R/EBTest_ext.R

Defines functions EBTest_ext

Documented in EBTest_ext

#' @title Extented EBTest function
#' @usage EBTest_ext(Data,NgVector=NULL,Conditions, 
#'	sizeFactors, maxround, Pool=FALSE, NumBin=1000,
#'	ApproxVal=10^-10, Alpha=NULL, Beta=NULL,
#'	PInput=NULL,RInput=NULL,PoolLower=.25, 
#'	PoolUpper=.75,OnlyCalcR=FALSE,Print=TRUE)
#' @param Data Input data, rows are genes/isoforms and columns are samples. Data should come from a two condition experiment
#' @param NgVector Ng vector; NULL for gene level data
#' @param Conditions A factor indicates the condition (time/spatial point) which each sample belongs to. Only two levels are allowed.
#' @param sizeFactors a vector indicates library size factors
#' @param maxround number of iteration
#' @param Pool While working without replicates, user could define the Pool
#'          = TRUE in the EBTest function to enable pooling.
#' @param NumBin By defining NumBin = 1000, EBSeq will group the genes with
#'          similar means together into 1,000 bins.	
#' @param PoolLower,PoolUpper With the assumption that only subset of the genes
#'          are DE in the data set, we take genes whose FC are in the
#'          PoolLower - PoolUpper quantile of the FCs as the candidate
#'					          genes (default is 25%-75%).
#' For each bin, the bin-wise variance estimation is defined as
#'          the median of the cross condition variance estimations of the
#'          candidate genes within that bin.
#'					  We use the cross condition variance estimations for the
#'					          candidate genes and the bin-wise variance estimations of the
#'										          host bin for the non-candidate genes.
#' @param ApproxVal The variances of the transcripts with mean < var will be
#'										          approximated as mean/(1-ApproxVal).
#' @param Alpha,Beta,PInput,RInput If the parameters are known and the user
#'          doesn't want to estimate them from the data, user may
#'					          specify them here.
#' @param Print Whether print the elapsed-time while running the test.					
#' @param OnlyCalcR if OnlyCalcR=TRUE, the function will only return estimation of r's.
#' @author Ning Leng
#' @examples data(GeneExampleData)
#' Data=GeneExampleData[,1:6]
#' CondVector <- rep(paste("t",1:2,sep=""),each=3)
#' Conditions <- factor(CondVector, levels=c("t1","t2"))
#' Sizes <- MedianNorm(Data[1:10,])
#' Out <- EBTest_ext(Data=Data[1:10,], sizeFactors=Sizes, Conditions=Conditions,
#'          maxround=1)
#' @details    
#' EBSeq_ext() function is an extension of EBTest() function, which is used to calculate the conditional probability P(X_g,t | X_g,t-1).
#' In EBSeqHMM, we assume the conditional distribution is Beta-Negative Binomial.
#' @return See \code{\link{EBTest}}

EBTest_ext <-
function(Data,NgVector=NULL,Conditions, sizeFactors, maxround, Pool=FALSE, NumBin=1000,ApproxVal=10^-10, Alpha=NULL, Beta=NULL,PInput=NULL,RInput=NULL,PoolLower=.25, PoolUpper=.75,OnlyCalcR=FALSE,Print=TRUE)
{
	
	
	 if (!is.factor(Conditions)) 
		         Conditions = as.factor(Conditions)
		if (!is.matrix(Data)) stop("The input Data is not a matrix")
    if (length(Conditions) != ncol(Data)) 
		         stop("The number of conditions is not the same as the number of samples! ")
   if (nlevels(Conditions) > 2) 
		         stop("More than 2 conditions! Please use EBMultiTest() function")
   if (nlevels(Conditions) < 2) 
		        stop("Less than 2 conditions - Please check your input")
    if (length(sizeFactors) != length(Data) & length(sizeFactors) != 
						         ncol(Data)) 
			     stop("The number of library size factors is not the same as the number of samples!")

