GPCA for improved multivariate analysis interpretation in lipidomics (poster)

S. Tortorella; José Camacho; G. Cruciani

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.

Research areas:

• Data processing and networkmetrics

Year:

2017

Type of Publication:

Proceedings

Address:

Barcelona, Spain

Month:

October