Unified feature association networks through integration of transcriptomic and proteomic data.

Autor: McClure RS; Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, United States of America., Wendler JP; Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, United States of America., Adkins JN; Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, United States of America., Swanstrom J; Department of Microbiology and Immunology, School of Medicine, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States of America., Baric R; Department of Microbiology and Immunology, School of Medicine, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States of America., Kaiser BLD; Signatures Science and Technology Division, Pacific Northwest National Laboratory, Richland WA, United States of America., Oxford KL; Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, United States of America., Waters KM; Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, United States of America., McDermott JE; Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, United States of America.; Department of Molecular Microbiology and Immunology, Oregon Health & Sciences University, Portland, OR, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2019 Sep 17; Vol. 15 (9), pp. e1007241. Date of Electronic Publication: 2019 Sep 17 (Print Publication: 2019).
DOI: 10.1371/journal.pcbi.1007241
Abstrakt: 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, networks that have a large number of edges that link nodes of different-omic types (transcripts, proteins, lipids etc). 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 mechanisms of disease.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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