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, padj.method="fdr", cor.method="spearman", nper=1000,
    regulators=NULL, plotcea=TRUE, ...)

### Arguments

`x` | A matrix or data frame. Varibles should be on rows, samples on columns. The 'x' matrix should be named, with unique row names.
`sig` | Significance threshold.
`padj.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.
`regulators` | An optional character vector listing varibles (e.g. genes) regarded as regulators.
`...` | 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

    #--- Load a a gene expression matrix
    data(ER.deg)
    x <- ER.deg$exp

    #--- NOTE1: 'x' should have varibles on rows and samples on columns!
    #--- NOTE2: 'x' should be named, with unique names on rows.
    x[1:3,1:3]
    #      GSM286031 GSM286032 GSM286033
    #1      7.106765  7.225175  7.217780
    #10005  8.108632  8.012673  7.932592
    #10007 10.225053 10.068826 10.236548

    #--- Get a subset, with 100 genes, for a quick demonstration!
    x <- x[sample(1:nrow(x))[1:100], ]

    ## Not run:
    #--- Run correlation analysis, estimating significant
    #--- associations by permutation.
    #--- NOTE: set 'nper' for at least 1000
    res <- cea(x=x, nper=100)

    ## End(Not run)

[Package RedeR version 1.40.5 Index]