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OmicsON – Integration of omics data with molecular networks and statistical procedures

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  • Cezary Turek
  • Sonia Wróbel
  • Monika Piwowar

Abstract

A huge amount of atomized biological data collected in various databases and the need for a description of their relation by theoretical methods causes the development of data integration methods. The omics data analysis by integration of biological knowledge with mathematical procedures implemented in the OmicsON R library is presented in the paper. OmicsON is a tool for the integration of two sets of data: transcriptomics and metabolomics. In the workflow of the library, the functional grouping and statistical analysis are applied. Subgroups among the transcriptomic and metabolomics sets are created based on the biological knowledge stored in Reactome and String databases. It gives the possibility to analyze such sets of data by multivariate statistical procedures like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). The integration of metabolomic and transcriptomic data based on the methodology contained in OmicsON helps to easily obtain information on the connection of data from two different sets. This information can significantly help in assessing the relationship between gene expression and metabolite concentrations, which in turn facilitates the biological interpretation of the analyzed process.

Suggested Citation

  • Cezary Turek & Sonia Wróbel & Monika Piwowar, 2020. "OmicsON – Integration of omics data with molecular networks and statistical procedures," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-13, July.
    • Handle: RePEc:plo:pone00:0235398
    DOI: 10.1371/journal.pone.0235398
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    References listed on IDEAS

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    1. Monika Piwowar & Wiktor Jurkowski, 2015. "ONION: Functional Approach for Integration of Lipidomics and Transcriptomics Data," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-14, June.
    2. Monika Piwowar & Kinga A Kocemba-Pilarczyk & Piotr Piwowar, 2018. "Regularization and grouping -omics data by GCA method: A transcriptomic case," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-14, November.
    3. Cruz-Cano, Raul & Lee, Mei-Ling Ting, 2014. "Fast regularized canonical correlation analysis," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 88-100.
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