cor2An {MineICA} | R Documentation

## 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")

[Package MineICA version 1.30.0 Index]