coGPS (version 1.16.0)

PlotTopPCOPA: Plot expression patterns of top ranked genes.

Description

It first sorts the expression value $exprslist[[i]]\$exprs[j,]$ among the baseline samples(e.g. normal ones) and comparison group (e.g. tumor ones)seperately for selected gene $j$, and then plot the sorted expression values. The first argument $exprslist$ should be the same one as for $PCOPA$; the second argument $PCOPAresult$ should be an output of PCOPA; the third argument $topcut$ determines how far we would go down the top ranked list; and the last argument $typelist$ is a vector specifying the titles for each graph corresponds to a specific study.

Usage

PlotTopPCOPA(exprslist, PCOPAresult, topcut, typelist)

Arguments

exprslist

Each element of $exprslist$ is a list with the first element being $exprs$ and the second element being $classlab$. Each row of $exprs$ represents one gene and each column represents one sample. $classlab$ is a zero-one vector indicating the status of samples. We use 0 for the baseline group, usually the normal group, and 1 for the comparison group, usually the tumor group.

PCOPAresult

Output of PCOPA.

topcut

Cutoff of top ranked gene list.

typelist

A vector specifying the titles for each graph corresponds to a specific study.

Examples

#read in data
data(Exon_exprs_matched)
data(Methy_exprs_matched)
data(CNV_exprs_matched)
data(Exon_classlab_matched)
data(Methy_classlab_matched)
data(CNV_classlab_matched)
head(Exon_exprs_matched)

#exprslist[[i]]$exprs should be in matrix format
Exon_exprs<-as.matrix(Exon_exprs_matched)
Methy_exprs<-as.matrix(Methy_exprs_matched)
CNV_exprs<-as.matrix(CNV_exprs_matched)

#exprslist[[i]]$classlab should be in vector format
Exon_classlab<-unlist(Exon_classlab_matched)
Methy_classlab<-unlist(Methy_classlab_matched)
CNV_classlab<-unlist(CNV_classlab_matched)

#make an exprslist consisting 3 studies
trylist<-list()
trylist[[1]]<-list(exprs=Exon_exprs,classlab=Exon_classlab)
trylist[[2]]<-list(exprs=Methy_exprs,classlab=Methy_classlab)
trylist[[3]]<-list(exprs=CNV_exprs,classlab=CNV_classlab)

#calculate P-value based statistics for outlier gene detection and output the outlier gene list for each patient
a7<-PCOPA(trylist,0.05,side=c("up","down","up"),type="subtype")

#plot expression patterns of top ranked genes. 
PlotTopPCOPA(trylist,a7,topcut=1,typelist=c("Exon","Methy","CNV"))