cea: Co-expression analysis.

In RedeR: Interactive visualization and manipulation of nested networks

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)

Example output

***This is RedeR 1.24.1! For a quick start, please type 'vignette('RedeR')'.
   Supporting information is available at Genome Biology 13:R29, 2012,
   (doi:10.1186/gb-2012-13-4-r29). 
Step 1 ...computing correlation
Step 2 ...computing null distribution
================================================================================
Step 3 ...computing probs
Step 4 ...adjusting pvals

RedeR documentation built on Nov. 8, 2020, 7:45 p.m.