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

Description

This function computes subtype scores and risk classifications from gene expression values following the algorithm developed by Yoshihara et al, for prognosis in ovarian cancer.

Usage

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ovcYoshihara(data, annot, hgs, 
gmap = c("entrezgene", "ensembl_gene_id", "hgnc_symbol", "unigene", "refseq_mrna"), 
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.

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

Yoshihara K, Tajima A, Yahata T, Kodama S, Fujiwara H, Suzuki M, Onishi Y, Hatae M, Sueyoshi K, Fujiwara H, Kudo, Yoshiki, Kotera K, Masuzaki H, Tashiro H, Katabuchi H, Inoue I, Tanaka K (2010) "Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets", PloS one, 5(3):e9615.

See Also

sigOvcYoshihara

Examples

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

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