Inferring microbial interaction networks based on consensus similarity network fusion

作     者：JIANG XingPeng HU XiaoHua 

作者机构：College of Computing and Informatics Drexel University School of Computer Central China Normal University 

出 版 物：《Science China(Life Sciences)》 (中国科学（生命科学英文版）)

年 卷 期：2014年第57卷第11期

页      面：1115-1120页

核心收录：

学科分类：0710[理学-生物学] 1007[医学-药学(可授医学、理学学位)] 100705[医学-微生物与生化药学] 07[理学] 071005[理学-微生物学] 10[医学] 

基　　金：supported in part by US National Science Foundation,Division of Industrial Innovation and Partnerships(1160960 and 1332024) Computing and Communication Foundations(0905291) National Natural Science Foundation of China(90920005,61170189) the Twelfth Five-year Plan of China(2012BAK24B01) National Social Science Funds of China(12&2D223,13&ZD183) 

主　　题：species interaction metagenome diffusion process biological network modularity 

摘      要：With the rapid accumulation of high-throughput metagenomic sequencing data,it is possible to infer microbial species relations in a microbial community *** recent years,some approaches have been proposed for identifying microbial interaction *** methods often focus on one dataset without considering the advantage of data *** this study,we propose to use a similarity network fusion(SNF)method to infer microbial *** SNF efficiently integrates the similarities of species derived from different datasets by a cross-network diffusion *** 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 *** 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.