Data analysis strategies for targeted and untargeted LC-MS metabolomic studies: Overview and workflow

Data analysis is a very challenging task in LC-MS metabolomic studies. The use of powerful analytical techniques (e.g., high-resolution mass spectrometry) provides high-dimensional data, often with noisy and collinear structures. Such amount of information-rich mass spectrometry data requires extensive processing in order to handle metabolomic data sets appropriately and to further assess sample classification/discrimination and biomarker discovery. This review shows the steps involved in the data analysis workflow for both targeted and untargeted metabolomic studies. Especial attention is focused on the distinct methodologies that have been developed in the last decade for the untargeted case. Furthermore, some powerful and recent alternatives based on the use of chemometric tools will also be discussed. In general terms, this review helps researchers to critically explore the distinct alternatives for LC-MS metabolomic data analysis to better choose the most appropriate for their case study. © 2016The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 320737. First author acknowledges the Spanish Government (Ministerio de Educación, Cultura y Deporte) for a predoctoral FPU scholarship (FPU13/04384).Peer reviewe