title: Multi-omic network signatures of disease.
abstract: To better understand dynamic disease processes, integrated multi-omic methods are needed, yet comparing different types of omic data remains difficult. Integrative solutions benefit experimenters by eliminating potential biases that come with single omic analysis. We have developed the methods needed to explore whether a relationship exists between co-expression network models built from transcriptomic and proteomic data types, and whether this relationship can be used to improve the disease signature discovery process. A naïve, correlation based method is utilized for comparison. Using publicly available infectious disease time series data, we analyzed the related co-expression structure of the transcriptome and proteome in response to SARS-CoV infection in mice. Transcript and peptide expression data was filtered using quality scores and subset by taking the intersection on mapped Entrez IDs. Using this data set, independent co-expression networks were built. The networks were integrated by constructing a bipartite module graph based on module member overlap, module summary correlation, and correlation to phenotypes of interest. Compared to the module level results, the naïve approach is hindered by a lack of correlation across data types, less significant enrichment results, and little functional overlap across data types. Our module graph approach avoids these problems, resulting in an integrated omic signature of disease progression, which allows prioritization across data types for down-stream experiment planning. Integrated modules exhibited related functional enrichments and could suggest novel interactions in response to infection. These disease and platform-independent methods can be used to realize the full potential of multi-omic network signatures. The data (experiment SM001) are publically available through the NIAID Systems Virology (https://www.systemsvirology.org) and PNNL (http://omics.pnl.gov) web portals. Phenotype data is found in the supplementary information. The ProCoNA package is available as part of Bioconductor 2.13.
authors.affiliation: McWeeney Lab, Division of Bioinformatics and Computational Biology, Oregon Health & Science University Portland, OR, USA.
authors.forename: David L
authors.initials: DL
authors.lastname: Gibbs
authors.affiliation: Baric Lab, Department of Microbiology and Immunology, University of North Carolina at Chapel Hill Chapel Hill, NC, USA.
authors.forename: Lisa
authors.initials: L
authors.lastname: Gralinski
authors.affiliation: Baric Lab, Department of Microbiology and Immunology, University of North Carolina at Chapel Hill Chapel Hill, NC, USA.
authors.forename: Ralph S
authors.initials: RS
authors.lastname: Baric
authors.affiliation: McWeeney Lab, Division of Bioinformatics and Computational Biology, Oregon Health & Science University Portland, OR, USA ; McWeeney Lab, OHSU Knight Cancer Institute, Oregon Health & Science University Portland, OR, USA.
authors.forename: Shannon K
authors.initials: SK
authors.lastname: McWeeney
keywords: SARS; biomarkers; data integration; networks; omics; proteomics; transcriptomics; virology