EBSeq/DESCRIPTION0000644000175100017510000000203012607342353014237 0ustar00biocbuildbiocbuildPackage: EBSeq Type: Package Title: An R package for gene and isoform differential expression analysis of RNA-seq data Version: 1.10.0 Date: 2015-7-28 Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng Depends: blockmodeling, gplots, testthat, R (>= 3.0.0) Description: Differential Expression analysis at both gene and isoform level using RNA-seq data License: Artistic-2.0 LazyLoad: yes Collate: 'MedianNorm.R' 'GetNg.R' 'beta.mom.R' 'f0.R' 'f1.R' 'Likefun.R' 'LogN.R' 'LogNMulti.R' 'LikefunMulti.R' 'EBTest.R' 'GetPatterns.R' 'EBMultiTest.R' 'GetPP.R' 'PostFC.R' 'GetPPMat.R' 'GetMultiPP.R' 'GetMultiFC.R' 'PlotPostVsRawFC.R' 'crit_fun.R' 'DenNHist.R' 'GetNormalizedMat.R' 'PlotPattern.R' 'PolyFitPlot.R' 'QQP.R' 'QuantileNorm.R' 'RankNorm.R' 'GetDEResults.R' BuildVignettes: yes biocViews: StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing NeedsCompilation: no Packaged: 2015-10-14 02:58:51 UTC; biocbuild EBSeq/NAMESPACE0000644000175100017510000000045012607264551013757 0ustar00biocbuildbiocbuildexport(crit_fun, DenNHist, EBTest, GetNg, GetPP, MedianNorm, PolyFitPlot, PostFC, QQP, QuantileNorm, RankNorm, EBMultiTest, GetMultiPP, GetPatterns, PlotPattern, GetPPMat, GetMultiFC, PlotPostVsRawFC, GetNormalizedMat,f0,f1,LogN,LogNMulti, GetDEResults) import(blockmodeling, gplots, testthat) EBSeq/NEWS0000644000175100017510000000570012607264551013242 0ustar00biocbuildbiocbuild CHANGES IN VERSION 1.9.3 ------------------------ o Correct typos in GetDEResults help file. o Include an additional method for normalization. CHANGES IN VERSION 1.9.2 ------------------------ o Fixed a bug which may cause error when input a matrix to the sizeFactors parameter CHANGES IN VERSION 1.9.1 ------------------------ o Added Q&A seqction in vignette to address common questions CHANGES IN VERSION 1.7.1 ------------------------ o In EBSeq 1.7.1, EBSeq incorporates a new function GetDEResults() which may be used to obtain a list of transcripts under a target FDR in a two-condition experiment. The results obtained by applying this function with its default setting will be more robust to transcripts with low variance and potential outliers. By using the default settings in this function, the number of genes identified in any given analysis may differ slightly from the previous version (1.7.0 or order). To obtain results that are comparable to results from earlier versions of EBSeq (1.7.0 or older), a user may set Method="classic" in GetDEResults() function, or use the original GetPPMat() function. The GeneDEResults() function also allows a user to modify thresholds to target genes/isoforms with a pre-specified posterior fold change. o Also, in EBSeq 1.7.1, the default settings in EBTest() and EBMultiTest() function will only remove transcripts with all 0's (instead of removing transcripts with 75th quantile less than 10 in version 1.3.3-1.7.0). To obtain a list of transcripts comparable to the results generated by EBSeq version 1.3.3-1.7.0, a user may change Qtrm = 0.75 and QtrmCut = 10 when applying EBTest() or EBMultiTest() function. CHANGES IN VERSION 1.5.4 ------------------------ o An extra numerical approximation step is implemented in EBMultiTest() function to avoid