title: Analysis and understanding of high-dimensionality data by means of multivariate data analysis.
abstract: 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.
authors.affiliation: Department of Medicinal Chemistry, AstraZeneca R&D, S-22187 Lund. bo.norden@astrazeneca.com
authors.forename: Bo
authors.initials: B
authors.lastname: Nordén
authors.forename: Per
authors.initials: P
authors.lastname: Broberg
authors.forename: Claes
authors.initials: C
authors.lastname: Lindberg
authors.forename: Amelie
authors.initials: A
authors.lastname: Plymoth
mesh_terms: D003627:Data Interpretation, Statistical; D016018:Least-Squares Analysis; D015999:Multivariate Analysis; D025341:Principal Component Analysis / Q000379:methods*