PlotTopPCOPA {coGPS} | R Documentation |
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.
PlotTopPCOPA(exprslist, PCOPAresult, topcut, typelist)
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. |
Michael Ochs, Yingying Wei
#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"))