compute.proto.cor.meta: Function to compute correlations to prototypes in a...
In genefu: Computation of Gene Expression-Based Signatures in Breast Cancer

Description Usage Arguments Value Author(s) See Also Examples

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.

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

`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`.

`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.

Benjamin Haibe-Kains

map.datasets

## 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)