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NetSHy: network summarization via a hybrid approach leveraging topological properties

Thao Vu,E. Litkowski,Weixuan Liu,K. Pratte,L. Lange,R. Bowler,F. Kashani,K. Kechris

Published 2022 in medRxiv

ABSTRACT

Biological networks can provide a system level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e., they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module's information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information. In this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome wide association study (GWAS) is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms (SNP) than the conventional network representation.

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