From Data Processing to Multivariate Validation - Essential Steps in Extracting Interpretable Information from Metabolomics Data

Title: From Data Processing to Multivariate Validation - Essential Steps in Extracting Interpretable Information from Metabolomics Data

Volume: 12 Issue: 7

Author(s): Mattias Eliasson, Stefan Rannar and Johan Trygg

Affiliation:

Keywords: Multivariate data analysis, data processing, chemometrics, metabolomics, statistical validation, validation procedures, chemometric and machine learning analysis, NMR, downstream data analysis, Filtration, non-linear regression method

Abstract: In metabolomics studies there is a clear increase of data. This indicates the necessity of both having a battery of suitable analysis methods and validation procedures able to handle large amounts of data. In this review, an overview of the metabolomics data processing pipeline is presented. A selection of recently developed and most cited data processing methods is discussed. In addition, commonly used chemometric and machine learning analysis methods as well as validation approaches are described.

Cite this article as:

Eliasson Mattias, Rannar Stefan and Trygg Johan, From Data Processing to Multivariate Validation - Essential Steps in Extracting Interpretable Information from Metabolomics Data, Current Pharmaceutical Biotechnology 2011; 12 (7) . https://dx.doi.org/10.2174/138920111795909041