	
	Vect5End <- NULL
	Vect3End <- NULL
	CI <- NULL
	CIthre <- NULL
	tau <-NULL
	Dataraw <- Data
	AllZeroNames <- which(rowMeans(Data)==0)
	NotAllZeroNames <- which(rowMeans(Data)>0)
	if(length(AllZeroNames)>0 & Print==TRUE) cat("Remove transcripts with all zero \n")
	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=FALSE)
	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=FALSE)
	names(DataList) <- names(NameList)
    
	NumEachGroup <- sapply(1:NoneZeroLength , function(i)dim(DataList[[i]])[1])
	# 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
	
	MeanList <- rowMeans(DataList.unlist.dvd)

###############
# Input R
###############
	if (!is.null(RInput)){

	RNoZero <- RInput[NotAllZeroNames]
	names(RNoZero) <- rownames(Data)
	RNoZero.order <- RNoZero[rownames(DataList.unlist)]
	if(length(sizeFactors)==ncol(Data)){
		RMat <-  outer(RNoZero.order, sizeFactors)
	}
	if(!length(sizeFactors)==ncol(Data)){
		RMat <-  RNoZero.order* sizeFactors
	}
		
	DataListSP <- vector("list",nlevels(Conditions))
	RMatSP <- vector("list",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)
		RMatSP[[lv]]= matrix(RMat[,Conditions==levels(Conditions)[lv]],nrow=dim(RMat)[1])
		rownames(RMatSP[[lv]]) <- rownames(RMat)
	
		}

	F0Log <- f0(Input=DataList.unlist, AlphaIn=Alpha, BetaIn=Beta, 
			 EmpiricalR=RMat, NumOfGroups=NumEachGroup, log=TRUE)
	F1Log <- f1(Input1=DataListSP[[1]], Input2=DataListSP[[2]], 
			 AlphaIn=Alpha, BetaIn=Beta, EmpiricalRSP1=RMatSP[[1]], 
			 EmpiricalRSP2=RMatSP[[2]], NumOfGroup=NumEachGroup, log=TRUE)
	F0LogMdf <- F0Log+600
	F1LogMdf <- F1Log+600
	F0Mdf <- exp(F0LogMdf)
	F1Mdf <- exp(F1LogMdf)
	if(!is.null(PInput)){
		z.list <- PInput*F1Mdf/(PInput*F1Mdf+(1-PInput)*F0Mdf)
		PIn <- PInput
	}
	if(is.null(PInput)){
		PIn <- .5
		PInput <- rep(NULL,maxround)
		for(i in 1:maxround){
			z.list <- PIn*F1Mdf/(PIn*F1Mdf+(1-PIn)*F0Mdf)
			zNaNName <- names(z.list)[is.na(z.list)]
			zGood <- which(!is.na(z.list))
			PIn <- sum(z.list[zGood])/length(z.list[zGood])
			PInput[i] <- PIn
		}
	
	zNaNName <- names(z.list)[is.na(z.list)]
	if(length(zNaNName)!=0){
	PNotIn <- rep(1-ApproxVal,length(zNaNName))
	MeanList.NotIn <- MeanList[zNaNName]
	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[zNaNName,]
	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))
	NumOfEachGroupNA <- sapply(1:NoneZeroLength, function(i)length(zNaNName%in%rownames(DataList[[i]])))
	F0LogNA <- f0(matrix(DataList.unlist[zNaNName,],ncol=ncol(DataList.unlist)),  Alpha, Beta, R.NotIn, NumOfEachGroupNA, log=TRUE)
    F1LogNA <- f1(matrix(DataListSP[[1]][zNaNName,],ncol=ncol(DataListSP[[1]])), 
			   matrix(DataListSP[[2]][zNaNName,],ncol=ncol(DataListSP[[2]])),
			   Alpha, Beta, R.NotIn1,R.NotIn2, NumOfEachGroupNA, log=TRUE)
	F0LogMdfNA <- F0LogNA+600
		F1LogMdfNA <- F1LogNA+600
		F0MdfNA <- exp(F0LogMdfNA)
		F1MdfNA <- exp(F1LogMdfNA)
		z.list.NotIn <- PIn*F1MdfNA/(PIn*F1MdfNA+(1-PIn)*F0MdfNA)
	z.list[zNaNName] <- z.list.NotIn
	F0Log[zNaNName] <- F0LogNA
	F1Log[zNaNName] <- F1LogNA
	}
	}
	RealName.Z.output <- z.list
	RealName.F0 <- F0Log
	RealName.F1 <- F1Log
	names(RealName.Z.output) <- IsoNamesIn
	names(RealName.F0) <- IsoNamesIn
	names(RealName.F1) <- IsoNamesIn


