A recurring problem in 'omics' measurements, in particular in gene expression and metabolomics, is the small number of samples versus high dimensionality; the traditional multivariate methods break down due to the singularity of the covariance matrix.
Four variables included in the bivariate analysis were excluded from multivariate regression and further methods due to complete collinearity with other variables in the model.
However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas.
Multivariate methods should, therefore, be considered instead.
Data were analyzed using univariate and multivariate methods.
The proposed discretization algorithms are incremental and multivariate methods.
The clustering is made by the use of multivariate methods.
The chemical data obtained were elaborated with statistical multivariate methods.
It can further be divided into bivariate and multivariate methods.
Agronomic potential was assessed with six descriptors and multivariate methods.
