Network-based modeling for analyzing the human skin microbiome

Microbes found on the skin are usually regarded as pathogens, potential pathogens or innocuous symbiotic organisms. Advances in microbiology and immunology are revising our understanding of the molecular mechanisms of microbial virulence and the specific events involved in the host-microbe interaction. A microbial community similarity function of skin sites was defined to analysis the topographical diversity of microbial community in this paper. We found that the moist skin sites and sebaceous skin sites easily group together respectively with furthest-neighbor clustering algorithm, which shows that the moist skin sites and sebaceous skin sites have their respective microenvironments. We also introduced a bipartite network modeling method and network aligning algorithm. The network analysis revealed that the microbial species of moist skin sites is more than that of sebaceous skin sites, and resampled volunteers were more like themselves over time than they were like other volunteers. These results show that our network analysis methods are effective for researching the complexity and stability of the human skin microbial community.