compareProtoCor {genefu} | R Documentation

## Function to statistically compare correlation to prototypes

### Description

This function performs a statistical comparison of the correlation coefficients as computed between each probe and prototype.

### Usage

    compareProtoCor(gene.cor, proto.cor, nn,
     p.adjust.m = c("none", "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr"))

### Arguments

`gene.cor` | Correlation coefficients between the probes and each of the prototypes.
`proto.cor` | Pairwise correlation coefficients of the prototypes.
`nn` | Number of samples used to compute the correlation coefficients between the probes and each of the prototypes.
`p.adjust.m` | Correction method as defined in p.adjust.

### Value

Data frame with probes in rows and with three columns: "proto" is the prototype to which the probe is the most correlated, "cor" is the actual correlation, and "signif" is the (corrected) p-value for the superiority of the correlation to this prototype compared to the second highest correlation.

### See Also

compute.proto.cor.meta, compute.pairw.cor.meta

### Examples

    # load VDX dataset
    data(vdxs)
    # load NKI dataset
    data(nkis)
    # reduce datasets
    ginter <- intersect(annot.vdxs[ ,"EntrezGene.ID"], annot.nkis[ ,"EntrezGene.ID"])
    ginter <- ginter[!is.na(ginter)][1:30]
    myx <- unique(c(match(ginter, annot.vdxs[ ,"EntrezGene.ID"]),
      sample(x=1:nrow(annot.vdxs), size=20)))
    data2.vdxs <- data.vdxs[ ,myx]
    annot2.vdxs <- annot.vdxs[myx, ]
    myx <- unique(c(match(ginter, annot.nkis[ ,"EntrezGene.ID"]),
    sample(x=1:nrow(annot.nkis), size=20)))
    data2.nkis <- data.nkis[ ,myx]
    annot2.nkis <- annot.nkis[myx, ]
    # mapping of datasets
    datas <- list("VDX"=data2.vdxs,"NKI"=data2.nkis)
    annots <- list("VDX"=annot2.vdxs, "NKI"=annot2.nkis)
    datas.mapped <- map.datasets(datas=datas, annots=annots, do.mapping=TRUE)
    # define some prototypes
    protos <- paste("geneid", ginter[1:3], sep=".")
    # compute meta-estimate of correlation coefficients to the three prototype genes
    probecor <- compute.proto.cor.meta(datas=datas.mapped$datas, proto=protos,
      method="pearson")
    # compute meta-estimate of pairwise correlation coefficients between prototypes
    datas.proto <- lapply(X=datas.mapped$datas, FUN=function(x, p) {
      return(x[ ,p,drop=FALSE]) }, p=protos)
    protocor <- compute.pairw.cor.meta(datas=datas.proto, method="pearson")
    # compare correlation coefficients to each prototype
    res <- compareProtoCor(gene.cor=probecor$cor, proto.cor=protocor$cor,
    nn=probecor$cor.n, p.adjust.m="fdr")
    head(res)

[Package genefu version 2.26.0 Index]