ovcCrijns: Function to compute the subtype scores and risk...

In bhklab/genefu: Computation of Gene Expression-Based Signatures in Breast Cancer

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 the 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

A list with items:

• 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.

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)

bhklab/genefu documentation built on Nov. 30, 2024, 9:03 p.m.