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Analysis of longitudinal metabolomic data using multivariate curve resolution-alternating least squares and pathway analysis

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Analysis of longitudinal metabolomic data using multivariate curve resolution-alternating least squares and pathway analysis

dc.contributor.author	Ten Doménech, Isabel
dc.contributor.author	Moreno Torres, Marta
dc.contributor.author	Sanjuan Herráez, Juan Daniel
dc.contributor.author	Pérez Guaita, David
dc.contributor.author	Quintás Soriano, Guillermo
dc.contributor.author	Kuligowski. Julia
dc.date.accessioned	2023-05-19T15:03:36Z
dc.date.available	2023-05-19T15:03:36Z
dc.date.issued	2023
dc.identifier.citation	Ten Doménech, Isabel Moreno Torres, Marta Sanjuan Herráez, Juan Daniel Pérez Guaita, David Quintás Soriano, Guillermo Kuligowski. Julia 2023 Analysis of longitudinal metabolomic data using multivariate curve resolution-alternating least squares and pathway analysis Chemometrics and Intelligent Laboratory Systems 232
dc.identifier.uri	https://hdl.handle.net/10550/86735
dc.description.abstract	Extraction of meaningful biological information from longitudinal metabolomic studies is a major challenge and typically involves multivariate analysis and dimensional reduction methods for data visualization such as Principal Component Analysis or Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Besides, a variety of computational tools have been developed to identify changes in metabolic pathways including functional analysis and pathway analysis. In this work, the joint analysis of results from MCR-ALS and metabolic pathway analysis is proposed to facilitate the interpretation of dynamic changes in longitudinal metabolomic data. The strategy is based on the use of MCR-ALS to remove unstructured random variation in the raw data, thus facilitating the interpretation of dynamic changes observed by metabolic pathway analysis over time. A simulated data set representing dynamic longitudinal changes in the intensities of a subset of metabolites from three metabolic pathways was initially used to test the applicability of MCR-ALS to support pathway analysis for detecting pathway perturbations. Then, the strategy is applied to real data acquired for the analysis of changes during CD8+ T cell activation. Results obtained show that MCR-ALS facilitates the interpretation of longitudinal metabolomic profiles in multivariate data sets by identifying metabolic pathways associated with each detected dynamic component.
dc.language.iso	eng
dc.relation.ispartof	Chemometrics and Intelligent Laboratory Systems, 2023, num. 232
dc.subject	Biologia
dc.title	Analysis of longitudinal metabolomic data using multivariate curve resolution-alternating least squares and pathway analysis
dc.type	journal article
dc.date.updated	2023-05-19T15:03:37Z
dc.identifier.doi	10.1016/j.chemolab.2022.104720
dc.identifier.idgrec	158848
dc.rights.accessRights	open access