underflow. The underflow is likely due to large number of samples. A bug in EBMultiTest() is fixed. The bug will cause error when there is exactly 1 gene/isoform that needs numerical approximation. CHANGES IN VERSION 1.5.3 ------------------------- BUG FIXES o Fixed a bug that may generate NA FC estimates when there are no replicates. CHANGES IN VERSION 1.5.2 ------------------------ NEW FEATURES o An extra numerical approximation step is implemented in EBTest() function to avoid underflow. The underflow is likely due to large number of samples. CHANGES IN VERSION 1.3.3 ------------------------ NEW FEATURES o In EBSeq 1.3.3, the default setting of EBTest function will remove low expressed genes (genes whose 75th quantile of normalized counts is less than 10) before identifying DE genes. These two thresholds can be changed in EBTest function. Because low expressed genes are disproportionately noisy, removing these genes prior to downstream analyses can improve model fitting and increase robustness (e.g. by removing outliers). EBSeq/R/0000755000175100017510000000000012607264551012742 5ustar00biocbuildbiocbuildEBSeq/R/DenNHist.R0000644000175100017510000000307212607264551014543 0ustar00biocbuildbiocbuildDenNHist <- function(EBOut, GeneLevel=F) { if(!"Alpha"%in%names(EBOut))stop("The input doesn't seem like an output from EBTest/EBMultiTest") maxround=nrow(EBOut$Alpha) Alpha=EBOut$Alpha[maxround,] Beta=EBOut$Beta[maxround,] # Multi if(!is.null(EBOut$PPpattern)){ QList=EBOut$QList for(i in 1:length(EBOut$QList)){ for(j in 1:length(EBOut$QList[[i]])){ if(GeneLevel==F)Main=paste("Ig",i,"C",j) if(GeneLevel==T)Main=paste("Gene","C",j) hist(QList[[i]][[j]][QList[[i]][[j]]<.98&QList[[i]][[j]]>0], prob=T,col="blue",breaks=100, main=Main, xlim=c(0,1),xlab=paste("Q alpha=",round(Alpha,2), " beta=",round(Beta[i],2),sep="")) tmpSize=length(QList[[i]][[j]][QList[[i]][[j]]<.98]) tmpseq=seq(0.001,1,length=1000) ll=tmpseq lines(ll,dbeta(ll,Alpha,Beta[i]),col="green",lwd=2) legend("topright",c("Data","Fitted density"),col=c("blue","green"),lwd=2) } } } if(is.null(EBOut$PPpattern)){ for(con in 1:2){ if(con==1)QList=EBOut$QList1 if(con==2)QList=EBOut$QList2 if(!is.list(QList)) QList=list(QList) for (i in 1:length(QList)){ if(GeneLevel==F)Main=paste("Ig",i,"C",con) if(GeneLevel==T)Main=paste("Gene","C",con) hist(QList[[i]][QList[[i]]<.98&QList[[i]]>0], prob=T,col="blue",breaks=100, main=Main, xlim=c(0,1),xlab=paste("Q alpha=",round(Alpha,2), " beta=",round(Beta[i],2),sep="")) tmpSize=length(QList[[i]][QList[[i]]<.98]) tmpseq=seq(0.001,1,length=1000) ll=tmpseq lines(ll,dbeta(ll,Alpha,Beta[i]),col="green",lwd=2) legend("topright",c("Data","Fitted density"),col=c("blue","green"),lwd=2) }} } } EBSeq/R/EBMultiTest.R0000644000175100017510000004405212607264551015233 0ustar00biocbuildbiocbuildEBMultiTest <- function(Data,NgVector=NULL,Conditions,AllParti=NULL, sizeFactors, maxround, Pool=F, NumBin=1000, ApproxVal=10^-10,PoolLower=.25, PoolUpper=.75,Print=T,Qtrm=1,QtrmCut=0) { expect_is(sizeFactors, c("numeric","integer")) expect_is(maxround, c("numeric","integer")) if(!is.factor(Conditions))Conditions=as.factor(Conditions) if(is.null(rownames(Data)))stop("Please add gene/isoform names to the data matrix") 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("Only 2 