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

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Vast amounts of atomized biological data necessitate advanced data integration methods.
• Understanding relationships between different omics datasets (e.g., transcriptomics, metabolomics) is crucial for biological insights.

Purpose of the Study:

• To present OmicsON, an R library designed for integrating transcriptomics and metabolomics data.
• To demonstrate how OmicsON leverages biological knowledge and statistical analysis for omics data interpretation.

Main Methods:

• OmicsON integrates transcriptomics and metabolomics datasets.
• Functional grouping and statistical analyses (CCA, PLS) are applied.
• Biological knowledge from Reactome and String databases is utilized for subgroup creation.

Main Results:

• OmicsON enables the analysis of integrated transcriptomic and metabolomic data.
• The library facilitates the identification of connections between gene expression and metabolite levels.
• Multivariate statistical procedures enhance the analysis of complex biological datasets.

Conclusions:

• OmicsON provides a robust tool for integrating and analyzing transcriptomics and metabolomics data.
• The integration approach aids in understanding the interplay between gene expression and metabolite concentrations.
• This facilitates deeper biological interpretation of complex biological processes.