Principal component analysis is the mostly used unsupervised dimensionality reduction method to get an overview of the multivariate profiles and for identifying patterns in data.
Most biomarker discovery projects use unbiased metabolic profiling methods, allowing rapid analysis of large sample sets for multivariate statistics with a sufficient base.
She especially focused on multivariate profile monitoring techniques under correlated process in her MS and PhD thesis.
3D profile method for predicting fibrillizing segments.
The GC-MS-based metabolic profiling platform paired with multivariate statistical method (PCA) has enabled us to highlight the distinct cellular response to individual enantiomers of Carvedilol and several metabolites altered in the treatment of S-Carvedilol.
It combines the application of analytical technologies (e.g. nuclear magnetic resonance spectroscopy (NMR), mass spectrometry (MS)) for metabolic profiling with multivariate statistical methods.
The aims of the study were, first, to determine whether significant differences existed between cases and controls on cognitive tasks, and whether cognitive profiles, derived using a multivariate classification method with data from multiple cognitive tasks, could distinguish between the two groups.
Class prediction was assessed to classify and to predict in which class was enclosed a sample in relation to its expression profile, using multivariate classification methods showed in Supplementary Table S4.
The results of this study show that high throughput mass spectrometric protein profiling, combined with multivariate analysis methods such as PCA and PLS-DA, are excellent methods for investigating and comparing protein expression from brain tissue extracts.
We therefore employed an alternative type of multivariate analysis method known as multivariate Bayes (MVB).