conditions - Please use EBTest() 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!") tau=CI=CIthre=NULL Dataraw=Data #Normalized DataNorm=GetNormalizedMat(Data, sizeFactors) QuantileFor0=apply(DataNorm,1,function(i)quantile(i,Qtrm)) AllZeroNames=which(QuantileFor0<=QtrmCut) NotAllZeroNames=which(QuantileFor0>QtrmCut) if(length(AllZeroNames)>0 & Print==T) cat(paste0("Removing transcripts with ",Qtrm*100, " th quantile < = ",QtrmCut," \n", length(NotAllZeroNames)," transcripts will be tested \n")) if(length(NotAllZeroNames)==0)stop("0 transcript passed") Data=Data[NotAllZeroNames,] if(!is.null(NgVector))NgVector=NgVector[NotAllZeroNames] if(is.null(NgVector))NgVector=rep(1,nrow(Data)) if(length(sizeFactors)!=ncol(Data))sizeFactors=sizeFactors[NotAllZeroNames,] #ReNameThem 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 PossibleCond==NULL, use all combinations NumCond=nlevels(Conditions) CondLevels=levels(Conditions) #library(blockmodeling) if(is.null(AllParti)){ AllPartiList=sapply(1:NumCond,function(i)nkpartitions(NumCond,i)) AllParti=do.call(rbind,AllPartiList) colnames(AllParti)=CondLevels rownames(AllParti)=paste("Pattern",1:nrow(AllParti),sep="") } if(length(sizeFactors)==length(Data)){ rownames(sizeFactors)=rownames(Data) colnames(sizeFactors)=Conditions } NoneZeroLength=nlevels(as.factor(NgVector)) NameList=sapply(1:NoneZeroLength,function(i)names(NgVector)[NgVector==i],simplify=F) 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)==length(Data)) DataList.unlist.dvd=DataList.unlist/sizeFactors # Pool or Not if(Pool==T){ DataforPoolSP.dvd=MeanforPoolSP.dvd=vector("list",NumCond) for(lv in 1:NumCond){ DataforPoolSP.dvd[[lv]]=matrix(DataList.unlist.dvd[,Conditions==levels(Conditions)[lv]],nrow=dim(DataList.unlist)[1]) MeanforPoolSP.dvd[[lv]]=rowMeans(DataforPoolSP.dvd[[lv]]) } MeanforPool.dvd=rowMeans(DataList.unlist.dvd) NumInBin=floor(dim(DataList.unlist)[1]/NumBin) StartSeq=c(0:(NumBin-1))*NumInBin+1 EndSeq=c(StartSeq[-1]-1,dim(DataList.unlist)[1]) MeanforPool.dvd.Sort=sort(MeanforPool.dvd,decreasing=T) MeanforPool.dvd.Order=order(MeanforPool.dvd,decreasing=T) PoolGroups=sapply(1:NumBin,function(i)(names(MeanforPool.dvd.Sort)[StartSeq[i]:EndSeq[i]]),simplify=F) #FCforPool=MeanforPoolSP.dvd1/MeanforPoolSP.dvd2 # Use GeoMean of every two-group partition Parti2=nkpartitions(NumCond,2) FCForPoolList=sapply(1:nrow(Parti2),function(i)rowMeans(do.call(cbind, MeanforPoolSP.dvd[Parti2[i,]==1]))/ rowMeans(do.call(cbind,MeanforPoolSP.dvd[Parti2[i,]==2])), simplify=F) FCForPoolMat=do.call(cbind,FCForPoolList) FCforPool=apply(FCForPoolMat,1,function(i)exp(mean(log(i)))) names(FCforPool)=names(MeanforPool.dvd) FC_Use=names(FCforPool)[which(FCforPool>=quantile(FCforPool[!is.na(FCforPool)],PoolLower) & FCforPool<=quantile(FCforPool[!is.na(FCforPool)],PoolUpper))] PoolGroupVar=sapply(1:NumBin,function(i)(mean(apply(matrix(DataList.unlist[PoolGroups[[i]][PoolGroups[[i]]%in%FC_Use],],ncol=ncol(DataList.unlist)),1,var)))) PoolGroupVarInList=sapply(1:NumBin,function(i)(rep(PoolGroupVar[i],length(PoolGroups[[i]]))),simplify=F) PoolGroupVarVector=unlist(PoolGroupVarInList) VarPool=PoolGroupVarVector[MeanforPool.dvd.Order] names(VarPool)=names(MeanforPool.dvd) } 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 NumEachCondLevel=summary(Conditions) if(Pool==F & is.null(CI)) CondLevelsUse=CondLevels[NumEachCondLevel>1] if(Pool==T | !is.null(CI)) CondLevelsUse=CondLevels NumCondUse=length(CondLevelsUse) 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]) if(ncol(DataListSP[[lv]])==1 & Pool==F & !