SUPPLEMENTAL DATA

Original Research ARTICLE

Front. Genet., 07 January 2014 | http://dx.doi.org/10.3389/fgene.2013.00309

Multi-omic network signatures of disease

David L. Gibbs1*, Lisa Gralinski2, Ralph S. Baric2 and Shannon K. McWeeney1,3
  • 1McWeeney Lab, Division of Bioinformatics and Computational Biology, Oregon Health & Science University, Portland, OR, USA
  • 2Baric Lab, Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
  • 3McWeeney Lab, OHSU Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA

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.

Keywords: omics, networks, data integration, proteomics, transcriptomics, virology, biomarkers, SARS

Citation: Gibbs DL, Gralinski L, Baric RS and McWeeney SK (2014) Multi-omic network signatures of disease. Front. Genet. 4:309. doi: 10.3389/fgene.2013.00309

Received: 20 September 2013; Paper pending published: 21 October 2013;
Accepted: 19 December 2013; Published online: 07 January 2014.

Edited by:

Xiaogang Wu, Indiana University-Purdue University Indianapolis, USA

Reviewed by:

Lifan Zeng, Indiana University, USA
Andrei Dragomir, University of Houston, USA
Anaïs Baudot, Centre National de la Recherche Scientifique, France

Copyright © 2014 Gibbs, Gralinski, Baric and McWeeney. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: David L. Gibbs, Division of Bioinformatics and Computational Biology, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239-3098, USA e-mail: gibbsd@ohsu.edu