	output <- list(Alpha=Alpha,Beta=Beta,P=PInput, Z=RealName.Z.output,
			 PPDE=RealName.Z.output,f0=RealName.F0, f1=RealName.F1)
	return(output)
	
	}


	# 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)],PoolLower) & 
								  FCforPool<=quantile(FCforPool[!is.na(FCforPool)],PoolUpper))
	
	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)
	if(Print==TRUE)cat(paste("No Replicate - estimate phi",round(Phi,5), "\n"))
	names(VarEst) = names(MeanforPoolSP.dvd1)=
	names(MeanforPoolSP.dvd2)=rownames(DataList.unlist.dvd)
	}

	#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]])
	}
}
	
	
	VarList <- apply(DataList.unlist.dvd, 1, var)
	if(ncol(Data)==2){
		PoolVar <- VarEst
		VarSP[[1]]=VarSP[[2]]=VarEst
		GetPSP[[1]] <- MeanSP[[1]]/VarEst
		GetPSP[[2]] <- MeanSP[[2]]/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
		VarSP[[1]]=VarSP[[2]]=VarEst
		names(MeanSP[[1]]) <- names(VarSP[[1]])
		names(MeanSP[[2]]) <- names(VarSP[[2]])
	}
	# 
	# 
	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)	
		EmpiricalRList.mat <-  outer(EmpiricalRList, sizeFactors)
	}
	if(!length(sizeFactors)==ncol(Data)){
	EmpiricalRList.Good.mat <- EmpiricalRList.Good* sizeFactors[GoodData,]
	EmpiricalRList.mat <- EmpiricalRList* sizeFactors
	}

	# Only Use Data has Good q's
	DataList.In <- sapply(1:NoneZeroLength, function(i)DataList[[i]][GoodData[GoodData%in%rownames(DataList[[i]])],],simplify=FALSE)
	DataList.NotIn <- sapply(1:NoneZeroLength, function(i)DataList[[i]][NotIn[NotIn%in%rownames(DataList[[i]])],],simplify=FALSE)
	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 = EmpiricalRList.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])
		EmpiricalRList.mat.SP[[lv]] <- matrix(EmpiricalRList.mat[,Conditions==levels(Conditions)[lv]],nrow=dim(EmpiricalRList.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]))
	

#################
# For output
#################
RealName.EmpiricalRList <- sapply(1:NoneZeroLength,function(i)EmpiricalRList[names(EmpiricalRList)%in%NameList[[i]]], simplify=FALSE)
RealName.MeanList <- sapply(1:NoneZeroLength,function(i)MeanList[names(MeanList)%in%NameList[[i]]], simplify=FALSE)
RealName.C1MeanList <- sapply(1:NoneZeroLength,function(i)MeanSP[[1]][names(MeanSP[[1]])%in%NameList[[i]]], simplify=FALSE)
RealName.C2MeanList <- sapply(1:NoneZeroLength,function(i)MeanSP[[2]][names(MeanSP[[2]])%in%NameList[[i]]], simplify=FALSE)
RealName.C1VarList <- sapply(1:NoneZeroLength,function(i)VarSP[[1]][names(VarSP[[1]])%in%NameList[[i]]], simplify=FALSE)
RealName.C2VarList <- sapply(1:NoneZeroLength,function(i)VarSP[[2]][names(VarSP[[2]])%in%NameList[[i]]], simplify=FALSE)
RealName.DataList <- sapply(1:NoneZeroLength,function(i)DataList[[i]][rownames(DataList[[i]])%in%NameList[[i]],], simplify=FALSE)



