summary: Summarizing the output from subtype

In subtype: Cluster analysis to find molecular subtypes and their assessment

Description

summary summarizes the output from subtype.

Usage

summary(object,...)

Arguments

object	the output from subtype
...	criterion for filtering out uninteresting cases

Details

summary provides a measure based on FDR and its p-value for assessing the subtypes.

Value

NFDR01 : see the reference

Author(s)

Andrey Alexeyenko, Woojoo Lee (maintainer:[e-mail]) and Yudi Pawitan

References

Alexeyenko, A. et al. (2011) Estimation of false discovery rate in a heterogeneous population.

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

set.seed(1234)
p<-100   #num.variables
n1<-5    #number of sample in population 1
n2<-5    #num.samples from population 2 

group<-c(rep(1,length.out=n1),rep(2,length.out=n2))
data<-matrix(rnorm((n1+n2)*p),(n1+n2),p)

############################

dimnames(data)[[1]]<-as.character(paste("P",runif(nrow(data),0,1),sep="")) ### making row names
dimnames(data)[[2]]<-as.character(paste("G",runif(ncol(data),0,1),sep="")) ### making column names

### The following procedure takes ~ 1 minute.
A=subtype(
   GEset = t(data),
   outcomeLabels = group,
   Npermutes = 2, 
   Nchunks = 5, 
   NclustersASet = 3,
   seed=1234
)

summary(A,f.out=0)  ### f.out can be used for filtering out uninteresting subtypes. e.g. if f.out=2, we ignore subtypes having N01_0<=2.

subtype documentation built on May 2, 2019, 9:23 a.m.