cit.assignCcmst: cit.assignCcmst

In citccmst: CIT Colon Cancer Molecular SubTypes Prediction

Description

assign expression data sample(s) to CIT Colon Cancer Molecular Subtype(s)

Usage

cit.assignCcmst(  data,
data.annot,
data.colId="Probe.Set.ID",
data.colMap=c("Probe.Set.ID","Gene.Symbol","Ensembl","UniGene.ID")[1],
citccmst.annot=NULL,citccmst.colId="Probe.Set.ID",
citccmst.colMap=c("Probe.Set.ID","Gene.Symbol","Ensembl","UniGene.ID")[1],
dist.method="dqda",
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
citccmst.annot	affymetrix annotation data.frame, if NULL (default) take the embedded annotation in object citccmst$data.annot
citccmst.colId	name of the column in citccmst.annot corresponding to rownames of citccmst$data. Default "Probe.Set.ID"
citccmst.colMap	name of the column in citccmst.annot containing the same annotation as in data.colMap
dist.method	metric to compute distance to assign a sample to a subtype ("pearson", "dlda", "dqda","euclidian"). Default "dqda".
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 citccmst.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 discarded (used only if dis.meth = "pearson" or "spearman")
plot	if TRUE plot an acp of cit data used to classify, and of the input data with subtype affectation and dist to centroid class

Value

a data.frame with 4 columns : "citccmst" assignments to the closest of the 6 subtype centroids, "citccmst.mixed" assignment to the n closest subtypes depending on dist.difftopcentcutoff, "citccmst.core" assignments without outlier and mixed samples and citccmst.confidence a confidence assignment 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="citccmst"), full.names=TRUE))
##load citvalid.exp.norm object
#citvalid.exp.annot <- data.frame(id=rownames(citvalid.exp.norm), stringsAsFactors=FALSE,
#                                 row.names=rownames(citvalid.exp.norm) )
#citccmst <- cit.assignCcmst(     data=citvalid.exp.norm,
#                                 data.annot=citvalid.exp.annot,
#                                 data.colId="id",
#                                 data.colMap="id" ,
#                                 citccmst.colMap="Probe.Set.ID",
#                                 plot=TRUE
#                           )
#head(citccmst) 

Example output

citccmst documentation built on May 2, 2019, 9:27 a.m.