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Analysis of longitudinal metabolomic data using multivariate curve resolution-alternating least squares and pathway analysis
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 Squ...
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Published in: | Chemometrics and intelligent laboratory systems 2023-01, Vol.232, p.104720, Article 104720 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | 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.
•MCR-ALS facilitates the interpretation of longitudinal metabolomic data.•MCR-ALS identifies metabolic pathways associated dynamic components.•Useful to interpret overlapping asynchronous metabolic processes. |
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ISSN: | 0169-7439 1873-3239 |
DOI: | 10.1016/j.chemolab.2022.104720 |