Identification of Multidimensional Regulatory Modules Through Multi-Graph Matching With Network Constraints

Objective: The accumulation of large amounts of multidimensional genomic data provides new opportunities to study multilevel biological regulatory associations. Identifying multidimensional regulatory modules (md-modules) from omics data is crucial to provide a comprehensive understanding of the regulatory mechanisms of biological systems. Methods: We develop a multi-graph matching with multiple network constraints (MGMMNC) model to identify the md-modules. The MGMMNC model aims to accurately capture highly relevant md-modules by considering the relationships intra- and inter-multidimensional omics data, including interactions within a network and cycle consistency information. The proposed technique adopts a novel graph-smoothing similarity measurement for the highly contaminated genetic data. Results: The superiority and effectiveness of MGMMNC have been demonstrated by comparative experiments with three state-of-the-art techniques using simulated and cervical cancer data. Conclusion: MGMMNC can accurately and efficiently identify the md-modules that are significantly enriched in gene ontology biological processes and in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Many different level molecules in the same md-module collaboratively regulate the same pathway. Moreover, the md-modules are capable of stratifying patients into subtypes with significant survival differences. Significance: The problem of identifying multidimensional regulatory modules from omics data is formulated as a multi-graph matching problem, and multiple network constraints and cycle consistency information are seamlessly integrated into the matching model.

I. Introduction

Tumorigenesis is the result of a complex interplay between multiple biological pathways. Therefore, use of only one single omics data to explore tumor progression will miss complex models that involve variation across multiple levels of biological regulation [1]. The development of high-throughput genomic technologies has made it possible for researchers to gain insights within multiple dimensions of genomic data. For example, The Cancer Genome Atlas (TCGA) project generates multidimensional genomic data, including gene expression (GE), DNA methylation (DM) and microRNA expression (ME) for the same cohort of tumor samples [2]. Unfortunately, advances in omics data analysis are far behind the fast accumulation of data. Identifying multidimensional regulatory modules (md-modules) from multidimensional genomic data is vital for investigation of the complex regulatory mechanisms by which elements at different levels interact with each other in biological systems.