cit.assignBcmst: cit.assignBcmst

In citbcmst: CIT Breast Cancer Molecular SubTypes Prediction

Description

assign expression data sample(s) to CIT Breast Cancer Molecular Subtypes

Usage

cit.assignBcmst(  data,
data.annot,
data.colId="Probe.Set.ID",
data.colMap=c("Probe.Set.ID","Gene.Symbol","Ensembl","UniGene.ID")[1],
citbcmst.annot=NULL,citbcmst.colId="Probe.Set.ID",
citbcmst.colMap=c("Probe.Set.ID","Gene.Symbol","Ensembl","UniGene.ID")[1],
dist.method="dlda",
dist.difftopcentcutoff=NULL,
dist.disttocentcutoff=NULL,
dist.maxcutoff=NULL,
plot=FALSE)

Arguments

data	a data.frame of expression data with id as rownames
data.annot	a data.frame of data probes annotations
data.colId	name of the column in data.annot containing data probes id
data.colMap	name of the column in data.annot containing data probes names to map
citbcmst.annot	affymetrix annotation data.frame, if NULL (default) takes the embeded annotation in object citbcmst$data.annot
citbcmst.colId	name of the column in citbcmst.annot corresponding to rownames of citbcmst$data. Default "Probe.Set.ID"
citbcmst.colMap	name of the column in citbcmst.annot containing the same annotation as in data.colMap
dist.method	metric to compute distance to assign a sample to a subtype. Default for Affymetrix data "dlda". For other platforms "pearson".
dist.difftopcentcutoff	cut-off on the differences between distances to centroids. If the distance is inferior to this cut-off for n centroids the sample is assigned to the n subtypes in the output variable citbcmst.mixed. If NULL, the cut-off is defined as the 1st decile of the difference between the top 2 closest centroids on data used to compute centroids.
dist.disttocentcutoff	cut-off on the mad (median absolute deviation) of distances to the centroid to define a sample as outlier. If the distance to the centroid of the assigned subtype is superior to sdisttocent*mad(distances of centroids samples to this centroid)
dist.maxcutoff	samples for which nearest centroid is above this threshold are discarted (used only if dis.meth = "pearson" or "spearman")
plot	if TRUE plot an acp of citdata used to classify, and of the input data with subtype affectation and dist to centroid class

Value

a data.frame with 4 columns : "citbcmst" classification to the closest of the 6 subtypes, "citbcmst.mixed" classification to the n closest subtypes depending on dist.difftopcentcutoff, "citbcmst.core" classification without outlier and mixed samples and citbcmst.confidence a confidence assignation annotation (CORE, MIXED or OUTLIER)

Note

This is a contribution from the Tumor Identity Cards (CIT) program founded by the 'Ligue Nationale Contre le Cancer' (France): http://cit.ligue-cancer.net. For any question please contact [e-mail]

Author(s)

Laetitia Marisa

Examples

load(list.files(system.file("extdata", package="citbcmst"), full.names=TRUE)[1])
# load exp.norm.bertheau07 object stored in /inst/extdata
exp.annot.bertheau07 <- data.frame(id=rownames(exp.norm.bertheau07), stringsAsFactors=FALSE,
row.names=rownames(exp.norm.bertheau07) )
citbcmst.bertheau07 <- cit.assignBcmst(    data=exp.norm.bertheau07,
                                           data.annot=exp.annot.bertheau07,
                                           data.colId="id",
                                           data.colMap="id" ,
                                           citbcmst.colMap="Probe.Set.ID",
                                           dist.method="dlda",
                                           plot=TRUE
                                 ) 

citbcmst documentation built on May 2, 2019, 6:03 a.m.