compute.proto.cor.meta {genefu} | R Documentation |
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.
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 |
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
## 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)