genefu (version 2.4.2)

compute.proto.cor.meta: Function to compute correlations to prototypes in a meta-analytical framework

Description

This function computes meta-estimate of correlation coefficients between a set of genes and a set of prototypes from a list of gene expression datasets.

Usage

compute.proto.cor.meta(datas, proto, method = c("pearson", "spearman"))

Arguments

datas

List of datasets. Each dataset is a matrix of gene expressions with samples in rows and probes in columns, dimnames being properly defined. All the datasets must have the same probes.

proto

Names of prototypes (e.g. their EntrezGene ID).

method

Estimator for correlation coefficient, can be either pearson or spearman.

Value

• cor
  Matrix of meta-estimate of correlation coefficients with probes in rows and prototypes in columns.
• cor.n
  Number of samples used to compute meta-estimate of correlation coefficients.

See Also

map.datasets

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")
str(probecor)