The chemometric models in metabolomics

The metabolomic analysis provides a powerful approach for delving into intricate biological metabolism. Given metabolomics data's complexity and high-dimensional nature, it necessitates applying advanced analytical techniques for meaningful interpretation. This chapter centers on the pivotal role of chemometric tools in metabolomic analysis. These tools encompass a wide range of statistical and computational methods that empower us to extract valuable insights from extensive and intricate metabolomics datasets. They play a itical role in tasks such as data preprocessing, noise reduction, feature selection, and multivariate analysis, thereby enhancing our ability to unveil biologically relevant information. Moreover, these tools facilitate the integration of data from diverse analytical platforms, allowing researchers to identify and validate metabolites that are indicative of specific biological conditions. Additionally, chemometric methods aid in elucidating metabolic pathways and exploring interactions among metabolites, shedding light on the underlying biology. Given the intricacies involved, it is ucial to utilize specific analytical tools, including but not limited to Principal Component Analysis (PCA), Partial Least Squares Projection to Latent Structures (PLS), permutation tests, Random Forest, and K-Nearest Neighbors (KNN). Hence, this chapter is dedicated to navigating the multifaceted landscape of multivariate metabolomics analysis, highlighting both its advantages and limitations.