Description Usage Arguments Value Author(s) See Also Examples
This function computes meta-estimate of pairwise correlation coefficients for a set of genes from a list of gene expression datasets.
1 | compute.pairw.cor.meta(datas, method = c("pearson", "spearman"))
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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. |
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
map.datasets
, compute.proto.cor.meta
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ## 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)
## compute meta-estimate of pairwise correlation coefficients
pairwcor <- compute.pairw.cor.meta(datas=datas.mapped$datas, method="pearson")
str(pairwcor)
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