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.
Nordén
Bo
B
Department of Medicinal Chemistry, AstraZeneca R&D, S-22187 Lund. bo.norden@astrazeneca.com
Broberg
Per
P
Lindberg
Claes
C
Plymoth
Amelie
A
D003627:Data Interpretation, Statistical; D016018:Least-Squares Analysis; D015999:Multivariate Analysis; D025341:Principal Component Analysis / Q000379:methods*