论文信息 - Analysis and Understanding of High‐Dimensionality Data by Means of Multivariate Data Analysis

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.

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