--- /dev/null
+EBTest <-
+function(Data,NgVector=NULL,Vect5End=NULL,Vect3End=NULL,Conditions, sizeFactors, maxround, tau=NULL,CI=NULL,CIthre=NULL, Pool=F, NumBin=1000,ApproxVal=10^-10)
+{
+ Dataraw=Data
+ AllZeroNames=which(rowMeans(Data)==0)
+ NotAllZeroNames=which(rowMeans(Data)>0)
+ if(length(AllZeroNames)>0) print("Remove transcripts with all zero")
+ Data=Data[NotAllZeroNames,]
+ if(!is.null(NgVector))NgVector=NgVector[NotAllZeroNames]
+ if(!length(sizeFactors)==ncol(Data))sizeFactors=sizeFactors[NotAllZeroNames,]
+
+ if(is.null(NgVector))NgVector=rep(1,nrow(Data))
+
+ #Rename Them
+ IsoNamesIn=rownames(Data)
+ Names=paste("I",c(1:dim(Data)[1]),sep="")
+ names(IsoNamesIn)=Names
+ rownames(Data)=paste("I",c(1:dim(Data)[1]),sep="")
+ names(NgVector)=paste("I",c(1:dim(Data)[1]),sep="")
+
+
+ if(!length(sizeFactors)==ncol(Data)){
+ rownames(sizeFactors)=rownames(Data)
+ colnames(sizeFactors)=Conditions
+ }
+
+ NumOfNg=nlevels(as.factor(NgVector))
+ NameList=sapply(1:NumOfNg,function(i)Names[NgVector==i],simplify=F)
+ names(NameList)=paste("Ng",c(1:NumOfNg),sep="")
+ NotNone=NULL
+ for (i in 1:NumOfNg) {
+ if (length(NameList[[i]])!=0)
+ NotNone=c(NotNone,names(NameList)[i])
+ }
+ NameList=NameList[NotNone]
+
+ NoneZeroLength=length(NameList)
+ DataList=vector("list",NoneZeroLength)
+ DataList=sapply(1:NoneZeroLength , function(i) Data[NameList[[i]],],simplify=F)
+ names(DataList)=names(NameList)
+
+ NumEachGroup=sapply(1:NoneZeroLength , function(i)dim(DataList)[i])
+ # Unlist
+ DataList.unlist=do.call(rbind, DataList)
+
+ # Divide by SampleSize factor
+
+ if(length(sizeFactors)==ncol(Data))
+ DataList.unlist.dvd=t(t( DataList.unlist)/sizeFactors)
+
+ if(length(sizeFactors)!=ncol(Data))
+ DataList.unlist.dvd=DataList.unlist/sizeFactors
+
+ # Get FC and VarPool for pooling - Only works on 2 conditions
+ if(ncol(Data)==2){
+ DataforPoolSP.dvd1=matrix(DataList.unlist.dvd[,Conditions==levels(Conditions)[1]],nrow=dim(DataList.unlist)[1])
+ DataforPoolSP.dvd2=matrix(DataList.unlist.dvd[,Conditions==levels(Conditions)[2]],nrow=dim(DataList.unlist)[1])
+ MeanforPoolSP.dvd1=rowMeans(DataforPoolSP.dvd1)
+ MeanforPoolSP.dvd2=rowMeans(DataforPoolSP.dvd2)
+ FCforPool=MeanforPoolSP.dvd1/MeanforPoolSP.dvd2
+ names(FCforPool)=rownames(Data)
+ FC_Use=which(FCforPool>=quantile(FCforPool[!is.na(FCforPool)],.25) &
+ FCforPool<=quantile(FCforPool[!is.na(FCforPool)],.75))
+
+ Var_FC_Use=apply( DataList.unlist.dvd[FC_Use,],1,var )
+ Mean_FC_Use=(MeanforPoolSP.dvd1[FC_Use]+MeanforPoolSP.dvd2[FC_Use])/2
+ MeanforPool=(MeanforPoolSP.dvd1+MeanforPoolSP.dvd2)/2
+ FC_Use2=which(Var_FC_Use>=Mean_FC_Use)
+ Var_FC_Use2=Var_FC_Use[FC_Use2]
+ Mean_FC_Use2=Mean_FC_Use[FC_Use2]
+ Phi=mean((Var_FC_Use2-Mean_FC_Use2)/Mean_FC_Use2^2)
+ VarEst= MeanforPool*(1+MeanforPool*Phi)
+ print(Phi)
+ }
+
+ #DataListSP Here also unlist.. Only two lists
+ DataListSP=vector("list",nlevels(Conditions))