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)==length(Data))SizeFSP[[lv]]=sizeFactors[,Conditions==levels(Conditions)[lv]] MeanSP[[lv]]=rowMeans(DataListSP.dvd[[lv]]) names(MeanSP[[lv]])=rownames(DataListSP[[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)==length(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 & Pool==F & !is.null(CI)) VarSP[[lv]]=as.vector(((DataListSP[[lv]]/tauSP[[lv]]) * CISP[[lv]]/(CIthre*2))^2) if( Pool==T){ VarSP[[lv]]=VarPool } if(ncol(DataListSP[[lv]])!=1){ VarSP[[lv]]=rowSums(PrePareVar)/ncol( DataListSP[[lv]]) names(VarSP[[lv]])=rownames(DataList.unlist) GetPSP[[lv]]=MeanSP[[lv]]/VarSP[[lv]] RSP[[lv]]=MeanSP[[lv]]*GetPSP[[lv]]/(1-GetPSP[[lv]]) } names(MeanSP[[lv]])=rownames(DataList.unlist) } # Get Empirical R # POOL R??? MeanList=rowMeans(DataList.unlist.dvd) VarList=apply(DataList.unlist.dvd, 1, var) if(NumCondUse!=0){ Varcbind=do.call(cbind,VarSP[CondLevels%in%CondLevelsUse]) PoolVarSpeedUp_MDFPoi_NoNormVarList=rowMeans(Varcbind) VarrowMin=apply(Varcbind,1,min) } if(NumCondUse==0) { NumFCgp=choose(NumCond,2) FC_Use_tmp=vector("list",NumFCgp) aa=1 for(k1 in 1:(NumCond-1)){ for(k2 in (k1+1):NumCond){ FCforPool=DataList.unlist.dvd[,k1]/DataList.unlist.dvd[,k2] names(FCforPool)=rownames(DataList.unlist.dvd) FC_Use_tmp[[aa]]=names(FCforPool)[which(FCforPool>=quantile(FCforPool[!is.na(FCforPool)],.25) & FCforPool<=quantile(FCforPool[!is.na(FCforPool)],.75))] aa=aa+1 }} FC_Use=Reduce(intersect,FC_Use_tmp) if(length(FC_Use)==0){ All_candi=unlist(FC_Use_tmp) FC_Use=names(table(All_candi))[1:3] } Var_FC_Use=apply( DataList.unlist.dvd[FC_Use,],1,var ) MeanforPool=apply( DataList.unlist.dvd,1,mean ) Mean_FC_Use=apply( DataList.unlist.dvd[FC_Use,],1,mean ) 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==T)message(paste("No Replicate - estimate phi",round(Phi,5), "\n")) Varcbind=VarEst PoolVarSpeedUp_MDFPoi_NoNormVarList=VarEst VarrowMin=VarEst } GetP=MeanList/PoolVarSpeedUp_MDFPoi_NoNormVarList EmpiricalRList=MeanList*GetP/(1-GetP) # sep #Rcb=cbind(RSP[[1]],RSP[[2]]) #Rbest=apply(Rcb,1,function(i)max(i[!is.na(i) & i!=Inf])) EmpiricalRList[EmpiricalRList==Inf] =max(EmpiricalRList[EmpiricalRList!=Inf]) # fine # 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)] #NotIn.BestR=Rbest[NotIn.raw] #NotIn.fix=NotIn.BestR[which(NotIn.BestR>0)] #EmpiricalRList[names(NotIn.fix)]=NotIn.fix #print(paste("ZeroVar",sum(VarrowMin==0), "InfR", length(which(EmpiricalRList==Inf)), "Poi", length(which(EmpiricalRList<0)), "")) #GoodData=c(GoodData.raw,names(NotIn.fix)) #NotIn=NotIn.raw[!NotIn.raw%in%names(NotIn.fix)] 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)==length(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(DataListSPIn[[lv]])=rownames(DataListIn.unlist) if(length(NotIn)>0)rownames(DataListSPNotIn[[lv]])=rownames(DataListNotIn.