Analysis and understanding of high-dimensionality data by means of multivariate data analysis.

Multivariate analysis such as principal-components analysis (PCA) and partial-least-squares-discriminant analysis (PLS-DA) have been applied to peptidomics data from clinical urine samples subjected to LC/MS analysis. We show that it is possible to use these methods to get information from a complex set of clinical data. The aim of the work is to use this information as a first step in the further search for clinical biomarker data. It is possible to identify peptide-biomarker fingerprints related to disease diagnosis and progression. Further, we review clinical proteomics and pharmacogenomics data analyzed with the same multivariate approach.