English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/158101
COMPARTIR / IMPACTO:
Estadísticas
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:
Título

Knowledge integration strategies for untargeted metabolomics based on MCR-ALS analysis of CE-MS and LC-MS data

AutorOrtiz-Villanueva, Elena; Benavente, Fernando; Piña, Benjamin; Sanz-Nebot, Victoria M.; Tauler, Romà; Jaumot, Joaquim
Palabras claveCapillary electrophoresis-mass spectrometry
Data fusion
Knowledge integration
MCR-ALS
Liquid chromatography-mass spectrometry
Untargeted metabolomics
Fecha de publicación25-jul-2017
EditorElsevier
CitaciónAnalytica Chimica Acta: 978: 10-23 (2017)
ResumenIn this work, two knowledge integration strategies based on multivariate curve resolution alternating least squares (MCR-ALS) were used for the simultaneous analysis of data from two metabolomic platforms. The benefits and the suitability of these integration strategies were demonstrated in a comparative study of the metabolite profiles from yeast (Saccharomyces cerevisiae) samples grown in non-fermentable (acetate) and fermentable (glucose) carbon source. Untargeted metabolomics data acquired by capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC-MS) were jointly analysed. On the one hand, features obtained by independent MCR-ALS analysis of each dataset were joined to obtain a biological interpretation based on the combined metabolic network visualization. On the other hand, taking advantage of the common spectral mode, a low-level data fusion strategy was proposed merging CE-MS and LC-MS data before the MCR-ALS analysis to extract the most relevant features for further biological interpretation. Then, results obtained by the two presented methods were compared. Overall, the study highlights the ability of MCR-ALS to be used in any of both knowledge integration strategies for untargeted metabolomics. Furthermore, enhanced metabolite identification and differential carbon source response detection were achieved when considering a combination of LC-MS and CE-MS based platforms. © 2017 Elsevier B.V.
Versión del editorhttps://doi.org/10.1016/j.aca.2017.04.049
URIhttp://hdl.handle.net/10261/158101
DOI10.1016/j.aca.2017.04.049
Aparece en las colecciones: (IDAEA) Artículos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
Knowledge integration strategies for untargeted metabolomics based on MCR-ALS analysis of CE-MS and LC-MS data.docx Embargado hasta 25 de julio de 20193,7 MBMicrosoft Word XMLVisualizar/Abrir     Petición de una copia
Mostrar el registro completo
 

Artículos relacionados:


NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.