Document Detail

Inferring microbial interaction networks based on consensus similarity network fusion.
MedLine Citation:
PMID:  25326827     Owner:  NLM     Status:  Publisher    
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.
XingPeng Jiang; XiaoHua Hu
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-10-18
Journal Detail:
Title:  Science China. Life sciences     Volume:  -     ISSN:  1869-1889     ISO Abbreviation:  Sci China Life Sci     Publication Date:  2014 Oct 
Date Detail:
Created Date:  2014-10-19     Completed Date:  -     Revised Date:  2014-10-20    
Medline Journal Info:
Nlm Unique ID:  101529880     Medline TA:  Sci China Life Sci     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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