claudinLow: Claudin-low classification for Breast Cancer Data

Description Usage Arguments References See Also Examples

View source: R/claudinLow.R

Description

Subtyping method for identifying Claudin-Low Breast Cancer Samples. Code generously provided by Aleix Prat.

Usage

1
claudinLow(x,classes="",y,nGenes="",priors="equal",std=F,distm="euclidean",centroids=F)

Arguments

x

the data matrix of training samples, or pre-calculated centroids

classes

a list labels for use in coloring the points

y

the data matrix of test samples

nGenes

the number of genes selected when training the model

priors

'equal' assumes equal class priors, 'class' calculates them based on proportion in the data

std

when true, the training and testing samples are standardized to mean=0 and var=1

distm

the distance metric for determining the nearest centroid, can be one of euclidean, pearson, or spearman

centroids

when true, it is assumed that x consists of pre-calculated centroids

References

Aleix Prat, Joel S Parker, Olga Karginova, Cheng Fan, Chad Livasy, Jason I Herschkowitz, Xiaping He, and Charles M. Perou (2010) "Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer", Breast Cancer Research, 12(5):R68

See Also

medianCtr, claudinLowData

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
data(claudinLowData)

#Training Set
train<-claudinLowData
train$xd<- medianCtr(train$xd)
# Testing Set
test<-claudinLowData
test$xd<- medianCtr(test$xd)

# Generate Predictions
predout<-claudinLow(train$xd, as.matrix(train$classes$Group,ncol=1), test$xd)

# Obtain results
results <- cbind(predout$predictions, predout$distances)
#write.table(results,"T.E.9CELL.LINE_results.txt",sep="\t",col=T, row=F)

Example output

Loading required package: survcomp
Loading required package: survival
Loading required package: prodlim
Loading required package: mclust
Package 'mclust' version 5.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: limma
Loading required package: biomaRt
Loading required package: iC10
Loading required package: pamr
Loading required package: cluster
Loading required package: iC10TrainingData
Loading required package: AIMS
Loading required package: e1071
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following object is masked from 'package:limma':

    plotMA

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colMeans, colSums, colnames, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
    pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
    setdiff, sort, table, tapply, union, unique, unsplit, which,
    which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

[1] "Number of genes used: 807"

genefu documentation built on Nov. 1, 2018, 2:25 a.m.