  The canonical correlation analysis (CCA) is commonly used to analyze data
sets with paired data, e.g. measurements of gene expression and metabolomic
intensities of the same experiments. This allows to find interesting
relationships between the data sets, e.g. they can be assigned to biological
processes. However, it can be difficult to interpret the processes and often
the relationships observed are not related to the experimental design but to
some unknown parameters.
  Here we present an extension of the penalized CCA, the supervised penalized
approach (spCCA), where the experimental design is used as a third data set and
the correlation of the biological data sets with the design data set is
maximized to find interpretable and meaningful canonical variables. The spCCA
was successfully tested on a data set of Arabidopsis thaliana with gene
expression and metabolite intensity measurements and resulted in eight
significant canonical variables and their interpretation. We provide an
R-package under the GPL license.
