compare.proto.cor {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

    compare.proto.cor(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.

### Author(s)

Benjamin Haibe-Kains

### 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 <- compare.proto.cor(gene.cor=probecor$cor, proto.cor=protocor$cor,
      nn=probecor$cor.n, p.adjust.m="fdr")
    head(res)

[Package genefu version 2.12.0 Index]