Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification

Mohamed Abdel-Hafiz; Mesbah Najafi; Shahab Helmi; Katherine A Pratte; Yonghua Zhuang; Weixuan Liu; Katerina J Kechris; Russell P Bowler; Leslie Lange; Farnoush Banaei-Kashani

doi:10.3389/fdata.2022.894632

Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification

Front Big Data. 2022 Jun 22:5:894632. doi: 10.3389/fdata.2022.894632. eCollection 2022.

Authors

Affiliations

¹ Big Data Management and Mining Laboratory, Department of Computer Science and Engineering, College of Engineering, Design and Computing, University of Colorado Denver, Denver, CO, United States.
² Department of Mathematics, College of Liberal Arts and Sciences, University of Colorado Denver, Denver, CO, United States.
³ National Jewish Health, Denver, CO, United States.
⁴ Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
⁵ School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
⁶ Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.

Abstract

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death in the United States. COPD represents one of many areas of research where identifying complex pathways and networks of interacting biomarkers is an important avenue toward studying disease progression and potentially discovering cures. Recently, sparse multiple canonical correlation network analysis (SmCCNet) was developed to identify complex relationships between omics associated with a disease phenotype, such as lung function. SmCCNet uses two sets of omics datasets and an associated output phenotypes to generate a multi-omics graph, which can then be used to explore relationships between omics in the context of a disease. Detecting significant subgraphs within this multi-omics network, i.e., subgraphs which exhibit high correlation to a disease phenotype and high inter-connectivity, can help clinicians identify complex biological relationships involved in disease progression. The current approach to identifying significant subgraphs relies on hierarchical clustering, which can be used to inform clinicians about important pathways involved in the disease or phenotype of interest. The reliance on a hierarchical clustering approach can hinder subgraph quality by biasing toward finding more compact subgraphs and removing larger significant subgraphs. This study aims to introduce new significant subgraph detection techniques. In particular, we introduce two subgraph detection methods, dubbed Correlated PageRank and Correlated Louvain, by extending the Personalized PageRank Clustering and Louvain algorithms, as well as a hybrid approach combining the two proposed methods, and compare them to the hierarchical method currently in use. The proposed methods show significant improvement in the quality of the subgraphs produced when compared to the current state of the art.

Keywords: Louvain; PageRank; graph clustering; multi-omics graph; subgraph detection.

Abstract

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