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

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.

Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology

“…(e) Omics data integrated telemedicine platforms that incorporate omics data can provide remote healthcare services, such as virtual consultations, to individuals in remote or underserved communities, improving access to care and outcomes [ 80 ].…”

Multi-Omics Integration for the Design of Novel Therapies and the Identification of Novel Biomarkers

“…This method is useful for generating hypotheses and exploring associations between different omics data, but it may not capture the complexity and dynamics of the biological system. Open-source pipelines such as STATegra [ 8 ] or OmicsON [ 9 ] have recently demonstrated an enhanced capacity of the framework to detect specific features overlapping between the compared omics sets; Statistical integration: This method involves using statistical techniques to combine or compare different omics data based on quantitative measures, such as correlation, regression, clustering, or classification [ 10 ]. For example, one can use correlation analysis to identify co-expressed genes or proteins across different omics data sets or use regression analysis to model the relationship between gene expression and drug response [ 11 ].…”

“…This method is useful for generating hypotheses and exploring associations between different omics data, but it may not capture the complexity and dynamics of the biological system. Open-source pipelines such as STATegra [ 8 ] or OmicsON [ 9 ] have recently demonstrated an enhanced capacity of the framework to detect specific features overlapping between the compared omics sets;…”

Radiovesicolomics-new approach in medical imaging

“…Such analyses, combined with results of high-throughput techniques, produce a vast number of multiparametric (quantitative and qualitative) data from less number of examined samples. It needs systematic and multimodal analyses for integration of omics datasets and selection highly correlated biological features using different bioinformatics methods like Canonical Correlation Analysis (CCA) ( Jun et al, 2018 ; Turek et al, 2020 ; Wróbel 2021 ) or deep/machine learning algorithms for better selection of biological interrelationships ( Stahlschmidt et al, 2022 ).…”