Finding High-Order Homologous Microbe Community Modules via Network Embedding

Abstract

Microbial network analysis help with discovering microbe groups that covariate with environmental factors. However, microbial communities are highly diversified and localized, which poses challenges to existing correlation-based network construction methods in terms of stability and functional significance. In this paper, we propose to explore the high-level relationships in the microbial network structure with the aid of network embedding methods. Microbial function modules are then extracted by spectrum clustering on the embedded networks, rather than the original ones. By investigating the correlation between the obtained modules and the environmental factors on several real-world microbial datasets, we demonstrate that the embedded modules provide feature information of the microbial community that are distinct to traditional correlation-based network modules. Furthermore, we show that the introduction of high-order modules helps with improving the performance of prediction models comparing with using OTU features or traditional correlation-based modules alone. Our study demonstrated that high-order network modules created by network embedding can be served as a potential new biomarker for feature extraction of microbial communities.