ovcCrijns: Function to compute the subtype scores and risk...
In genefu: Computation of Gene Expression-Based Signatures in Breast Cancer

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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

`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.

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.

`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.

Benjamin Haibe-Kains

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.

sigOvcCrijns

## 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)