+ DataListSP.dvd=vector("list",nlevels(Conditions))
+ SizeFSP=DataListSP
+ MeanSP=DataListSP
+ VarSP=DataListSP
+ GetPSP=DataListSP
+ RSP=DataListSP
+ CISP=DataListSP
+ tauSP=DataListSP
+ NumSampleEachCon=rep(NULL,nlevels(Conditions))
+
+ for (lv in 1:nlevels(Conditions)){
+ DataListSP[[lv]]= matrix(DataList.unlist[,Conditions==levels(Conditions)[lv]],nrow=dim(DataList.unlist)[1])
+ rownames(DataListSP[[lv]])=rownames(DataList.unlist)
+ DataListSP.dvd[[lv]]= matrix(DataList.unlist.dvd[,Conditions==levels(Conditions)[lv]],nrow=dim(DataList.unlist.dvd)[1])
+ NumSampleEachCon[lv]=ncol(DataListSP[[lv]])
+
+ if(ncol(DataListSP[[lv]])==1 & !is.null(CI)){
+ CISP[[lv]]=matrix(CI[,Conditions==levels(Conditions)[lv]],nrow=dim(DataList.unlist.dvd)[1])
+ tauSP[[lv]]=matrix(tau[,Conditions==levels(Conditions)[lv]],nrow=dim(DataList.unlist.dvd)[1])
+ }
+ # no matter sizeFactors is a vector or a matrix. Matrix should be columns are the normalization factors
+ # may input one for each
+ if(length(sizeFactors)==ncol(Data))SizeFSP[[lv]]=sizeFactors[Conditions==levels(Conditions)[lv]]
+ if(length(sizeFactors)!=ncol(Data))SizeFSP[[lv]]=sizeFactors[,Conditions==levels(Conditions)[lv]]
+
+
+ MeanSP[[lv]]=rowMeans(DataListSP.dvd[[lv]])
+
+ if(length(sizeFactors)==ncol(Data))PrePareVar=sapply(1:ncol( DataListSP[[lv]]),function(i)( DataListSP[[lv]][,i]- SizeFSP[[lv]][i]*MeanSP[[lv]])^2 /SizeFSP[[lv]][i])
+ if(length(sizeFactors)!=ncol(Data))PrePareVar=sapply(1:ncol( DataListSP[[lv]]),function(i)( DataListSP[[lv]][,i]- SizeFSP[[lv]][,i]*MeanSP[[lv]])^2 /SizeFSP[[lv]][,i])
+
+ if(ncol(DataListSP[[lv]])==1 & !is.null(CI))
+ VarSP[[lv]]=as.vector(((DataListSP[[lv]]/tauSP[[lv]]) * CISP[[lv]]/(CIthre*2))^2)
+ if(ncol(DataListSP[[lv]])!=1){
+ VarSP[[lv]]=rowSums(PrePareVar)/ncol( DataListSP[[lv]])
+ names(MeanSP[[lv]])=rownames(DataList.unlist)
+ names(VarSP[[lv]])=rownames(DataList.unlist)
+ GetPSP[[lv]]=MeanSP[[lv]]/VarSP[[lv]]
+ RSP[[lv]]=MeanSP[[lv]]*GetPSP[[lv]]/(1-GetPSP[[lv]])
+ }
+}
+
+
+ MeanList=rowMeans(DataList.unlist.dvd)
+ VarList=apply(DataList.unlist.dvd, 1, var)
+ if(ncol(Data)==2)PoolVar=VarEst
+ if(!ncol(Data)==2){
+ CondWithRep=which(NumSampleEachCon>1)
+ VarCondWithRep=do.call(cbind,VarSP[CondWithRep])
+ PoolVar=rowMeans(VarCondWithRep)
+ }
+ GetP=MeanList/PoolVar
+
+ EmpiricalRList=MeanList*GetP/(1-GetP)
+ EmpiricalRList[EmpiricalRList==Inf] =max(EmpiricalRList[EmpiricalRList!=Inf])
+
+ if(ncol(Data)!=2){
+ Varcbind=do.call(cbind,VarSP)
+ VarrowMin=apply(Varcbind,1,min)
+ }
+
+ if(ncol(Data)==2){
+ Varcbind=VarEst
+ VarrowMin=VarEst
+ }
+ #
+ #
+ GoodData=names(MeanList)[EmpiricalRList>0 & VarrowMin!=0 & EmpiricalRList!=Inf & !is.na(VarrowMin) & !is.na(EmpiricalRList)]
+ NotIn=names(MeanList)[EmpiricalRList<=0 | VarrowMin==0 | EmpiricalRList==Inf | is.na(VarrowMin) | is.na(EmpiricalRList)]
