ovcCrijns {genefu} | R Documentation

## Function to compute the subtype scores and risk classifications for the prognostic signature published by Crinjs et al.

### Description

This function computes subtype scores and risk classifications from gene expression values using teh weights published by Crijns et al.

### Usage

    ovcCrijns(data, annot, hgs,
    gmap = c("entrezgene", "ensembl_gene_id", "hgnc_symbol", "unigene"),
    do.mapping = FALSE, verbose = FALSE)

### Arguments

`data` | Matrix of gene expressions with samples in rows and probes in columns, dimnames being properly defined.
`annot` | Matrix of annotations with one column named as gmap, dimnames being properly defined.
`hgs` | vector of booleans with TRUE represents the ovarian cancer patients who have a high grade, late stage, serous tumor, FALSE otherwise. This is particularly important for properly rescaling the data. If `hgs` is missing, all the patients will be used to rescale the subtype score.
`gmap` | character string containing the `biomaRt` attribute to use for mapping if `do.mapping=TRUE`
`do.mapping` | `TRUE` if the mapping through Entrez Gene ids must be performed (in case of ambiguities, the most variant probe is kept for each gene), `FALSE` otherwise.
`verbose` | `TRUE` to print informative messages, `FALSE` otherwise.

### Details

Note that the original algorithm has not been implemented as it necessitates refitting of the model weights in each new dataset. However the current implementation should give similar results.

### Value

`score` | Continuous signature scores
`risk` | Binary risk classification, `1` being high risk and `0` being low risk.
`mapping` | Mapping used if necessary.
`probe` | If mapping is performed, this matrix contains the correspondence between the gene list (aka signature) and gene expression data.

### Author(s)

Benjamin Haibe-Kains

### References

Crijns APG, Fehrmann RSN, de Jong S, Gerbens F, Meersma G J, Klip HG, Hollema H, Hofstra RMW, te Meerman GJ, de Vries EGE, van der Zee AGJ (2009) "Survival-Related Profile, Pathways, and Transcription Factors in Ovarian Cancer" PLoS Medicine, 6(2):e1000024.

### See Also

`sigOvcCrijns`

### Examples

    ## load the ovsCrijns signature
    data(sigOvcCrijns)
    ## load NKI dataset
    data(nkis)
    colnames(annot.nkis)[is.element(colnames(annot.nkis), "EntrezGene.ID")] <- "entrezgene"
    ## compute relapse score
    ovcCrijns.nkis <- ovcCrijns(data=data.nkis, annot=annot.nkis, gmap="entrezgene", do.mapping=TRUE)
    table(ovcCrijns.nkis$risk)

[Package genefu version 2.16.0 Index]