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...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.展开更多
基金supported in part by US National Science Foundation,Division of Industrial Innovation and Partnerships(1160960 and 1332024)Computing and Communication Foundations(0905291)+2 种基金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)
文摘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.
