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A number of multivariate tools have been proposed to estimate how much the structure of the G-matrix influences the ability of a multivariate phenotype to respond to selection [ 26, 42, 71].
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In addition, a number of univariate and multivariate statistical tools have been incorporated into the workflow to aid in elucidating the features that have the highest statistical significance and drive sample clustering.
In the last decades, several applications using FT-IR spectroscopy and multivariate chemometric tools have been published to monitor yeast metabolism taking advantage of the high sensitivity to small changes in the composition of cells [ 12– 15].
Many multivariate visualization and classification tools have been developed [ 1, 2], both 'unsupervised' and 'supervised' (discriminating on treatment group).
These tools have been generalized to the multivariate analysis of high-throughput biological datasets, where the main challenge is the problem of small sample size and high dimensionality.
For human-computer interaction, front-end tools have been worked out where advanced multivariate statistical models are applied to extract the most informative features.
The tools have since changed.
All tools have their potentialities and biases.
Many tools have several options.
Hierarchical cluster analysis, HCA, as a multivariate statistical tool has also been widely used to formulate hydrochemical models on the basis of available data (Meng and Maynard 2001).
A machine learning multivariate feature tool has been implemented in parallel and locally installable form, for plant miRNA target identification.
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