Abstract
BackgroundRepresenting biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets.ResultsWe compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality.ConclusionsOur results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.
Highlights
Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data
In this paper we investigate the role that hypergraph models, as mathematical generalizations of graph models, can play in providing the necessary complexity to capture multi-way interactions in biological systems inferred from genomic expression data
In order to validate this assertion we introduce new average hypergraph centrality metrics and provide a comparison between the use of graph and hypergraph centrality metrics to identify genes that are critical in host responses to viral infection
Summary
Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. Graphs do not natively capture the multi-way relation‐ ships present among genes and proteins in biological systems. Graphs are frequently used to model these interactions, but since graphs can only capture interactions between pairs of entities, they fall short in many cases and are not able to model the full complexity present in biological systems and processes. In this paper we investigate the role that hypergraph models, as mathematical generalizations of graph models, can play in providing the necessary complexity to capture multi-way interactions in biological systems inferred from genomic expression data. In order to validate this assertion we introduce new average hypergraph centrality metrics and provide a comparison between the use of graph and hypergraph centrality metrics to identify genes that are critical in host responses to viral infection. Our findings show that the genes identified using our hypergraph model and centrality metrics align better with genes previously known to correlate with viral response than do genes identified using similar metrics applied to graphs or using average fold change for each gene across all experimental conditions
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