Unified feature association networks through integration of transcriptomic and proteomic data

Journal Article · 16 September 2019 · PLoS Computational Biology (Online)

DOI: https://doi.org/10.1371/journal.pcbi.1007241 · OSTI ID:1577846

McClure, Ryan S. ^[1]; Wendler, Jason P.  ^[1]; Adkins, Joshua N. ^[1]; Swanstrom, Jesica ^[2]; Baric, Ralph ^[2]; Kaiser, Brooke L. Deatherage ^[3]; Oxford, Kristie L.  ^[1]; Waters, Katrina M.  ^[1]; McDermott, Jason E.  ^[4]; Jensen, Paul ^[5]

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1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Biological Sciences Division
2. Univ. of North Carolina, Chapel Hill, NC (United States). School of Medicine, Dept. of Microbiology and Immunology
3. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Signatures Science and Technology Division
4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Biological Sciences Division; Oregon Health & Sciences Univ., Portland, OR (United States). Dept. of Molecular Microbiology and Immunology
5. Univ. of Illinois at Urbana-Champaign, IL (United States)

High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks. We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm disease associated mechanisms of disease.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 1577846
• Report Number(s): PNNL-SA--137420
• Journal Information: PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Journal Issue: 9 Vol. 15; ISSN 1553-7358
• Publisher: Public Library of ScienceCopyright Statement
• Country of Publication: United States
• Language: English