cea {RedeR} | R Documentation |
Simple function for correlation analysis. This function computes a null distribution via permutation and returns the significant correlation values.
cea(x, sig=0.01, padj.method="fdr", cor.method="spearman", nper=1000, regulators=NULL, plotcea=TRUE, ...)
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. |
Additional arguments:
If plotcea=TRUE, 'n.breaks' sets the number of histogram breaks (Default=100 <integer>).
If plotcea=TRUE, 'plotnull' sets whether to plot the null distribution (Default=TRUE <logical>).
If plotcea=TRUE, 'avnull' takes the average null distribution (Default=TRUE <logical>).
If plotcea=TRUE, 'nullcol' sets the color of the null distribution (Default="black" <character>).
An adjacency matrix with significant correlation values.
Mauro Castro
#--- 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)