Detecting and Quantifying Variability in Mass Spectrometry-Based Metabolomics Datasets

Mass spectrometry-based metabolomics approaches generate high-dimensional datasets that enable comprehensive profiling of metabolites and other biomolecules. However, these datasets are influenced by multiple sources of variability, including both the biological variation of interest and unwanted technical or systematic variation introduced throughout experimental or analytical workflows. Distinguishing between these sources of variation is important for ensuring robust and reliable downstream interpretation. This review examines commonly used approaches for the detection and quantification of variability in MS-based omics datasets, including statistical and multivariate methods such as coefficient of variation analysis, principal component analysis, variance-based metrics and other quality assessment and visualization techniques used to evaluate data structure and reproducibility. The review further highlights how these approaches can be applied to identify systematic sources of variation to support the appropriate implementation of normalization strategies. Improved detection and characterization of variability enhance data quality, reproducibility, and interpretability, which thereby support more reliable biomarker discovery and broader omics applications.