Statistical methods and resources for biomarker discovery using metabolomics

• Published in: BMC bioinformatics Vol. 24; no. 1; pp. 250 - 18
• Main Authors: Anwardeen, Najeha R., Diboun, Ilhame, Mokrab, Younes, Althani, Asma A., Elrayess, Mohamed A.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 15.06.2023; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Analytical workflow; Bioinformatics; Biological markers; Biomarkers; Biomedical and Life Sciences; Cancer; Chromatography; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; COVID-19; Data analysis; Data mining; Diabetes; Disease; Enzymes; Genetic diversity; Information retrieval; Life Sciences; Mass spectrometry; Medical research; Medicine, Experimental; Metabolism; Metabolites; Metabolomics; Metabolomics tools; Methods; Microarrays; Multivariate; Network management systems; NMR; Nuclear magnetic resonance; Perturbation; Review; Risk assessment; Scientific imaging; Statistical analysis; Statistical methods; Statistics; Univariate; Qatar; Metabolomics; Metabolomics tools; Univariate; Statistical methods; Analytical workflow; Multivariate
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-023-05383-0

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.