RealName.VarList <- sapply(1:NoneZeroLength,function(i)VarList[names(VarList)%in%NameList[[i]]], simplify=FALSE)
RealName.PoolVarList <- sapply(1:NoneZeroLength,function(i)PoolVar[names(PoolVar)%in%NameList[[i]]], simplify=FALSE)


RealName.QList1 <- sapply(1:NoneZeroLength,function(i)GetPSP[[1]][names(GetPSP[[1]])%in%NameList[[i]]], simplify=FALSE)
RealName.QList2 <- sapply(1:NoneZeroLength,function(i)GetPSP[[2]][names(GetPSP[[2]])%in%NameList[[i]]], simplify=FALSE)


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


}
  


#####################
# If Don need EM
#####################	
	if(!is.null(Alpha)&!is.null(Beta)){
	F0Log <- f0(Input=DataList.unlist, AlphaIn=Alpha, BetaIn=Beta, 
			 EmpiricalR=EmpiricalRList.mat, NumOfGroups=NumEachGroup, log=TRUE)
	F1Log <- f1(Input1=DataListSP[[1]], Input2=DataListSP[[2]], 
			 AlphaIn=Alpha, BetaIn=Beta, EmpiricalRSP1=EmpiricalRList.mat.SP[[1]], 
			 EmpiricalRSP2 <- EmpiricalRList.mat.SP[[2]], NumOfGroup=NumEachGroup, log=TRUE)
	F0LogMdf <- F0Log+600
	F1LogMdf <- F1Log+600
	F0Mdf <- exp(F0LogMdf)
	F1Mdf <- exp(F1LogMdf)
	if(!is.null(PInput)){
		z.list <- PInput*F1Mdf/(PInput*F1Mdf+(1-PInput)*F0Mdf)
		PIn <- PInput
	}
	if(is.null(PInput)){
		PIn <- .5
		PInput <- rep(NULL,maxround)
		for(i in 1:maxround){
			z.list <- PIn*F1Mdf/(PIn*F1Mdf+(1-PIn)*F0Mdf)
			zNaNName <- names(z.list)[is.na(z.list)]
			zGood <- which(!is.na(z.list))
			PIn <- sum(z.list[zGood])/length(z.list[zGood])
			PInput[i] <- PIn
		}
	
	zNaNName <- names(z.list)[is.na(z.list)]
	if(length(zNaNName)!=0){
	PNotIn <- rep(1-ApproxVal,length(zNaNName))
	MeanList.NotIn <- MeanList[zNaNName]
	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[zNaNName,]
	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))
	NumOfEachGroupNA <- sapply(1:NoneZeroLength, function(i)length(zNaNName%in%rownames(DataList[[i]])))
	
    
      
    
    F0LogNA <- f0(matrix(DataList.unlist[zNaNName,], ncol = ncol(DataList.unlist)), Alpha, Beta, R.NotIn, NumOfEachGroupNA, log=TRUE)
    F1LogNA <- f1(matrix(DataListSP[[1]][zNaNName,],ncol=ncol(DataListSP[[1]])), 
               matrix(DataListSP[[2]][zNaNName,],ncol=ncol(DataListSP[[2]])),
			   Alpha, Beta, R.NotIn1,R.NotIn2, NumOfEachGroupNA, log=TRUE)
	F0LogMdfNA <- F0LogNA+600
		F1LogMdfNA <- F1LogNA+600
		F0MdfNA <- exp(F0LogMdfNA)
		F1MdfNA <- exp(F1LogMdfNA)
		z.list.NotIn <- PIn*F1MdfNA/(PIn*F1MdfNA+(1-PIn)*F0MdfNA)
	z.list[zNaNName] <- z.list.NotIn
	F0Log[zNaNName] <- F0LogNA
	F1Log[zNaNName] <- F1LogNA
	}
	}
	RealName.Z.output <- z.list
	RealName.F0 <- F0Log
	RealName.F1 <- F1Log
	names(RealName.Z.output) <- IsoNamesIn
	names(RealName.F0) <- IsoNamesIn
	names(RealName.F1) <- IsoNamesIn