+ #print(paste("ZeroVar",sum(VarrowMin==0), "InfR", length(which(EmpiricalRList==Inf)), "Poi", length(which(EmpiricalRList<0)), ""))
+ EmpiricalRList.NotIn=EmpiricalRList[NotIn]
+ EmpiricalRList.Good=EmpiricalRList[GoodData]
+ EmpiricalRList.Good[EmpiricalRList.Good<1]=1+EmpiricalRList.Good[EmpiricalRList.Good<1]
+ if(length(sizeFactors)==ncol(Data))
+ EmpiricalRList.Good.mat= outer(EmpiricalRList.Good, sizeFactors)
+ if(!length(sizeFactors)==ncol(Data))
+ EmpiricalRList.Good.mat=EmpiricalRList.Good* sizeFactors[GoodData,]
+
+
+ # Only Use Data has Good q's
+ DataList.In=sapply(1:NoneZeroLength, function(i)DataList[[i]][GoodData[GoodData%in%rownames(DataList[[i]])],],simplify=F)
+ DataList.NotIn=sapply(1:NoneZeroLength, function(i)DataList[[i]][NotIn[NotIn%in%rownames(DataList[[i]])],],simplify=F)
+ DataListIn.unlist=do.call(rbind, DataList.In)
+ DataListNotIn.unlist=do.call(rbind, DataList.NotIn)
+
+ DataListSPIn=vector("list",nlevels(Conditions))
+ DataListSPNotIn=vector("list",nlevels(Conditions))
+ EmpiricalRList.Good.mat.SP=vector("list",nlevels(Conditions))
+ for (lv in 1:nlevels(Conditions)){
+ DataListSPIn[[lv]]= matrix(DataListIn.unlist[,Conditions==levels(Conditions)[lv]],nrow=dim(DataListIn.unlist)[1])
+ if(length(NotIn)>0){ DataListSPNotIn[[lv]]= matrix(DataListNotIn.unlist[,Conditions==levels(Conditions)[lv]],nrow=dim(DataListNotIn.unlist)[1])
+ rownames(DataListSPNotIn[[lv]])=rownames(DataListNotIn.unlist)
+ }
+ rownames(DataListSPIn[[lv]])=rownames(DataListIn.unlist)
+ EmpiricalRList.Good.mat.SP[[lv]]=matrix(EmpiricalRList.Good.mat[,Conditions==levels(Conditions)[lv]],nrow=dim(EmpiricalRList.Good.mat)[1])
+}
+
+ NumOfEachGroupIn=sapply(1:NoneZeroLength, function(i)max(0,dim(DataList.In[[i]])[1]))
+ NumOfEachGroupNotIn=sapply(1:NoneZeroLength, function(i)max(0,dim(DataList.NotIn[[i]])[1]))
+
+ #Initialize SigIn & ...
+ AlphaIn=0.5
+ BetaIn=rep(0.5,NoneZeroLength)
+ PIn=0.5
+
+ ####use while to make an infinity round?
+ UpdateAlpha=NULL
+ UpdateBeta=NULL
+ UpdateP=NULL
+ UpdatePFromZ=NULL
+ Timeperround=NULL
+ for (times in 1:maxround){
+ temptime1=proc.time()
+ UpdateOutput=suppressWarnings(LogN(DataListIn.unlist,DataListSPIn, EmpiricalRList.Good.mat ,EmpiricalRList.Good.mat.SP, NumOfEachGroupIn, AlphaIn, BetaIn, PIn, NoneZeroLength))
+ print(paste("iteration", times, "done",sep=" "))
+ AlphaIn=UpdateOutput$AlphaNew
+ BetaIn=UpdateOutput$BetaNew
+ PIn=UpdateOutput$PNew
+ PFromZ=UpdateOutput$PFromZ
+ F0Out=UpdateOutput$F0Out
+ F1Out=UpdateOutput$F1Out
+ UpdateAlpha=rbind(UpdateAlpha,AlphaIn)
+ UpdateBeta=rbind(UpdateBeta,BetaIn)
+ UpdateP=rbind(UpdateP,PIn)
+ UpdatePFromZ=rbind(UpdatePFromZ,PFromZ)
+ temptime2=proc.time()
+ Timeperround=c(Timeperround,temptime2[3]-temptime1[3])
+ print(paste("time" ,Timeperround[times],sep=" "))
+ Z.output=UpdateOutput$ZNew.list[!is.na(UpdateOutput$ZNew.list)]
+ Z.NA.Names=UpdateOutput$zNaNName
+ }
+ #Remove this } after testing!!
