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Title: Unified feature association networks through integration of transcriptomic and proteomic data

Abstract

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.more » 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.« less

Authors:
 [1]; ORCiD logo [1];  [1];  [2];  [2];  [3]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [4];  [5]
  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)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1577846
Report Number(s):
PNNL-SA-137420
Journal ID: ISSN 1553-7358
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 15; Journal Issue: 9; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

McClure, Ryan S., Wendler, Jason P., Adkins, Joshua N., Swanstrom, Jesica, Baric, Ralph, Kaiser, Brooke L. Deatherage, Oxford, Kristie L., Waters, Katrina M., McDermott, Jason E., and Jensen, Paul. Unified feature association networks through integration of transcriptomic and proteomic data. United States: N. p., 2019. Web. doi:10.1371/journal.pcbi.1007241.
McClure, Ryan S., Wendler, Jason P., Adkins, Joshua N., Swanstrom, Jesica, Baric, Ralph, Kaiser, Brooke L. Deatherage, Oxford, Kristie L., Waters, Katrina M., McDermott, Jason E., & Jensen, Paul. Unified feature association networks through integration of transcriptomic and proteomic data. United States. doi:10.1371/journal.pcbi.1007241.
McClure, Ryan S., Wendler, Jason P., Adkins, Joshua N., Swanstrom, Jesica, Baric, Ralph, Kaiser, Brooke L. Deatherage, Oxford, Kristie L., Waters, Katrina M., McDermott, Jason E., and Jensen, Paul. Tue . "Unified feature association networks through integration of transcriptomic and proteomic data". United States. doi:10.1371/journal.pcbi.1007241. https://www.osti.gov/servlets/purl/1577846.
@article{osti_1577846,
title = {Unified feature association networks through integration of transcriptomic and proteomic data},
author = {McClure, Ryan S. and Wendler, Jason P. and Adkins, Joshua N. and Swanstrom, Jesica and Baric, Ralph and Kaiser, Brooke L. Deatherage and Oxford, Kristie L. and Waters, Katrina M. and McDermott, Jason E. and Jensen, Paul},
abstractNote = {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.},
doi = {10.1371/journal.pcbi.1007241},
journal = {PLoS Computational Biology (Online)},
number = 9,
volume = 15,
place = {United States},
year = {2019},
month = {9}
}

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