to infer microbial relations. The SNF efficiently integrates the similarities of species derived from different datasets by a cross-network diffusion process. We also introduce consensus k-nearest neighborhood (Ck-NN) method instead of k-NN in the original SNF (we call the approach CSNF). The final network represents the augmented species relationships with aggregated evidence from various datasets, taking advantage of complementarity in the data. We apply the method on genus profiles derived from three microbiome datasets and we find that CSNF can discover the modular structure of microbial interaction network which cannot be identified by analyzing a single dataset.
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Details
- Title
- Inferring microbial interaction networks based on consensus similarity network fusion
- Creators
- XingPeng Jiang - School of Computer Central China Normal University Wuhan 430079 ChinaXiaoHua Hu - School of Computer Central China Normal University Wuhan 430079 China
- Publication Details
- Science China. Life sciences, v 57(11), pp 1115-1120
- Publisher
- Science China Press; Beijing
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000345484700008
- Scopus ID
- 2-s2.0-84922006760
- Other Identifier
- 991014877795904721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biology