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.
Highlights
Technologies of molecular biology providing a big amount of data have given rise to largescale biological datasets
Based on the analysis of hepatic samples, it comprises gene expression data of 120 selected genes potentially involved in lipid metabolism and concentrations of 21 fatty acids
This is not a large data set, but sufficient to demonstrate the functionality of the OmicsON library
Summary
Technologies of molecular biology providing a big amount of data have given rise to largescale biological datasets. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. This approach has had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era [1]. Systemic exploration of complex interactions in biological systems because of the development of new technologies and analytical methods allows the creation of clinically useful tools [2] Despite their promise, the translation of these technologies into clinically actionable tools has been slow [3] [4] [5].
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