Analytical challenges of untargeted GC-MS-based metabolomics and the critical issues in selecting the data processing strategy.

ABSTRACT: Background: A challenge of metabolomics is data processing the enormous amount of information generated by sophisticated analytical techniques. The raw data of an untargeted metabolomic experiment are composited with unwanted biological and technical variations that confound the biological variations of interest. The art of data normalisation to offset these variations and/or eliminate experimental or biological biases has made significant progress recently. However, published comparative studies are often biased or have omissions. Methods: We investigated the issues with our own data set, using five different representative methods of internal standard-based, model-based, and pooled quality control-based approaches, and examined the performance of these methods

SUBMITTER: Han TL 

PROVIDER: S-EPMC5553085 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature