cor2An: Correlation between two matrices

In bitona/MineICA: Analysis of an ICA decomposition obtained on genomics data

Correlation between two matrices

Description

This function measures the correlation between two matrices containing the results of two decompositions.

Usage

  cor2An(mat1, mat2, lab,
    type.corr = c("pearson", "spearman"), cutoff_zval = 0)

Arguments

mat1	matrix of dimension features/genes x number of components, e.g the results of an ICA decomposition
mat2	matrix of dimension features/genes x number of components, e.g the results of an ICA decomposition
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
type.corr	Type of correlation, either 'pearson' or 'spearman'
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.

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 cutoff_zval is different from NULL or zero, the computation will be slowler since each pair of component is treated individually.

When cutoff_zval is specified, for each pair of components, genes that are included in the circle of center 0 and radius cutoff_zval are excluded from the computation of the correlation between the gene projection of the two components.

Value

This function returns a list consisting of:

cor	a matrix of dimensions '(nbcomp1+nbcomp2) x (nbcomp1*nbcomp2)', containing the correlation values between each pair of components,
pval	a matrix of dimension '(nbcomp1+nbcomp2) x (nbcomp1*nbcomp2)', containing the p-value of the correlation tests for each pair of components,
inter	the intersection between the features/genes of mat1 and mat2,
labAn	the labels of the compared matrices.

Author(s)

Anne Biton

See Also

rcorr, cor.test, compareAn

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")

bitona/MineICA documentation built on April 23, 2023, 1:41 p.m.