Journal
METABOLITES
Volume 10, Issue 5, Pages -Publisher
MDPI
DOI: 10.3390/metabo10050202
Keywords
multi-omics integration; dimensionality reduction; co-regulation; pathway enrichment; clustering; machine learning; deep learning; network analysis; visualization; biological pathways
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Funding
- Ohio State University Translational Data Analytics Institute
- Ohio State University
- Intramural/Extramural research program of the National Center for Advancing Translational Sciences, National Institutes of Health
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As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.
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