compute.pairw.cor.meta {genefu} | R Documentation

## Function to compute pairwise correlations in a meta-analytical framework

### Description

This function computes meta-estimate of pairwise correlation coefficients for a set of genes from a list of gene expression datasets.

### Usage

    compute.pairw.cor.meta(datas, 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.
`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.

### Author(s)

Benjamin Haibe-Kains

### See Also

`map.datasets`, `compute.proto.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)
    ## compute meta-estimate of pairwise correlation coefficients
    pairwcor <- compute.pairw.cor.meta(datas=datas.mapped$datas, method="pearson")
    str(pairwcor)

[Package genefu version 2.14.0 Index]