	output <- list(Alpha=Alpha,Beta=Beta,P=PInput, Z=RealName.Z.output,
			 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=RealName.Z.output,f0=RealName.F0, f1=RealName.F1)
	return(output)
	}
   
###################
# If only need R
###################
	if(OnlyCalcR==TRUE){
	output <- list(
			 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)
	return(output)
	}


#####################	
#Initialize SigIn & ...
#####################	
	AlphaIn <- 0.5
	BetaIn <- rep(0.5,NoneZeroLength)
	PIn <- 0.5


#####################	
# EM
#####################	
	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))
    	#cat(paste("iteration", times, "done \n",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])
		cat(paste("time" ,round(Timeperround[times],2),"\n",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
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



#########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 <- matrix(DataList.unlist[AllNA.Ngorder,],nrow=length(AllNA.Ngorder))
	DataListSPNotInWithZ <- vector("list",nlevels(Conditions))
	for (lv in 1:nlevels(Conditions)) 
		DataListSPNotInWithZ[[lv]]  <-  matrix(DataListSP[[lv]][AllNA.Ngorder,],nrow=length(AllNA.Ngorder))
		F0Log <- f0(DataListNotIn.unlistWithZ,  AlphaIn, BetaIn, R.NotIn, NumOfEachGroupNA, log=TRUE)
    	F1Log <- f1(DataListSPNotInWithZ[[1]], DataListSPNotInWithZ[[2]], AlphaIn, BetaIn, R.NotIn1,R.NotIn2, NumOfEachGroupNA, log=TRUE)
		F0LogMdf <- F0Log+600
		F1LogMdf <- F1Log+600
		F0Mdf <- exp(F0LogMdf)
		F1Mdf <- exp(F1LogMdf)
		z.list.NotIn <- PIn*F1Mdf/(PIn*F1Mdf+(1-PIn)*F0Mdf)
#	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 <- F0Log
	F1.NotIn <- F1Log
	names(F0.NotIn) <- IsoNamesIn[names(F0Log)]
    names(F1.NotIn) <- IsoNamesIn[names(F1Log)]
	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
}
PPMatNZ <- cbind(1-AllZ,AllZ)
colnames(PPMatNZ) <- c("PPEE","PPDE")
rownames(UpdateAlpha) <- paste("iter",1:nrow(UpdateAlpha),sep="")
rownames(UpdateBeta) <- paste("iter",1:nrow(UpdateBeta),sep="")
rownames(UpdateP) <- paste("iter",1:nrow(UpdateP),sep="")
rownames(UpdatePFromZ) <- paste("iter",1:nrow(UpdatePFromZ),sep="")
colnames(UpdateBeta) <- paste("Ng",1:ncol(UpdateBeta),sep="")

CondOut <- levels(Conditions)
names(CondOut) <- paste("Condition",c(1:length(CondOut)),sep="")

PPMat <- matrix(NA,ncol=2,nrow=nrow(Dataraw))
rownames(PPMat) <- rownames(Dataraw)
colnames(PPMat) <- c("PPEE","PPDE")
if(is.null(AllZeroNames))PPMat <- PPMatNZ
if(!is.null(AllZeroNames))PPMat[names(NotAllZeroNames),] <- PPMatNZ[names(NotAllZeroNames),]


#############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,PPMat=PPMatNZ, PPMatWith0=PPMat,
			ConditionOrder=CondOut, Conditions=Conditions)
}

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EBSeqHMM documentation built on Nov. 1, 2018, 2:35 a.m.