rorS: Function to compute the rorS signature as published by Parker...

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

Function to compute the rorS signature as published by Parker et al 2009

Description

This function computes signature scores and risk classifications from gene expression values following the algorithm used for the rorS signature as published by Parker et al 2009.

Usage

rorS(data, annot, do.mapping = FALSE, mapping, 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 at least one column named "EntrezGene.ID", dimnames being properly defined.
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. Note that for Affymetrix HGU datasets, the mapping is not necessary.
mapping	Matrix with columns "EntrezGene.ID" and "probe" used to force the mapping such that the probes are not selected based on their variance.
verbose	TRUE to print informative messages, FALSE otherwis.

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

Parker, Joel S. and Mullins, Michael and Cheang, Maggie C.U. and Leung, Samuel and Voduc, David and Vickery, Tammi and Davies, Sherri and Fauron, Christiane and He, Xiaping and Hu, Zhiyuan and Quackenbush, John F. and Stijleman, Inge J. and Palazzo, Juan and Marron, J.S. and Nobel, Andrew B. and Mardis, Elaine and Nielsen, Torsten O. and Ellis, Matthew J. and Perou, Charles M. and Bernard, Philip S. (2009) "Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes", Journal of Clinical Oncology, 27(8):1160-1167

Examples

# load NKI dataset
data(vdxs)
data(pam50)

# compute relapse score
rs.vdxs <- rorS(data=data.vdxs, annot=annot.vdxs, do.mapping=TRUE)

bhklab/genefu documentation built on June 2, 2022, 2:56 p.m.