The role of chemometrics in metabolomics studies

Metabolomics is a growing area in the field of systems biology [1]. In this study the role of chemometrics in metabolomics is considered. Chemometrics can be used throughout the steps involved in metabolomics including data acquisition, raw data pre-processing, pattern analyses and identification of important features. The real challenge is to identify the biomarkers of a particular disease from hundreds of metabolites identified by metabolomics [1-4]. Data acquisition in appropriate platform generates a data file commonly called as raw data. The process of obtaining meaningful information from raw data for further analysis is called data pre-processing [3]. Following pre-processing metadata or the data matrix has been obtained and further analysis for pattern analyses and identification of important feature are crucial steps. It involved multiple statistical steps to identify a robust biomarker of a set of biomarkers [1]. Depending on complexity of the data matrix and between or within group variations, both uni- and multivariate statistical methods can be used in order to identify biomarkers [4]. Some practical examples of metabolomics in presence of different pre-processing methods and their effect on metabolite identification has been considered.