OmicsON

OmicsON integrates transcriptomic and metabolomic datasets to identify gene–metabolite relationships by combining biological knowledge from Reactome and String with multivariate statistics such as Canonical Correlation Analysis (CCA) and Partial Least Squares (PLS).

Key Features:

• Data Integration: Merges transcriptomic and metabolomic datasets and leverages Reactome and String to relate gene expression profiles to metabolite concentrations.
• Functional Grouping: Organizes features into biologically meaningful subgroups using pathway and interaction knowledge from Reactome and String.
• Multivariate Statistical Procedures: Applies Canonical Correlation Analysis (CCA) and Partial Least Squares (PLS) to detect correlations and interactions between datasets.

Scientific Applications:

• Systems Biology: Elucidates molecular network relationships by linking gene expression and metabolic pathway data.
• Personalized Medicine: Supports analysis of patient-specific transcriptomic and metabolomic profiles to interpret molecular phenotypes.
• Drug Discovery: Aids identification of gene–metabolite associations relevant to biochemical pathways and therapeutic targets.

Methodology:

Computational steps: collecting transcriptomic and metabolomic data; organizing data into biological subgroups using Reactome and String; applying multivariate statistical procedures including Canonical Correlation Analysis (CCA) and Partial Least Squares (PLS).