\name{cor2An}
 \alias{cor2An}
 \title{Correlation between two matrices}
 \usage{
   cor2An(mat1, mat2, lab,
     type.corr = c("pearson", "spearman"), cutoff_zval = 0)
 }
 \arguments{
   \item{mat1}{matrix of dimension features/genes x number
   of components, e.g the results of an ICA decomposition}
 
   \item{mat2}{matrix of dimension features/genes x number
   of components, e.g the results of an ICA decomposition}
 
   \item{lab}{The vector of labels for mat1 and mat2, e.g
   the the names of the two datasets on which were
   calculated the two decompositions}
 
   \item{type.corr}{Type of correlation, either
   \code{'pearson'} or \code{'spearman'}}
 
   \item{cutoff_zval}{cutoff_zval: 0 (default) if all genes
   are used to compute the correlation between the
   components, or a threshold to compute the correlation on
   the genes that have at least a scaled projection higher
   than cutoff_zval.}
 }
 \value{
   This function returns a list consisting of: \item{cor}{a
   matrix of dimensions '(nbcomp1+nbcomp2) x
   (nbcomp1*nbcomp2)', containing the correlation values
   between each pair of components,} \item{pval}{ a matrix
   of dimension '(nbcomp1+nbcomp2) x (nbcomp1*nbcomp2)',
   containing the p-value of the correlation tests for each
   pair of components,} \item{inter}{ the intersection
   between the features/genes of \code{mat1} and
   \code{mat2},} \item{labAn}{ the labels of the compared
   matrices.}
 }
 \description{
   This function measures the correlation between two
   matrices containing the results of two decompositions.
 }
 \details{
   Before computing the correlations, the components are
   scaled and restricted to common row names.
 
   It must be taken into account by the user that if
   \code{cutoff_zval} is different from NULL or zero, the
   computation will be slowler since each pair of component
   is treated individually.
 
   When \code{cutoff_zval} is specified, for each pair of
   components, genes that are included in the circle of
   center 0 and radius \code{cutoff_zval} are excluded from
   the computation of the correlation between the gene
   projection of the two components.
 }
 \examples{
 cor2An(mat1=matrix(rnorm(10000),nrow=1000,ncol=10), mat2=matrix(rnorm(10000),nrow=1000,ncol=10),
        lab=c("An1","An2"), type.corr="pearson")
 }
 \author{
   Anne Biton
 }
 \seealso{
   \code{rcorr}, \code{cor.test}, \code{\link{compareAn}}
 }