cea {RedeR} | R Documentation

## Co-expression analysis.

### Description

Simple function for correlation analysis. This function computes a null distribution via permutation and returns the significant correlation values.

### Usage

    cea(x, sig=0.01, p.adj.method="fdr", cor.method="spearman", nper=1000, plotcea=TRUE, ...)

### Arguments

`x` | A matrix or data frame.
`sig` | Significance threshold.
`p.adj.method` | Correction method passed to "p.adjust" function.
`cor.method` | Correlation method passed to "cor" function.
`nper` | Number of permutations.
`plotcea` | Logical value, option to plot density and the null distributions.
`...` | Additional arguments passed to plotcea option.

### Details

Additional arguments:

n.breaks

  If plotcea=TRUE, 'n.breaks' sets the number of histogram breaks (Default=100 <integer>).

plotnull

  If plotcea=TRUE, 'plotnull' sets whether to plot the null distribution (Default=TRUE <logical>).

avnull

  If plotcea=TRUE, 'avnull' takes the average null distribution (Default=TRUE <logical>).

nullcol

  If plotcea=TRUE, 'nullcol' sets the color of the null distribution (Default="black" <character>).

### Value

An adjacency matrix with significant correlation values.

### Author(s)

Mauro Castro

### See Also

`cor` `p.adjust`

### Examples

    data(ER.deg)
    #--- a gene expression matrix
    exp <- ER.deg$exp
    #--- a sample from gx!!
    idx <- sample(1:nrow(exp))[1:100]
    exp <- exp[idx,]

    ## Not run:

    res <- cea(x=exp, nper=100) #ps set 'nper' for at least 1000

    ## End(Not run)

[Package RedeR version 1.35.0 Index]