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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Unified feature association networks through integration of transcriptomic and proteomic data
Creator	Baric, Ralph Swanstrom, Jesica Adkins, Joshua Deatherage Kaiser, Brooke Mcclure, Ryan Mcdermottid, Jason Oxfordid, Kristie Watersid, Katrina Wendler Id, Jason
Source	PMC
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, 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.
has issue date	2019-09-17(xsd:dateTime)
bibo:doi	10.1371/journal.pcbi.1007241
bibo:pmid	31527878
has license	cc0
sha1sum (hex)	c3d79e1b3bb0159b3c333de43cdff8988bcd459f
schema:url	https://doi.org/10.1371/journal.pcbi.1007241
resource representing a document's title	Unified feature association networks through integration of transcriptomic and proteomic data
has PubMed Central identifier	PMC6748406
has PubMed identifier	31527878
schema:publication	PLoS Comput Biol
resource representing a document's body	covid:c3d79e1b3bb0159b3c333de43cdff8988bcd459f#body_text
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