Plot expression patterns of top ranked genes.

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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

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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.

Author(s)

Michael Ochs, Yingying Wei

Examples

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#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"))