+
+# if (times!=1){
+# if((UpdateAlpha[times]-UpdateAlpha[times-1])^2+UpdateBeta[times]-UpdateBeta[times-1])^2+UpdateR[times]-UpdateR[times-1])^2+UpdateP[times]-UpdateP[times-1])^2<=10^(-6)){
+# Result=list(Sig=SigIn, Miu=MiuIn, Tau=TauIn)
+# break
+# }
+# }
+#}
+
+##########Change Names############
+## Only z are for Good Ones
+## Others are for ALL Data
+GoodData=GoodData[!GoodData%in%Z.NA.Names]
+IsoNamesIn.Good=IsoNamesIn[GoodData]
+RealName.Z.output=Z.output
+RealName.F0=F0Out
+RealName.F1=F1Out
+names(RealName.Z.output)=IsoNamesIn.Good
+names(RealName.F0)=IsoNamesIn.Good
+names(RealName.F1)=IsoNamesIn.Good
+
+
+RealName.EmpiricalRList=sapply(1:NoneZeroLength,function(i)EmpiricalRList[names(EmpiricalRList)%in%NameList[[i]]], simplify=F)
+RealName.MeanList=sapply(1:NoneZeroLength,function(i)MeanList[names(MeanList)%in%NameList[[i]]], simplify=F)
+RealName.C1MeanList=sapply(1:NoneZeroLength,function(i)MeanSP[[1]][names(MeanSP[[1]])%in%NameList[[i]]], simplify=F)
+RealName.C2MeanList=sapply(1:NoneZeroLength,function(i)MeanSP[[2]][names(MeanSP[[2]])%in%NameList[[i]]], simplify=F)
+RealName.C1VarList=sapply(1:NoneZeroLength,function(i)VarSP[[1]][names(VarSP[[1]])%in%NameList[[i]]], simplify=F)
+RealName.C2VarList=sapply(1:NoneZeroLength,function(i)VarSP[[2]][names(VarSP[[2]])%in%NameList[[i]]], simplify=F)
+RealName.DataList=sapply(1:NoneZeroLength,function(i)DataList[[i]][rownames(DataList[[i]])%in%NameList[[i]],], simplify=F)
+
+
+
+RealName.VarList=sapply(1:NoneZeroLength,function(i)VarList[names(VarList)%in%NameList[[i]]], simplify=F)
+RealName.PoolVarList=sapply(1:NoneZeroLength,function(i)PoolVar[names(PoolVar)%in%NameList[[i]]], simplify=F)
+
+
+RealName.QList1=sapply(1:NoneZeroLength,function(i)GetPSP[[1]][names(GetPSP[[1]])%in%NameList[[i]]], simplify=F)
+RealName.QList2=sapply(1:NoneZeroLength,function(i)GetPSP[[2]][names(GetPSP[[2]])%in%NameList[[i]]], simplify=F)
+
+
+for (i in 1:NoneZeroLength){
+tmp=NameList[[i]]
+names=IsoNamesIn[tmp]
+
+RealName.MeanList[[i]]=RealName.MeanList[[i]][NameList[[i]]]
+RealName.VarList[[i]]=RealName.VarList[[i]][NameList[[i]]]
+RealName.QList1[[i]]=RealName.QList1[[i]][NameList[[i]]]
+RealName.QList2[[i]]=RealName.QList2[[i]][NameList[[i]]]
+RealName.EmpiricalRList[[i]]=RealName.EmpiricalRList[[i]][NameList[[i]]]
+RealName.C1MeanList[[i]]=RealName.C1MeanList[[i]][NameList[[i]]]
+RealName.C2MeanList[[i]]=RealName.C2MeanList[[i]][NameList[[i]]]
+RealName.PoolVarList[[i]]=RealName.PoolVarList[[i]][NameList[[i]]]
+RealName.C1VarList[[i]]=RealName.C1VarList[[i]][NameList[[i]]]
+RealName.C2VarList[[i]]=RealName.C2VarList[[i]][NameList[[i]]]
+RealName.DataList[[i]]=RealName.DataList[[i]][NameList[[i]],]
+
+names(RealName.MeanList[[i]])=names
+names(RealName.VarList[[i]])=names
+if(ncol(DataListSP[[1]])!=1){
+ names(RealName.QList1[[i]])=names
