Multi-omic network signatures of disease

David L. Gibbs, Lisa Gralinski, Ralph S. Baric, Shannon K. McWeeney

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

To better understand dynamic disease processes, integrated multi-omic methods are needed, yet comparing different types of omic data remainsdifficult. 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.

Original languageEnglish (US)
Article numberArticle 309
JournalFrontiers in Genetics
Volume4
Issue numberJAN
DOIs
StatePublished - 2013

Keywords

  • Biomarkers
  • Data integration
  • Networks
  • Omics
  • Proteomics
  • SARS
  • Transcriptomics
  • Virology

ASJC Scopus subject areas

  • Molecular Medicine
  • Genetics
  • Genetics(clinical)

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