@proceedings{tortorella_omics_2017,
author = "S. Tortorella and Camacho, Jos{\'e} and G. Cruciani",
abstract = "Individual lipid species, lipid families, or specific lipid changes from sample to sample can be easily revealed using multivariate statistical procedures. To this end, both unsupervised (e.g., principal component analysis: PCA) and supervised (e.g., partial least-squares: PLS) algorithms can be used [2]. However, the tendency of lipidomic tools is to make multivariate statistical analysis as simple as possible for the user, leading in many cases to “black boxes” in which advanced data interpretation is very limited. Furthermore, interpretation of standard MA tools, like PCA loading plots, may be challenging due to the dimensionality of the data, since the principal components are linear combinations of all the variables simultaneously. To overcome these limitations, here we demonstrate that Group-wise Principal Component Analysis (GPCA) [3], a recently proposed extension of PCA for exploratory analysis, can be successfully applied as a user-friendly advanced tool for the visualization and interpretation of the statistical analysis. GPCA starts from the groups of variables identified by MEDA (Missing-data for Exploratory Data Analysis) [4] and performs a constrained PCA-like calibration where loadings are restricted to present non-zero values only for a group of variables. In this way, the obtained Group-wise PCs (GPCs) are sparse factorizations that can be inspected individually (one GPC at a time), simplifying interpretation. To further make MA outcomes interpretation easier and faster, we also introduce here the Discriminative score (D-score), which assesses the discriminative power of each GPC according to an arbitrary desired clusterization, in turn allowing the ranking of the GPCs.
Examples of how and why GPCA combined with the D-score are valuable solutions for the exploration and interpretation of complex real lipidomic case studies will be given.",
address = "Barcelona, Spain",
month = "October",
title = "{GPCA} for improved multivariate analysis interpretation in lipidomics",
year = "2017",
}