+ names(RealName.C1VarList[[i]])=names
+}
+if(ncol(DataListSP[[2]])!=1){
+ names(RealName.QList2[[i]])=names
+ names(RealName.C2VarList[[i]])=names
+}
+
+names(RealName.EmpiricalRList[[i]])=names
+names(RealName.C1MeanList[[i]])=names
+names(RealName.C2MeanList[[i]])=names
+names(RealName.PoolVarList[[i]])=names
+rownames(RealName.DataList[[i]])=names
+
+
+}
+
+
+#########posterior part for other data set here later############
+AllNA=unique(c(Z.NA.Names,NotIn))
+z.list.NotIn=NULL
+AllF0=c(RealName.F0)
+AllF1=c(RealName.F1)
+AllZ=RealName.Z.output
+
+if (length(AllNA)>0){
+ Ng.NA=NgVector[AllNA]
+ AllNA.Ngorder=AllNA[order(Ng.NA)]
+ NumOfEachGroupNA=rep(0,NoneZeroLength)
+ NumOfEachGroupNA.tmp=tapply(Ng.NA,Ng.NA,length)
+ names(NumOfEachGroupNA)=c(1:NoneZeroLength)
+ NumOfEachGroupNA[names(NumOfEachGroupNA.tmp)]=NumOfEachGroupNA.tmp
+ PNotIn=rep(1-ApproxVal,length(AllNA.Ngorder))
+ MeanList.NotIn=MeanList[AllNA.Ngorder]
+ R.NotIn.raw=MeanList.NotIn*PNotIn/(1-PNotIn)
+ if(length(sizeFactors)==ncol(Data))
+ R.NotIn=outer(R.NotIn.raw,sizeFactors)
+ if(!length(sizeFactors)==ncol(Data))
+ R.NotIn=R.NotIn.raw*sizeFactors[NotIn,]
+ R.NotIn1=matrix(R.NotIn[,Conditions==levels(Conditions)[1]],nrow=nrow(R.NotIn))
+ R.NotIn2=matrix(R.NotIn[,Conditions==levels(Conditions)[2]],nrow=nrow(R.NotIn))
+
+ DataListNotIn.unlistWithZ=DataList.unlist[AllNA.Ngorder,]
+ DataListSPNotInWithZ=vector("list",nlevels(Conditions))
+ for (lv in 1:nlevels(Conditions))
+ DataListSPNotInWithZ[[lv]] = matrix(DataListSP[[lv]][AllNA.Ngorder,],nrow=length(AllNA.Ngorder))
+ F0=f0(DataListNotIn.unlistWithZ, AlphaIn, BetaIn, R.NotIn, NumOfEachGroupNA, log=F)
+ F1=f1(DataListSPNotInWithZ[[1]], DataListSPNotInWithZ[[2]], AlphaIn, BetaIn, R.NotIn1,R.NotIn2, NumOfEachGroupNA, log=F)
+ z.list.NotIn=PIn*F1/(PIn*F1+(1-PIn)*F0)
+# names(z.list.NotIn)=IsoNamesIn.Good=IsoNamesIn[which(Names%in%NotIn)]
+ names(z.list.NotIn)=IsoNamesIn[AllNA.Ngorder]
+
+ AllZ=c(RealName.Z.output,z.list.NotIn)
+ AllZ=AllZ[IsoNamesIn]
+ AllZ[is.na(AllZ)]=0
+ F0.NotIn=F0
+ F1.NotIn=F1
+ names(F0.NotIn)=IsoNamesIn[names(F0)]
+ names(F1.NotIn)=IsoNamesIn[names(F1)]
+ AllF0=c(RealName.F0,F0.NotIn)
+ AllF1=c(RealName.F1,F1.NotIn)
+ AllF0=AllF0[IsoNamesIn]
+ AllF1=AllF1[IsoNamesIn]
+ AllF0[is.na(AllF0)]=0
+ AllF1[is.na(AllF1)]=0
+}
+#############Result############################
+Result=list(Alpha=UpdateAlpha,Beta=UpdateBeta,P=UpdateP,PFromZ=UpdatePFromZ, Z=RealName.Z.output,PoissonZ=z.list.NotIn, RList=RealName.EmpiricalRList, MeanList=RealName.MeanList, VarList=RealName.VarList, QList1=RealName.QList1, QList2=RealName.QList2, C1Mean=RealName.C1MeanList, C2Mean=RealName.C2MeanList,C1EstVar=RealName.C1VarList, C2EstVar=RealName.C2VarList, PoolVar=RealName.PoolVarList , DataList=RealName.DataList,PPDE=AllZ,f0=AllF0, f1=AllF1,
+ AllZeroIndex=AllZeroNames)
+}
+
projects / rsem.git / blobdiff