Referencia completa:
S. Tortorella, J. Camacho, G. Cruciani. "GPCA for improved multivariate analysis interpretation in lipidomics". International Workshop Enviromental OMICS Integration & Modelling. 2017
Abstract:
Lipidomics involves the identification and quantitation of thousands of cellular lipid molecular species and the
monitoring of their regulation[1].
Unsupervised multivariate statistical analysis (e.g. PCA) has become an integral part of the lipidomic workflow for the
discovery of lipids responsible for discrimination between two (or even more) pathophysiologically different groups of
samples (e.g., cases versus controls).
Using PCA, data interpretation is frequently hampered by the lack of effective tool for relevant variables identification.
1. Han X. Lipidomics Comprehensive Mass Spectrometry of Lipids. Cambridge University Press; 2016.
2. Camacho J, Rodríguez-Gómez RA, Saccenti E, J. Comp. Graph. Stat. 2016:1-42.
3. Goracci L, Tortorella S, et al Analytical Chemistry, 2017, 89 (11), 6257-64.