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=rep(1/nrow(AllParti),nrow(AllParti)) ####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(LogNMulti(DataListIn.unlist,DataListSPIn, EmpiricalRList.Good.mat ,EmpiricalRList.Good.mat.SP, NumOfEachGroupIn, AlphaIn, BetaIn, PIn, NoneZeroLength, AllParti,Conditions)) message(paste("iteration", times, "done \n",sep=" ")) AlphaIn=UpdateOutput$AlphaNew BetaIn=UpdateOutput$BetaNew PIn=UpdateOutput$PNew PFromZ=UpdateOutput$PFromZ FOut=UpdateOutput$FGood UpdateAlpha=rbind(UpdateAlpha,AlphaIn) UpdateBeta=rbind(UpdateBeta,BetaIn) UpdateP=rbind(UpdateP,PIn) UpdatePFromZ=rbind(UpdatePFromZ,PFromZ) temptime2=proc.time() Timeperround=c(Timeperround,temptime2[3]-temptime1[3]) message(paste("time" ,round(Timeperround[times],2),"\n",sep=" ")) Z.output=UpdateOutput$ZEachGood 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=as.vector(IsoNamesIn[GoodData]) RealName.Z.output=Z.output RealName.F=FOut rownames(RealName.Z.output)=IsoNamesIn.Good rownames(RealName.F)=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.SPMeanList=sapply(1:NoneZeroLength,function(i)sapply(1:length(MeanSP), function(j)MeanSP[[j]][names(MeanSP[[j]])%in%NameList[[i]]],simplify=F), simplify=F) RealName.SPVarList=sapply(1:NoneZeroLength,function(i)sapply(1:length(VarSP), function(j)VarSP[[j]][names(VarSP[[j]])%in%NameList[[i]]],simplify=F), 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)PoolVarSpeedUp_MDFPoi_NoNormVarList[names(PoolVarSpeedUp_MDFPoi_NoNormVarList)%in%NameList[[i]]], simplify=F) RealName.QList=sapply(1:NoneZeroLength,function(i)sapply(1:length(GetPSP), function(j)GetPSP[[j]][names(GetPSP[[j]])%in%NameList[[i]]],simplify=F), 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]]] for(j in 1:NumCond){ RealName.SPMeanList[[i]][[j]]=RealName.SPMeanList[[i]][[j]][NameList[[i]]] if(!is.null(RealName.QList[[i]][[j]])){ RealName.QList[[i]][[j]]=RealName.QList[[i]][[j]][NameList[[i]]] RealName.SPVarList[[i]][[j]]=RealName.SPVarList[[i]][[j]][NameList[[i]]] names(RealName.QList[[i]][[j]])=Names names(RealName.SPVarList[[i]][[j]])=Names } names(RealName.SPMeanList[[i]][[j]])=Names } RealName.EmpiricalRList[[i]]=RealName.EmpiricalRList[[i]][NameList[[i]]] RealName.PoolVarList[[i]]=RealName.PoolVarList[[i]][NameList[[i]]] RealName.DataList[[i]]=RealName.DataList[[i]][NameList[[i]],] names(RealName.MeanList[[i]])=Names names(RealName.VarList[[i]])=Names names(RealName.EmpiricalRList[[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)) AllZ=NULL AllF=NULL if(length(AllNA)==0){ AllZ=RealName.Z.output[IsoNamesIn,] AllF=RealName.F[IsoNamesIn,] } ZEachNA=NULL 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=matrix(outer(R.NotIn.raw,sizeFactors),nrow=length(AllNA.Ngorder)) if(length(sizeFactors)==length(Data)) R.NotIn=matrix(R.NotIn.raw*sizeFactors[names(R.NotIn.raw),],nrow=length(AllNA.Ngorder)) DataListNotIn.unlistWithZ=matrix(DataList.unlist[AllNA.Ngorder,], nrow=length(AllNA.Ngorder)) rownames(DataListNotIn.unlistWithZ)=AllNA.Ngorder DataListSPNotInWithZ=vector("list",nlevels(Conditions)) RListSPNotInWithZ=vector("list",nlevels(Conditions)) for (lv in 1:nlevels(Conditions)) { DataListSPNotInWithZ[[lv]] = matrix(DataListSP[[lv]][AllNA.Ngorder,],nrow=length(AllNA.Ngorder)) RListSPNotInWithZ[[lv]]=matrix(R.NotIn[,Conditions==levels(Conditions)[lv]],nrow=length(AllNA.Ngorder)) } FListNA=sapply(1:nrow(AllParti),function(i)sapply(1:nlevels(as.factor(AllParti[i,])), function(j)f0(do.call(cbind, DataListSPNotInWithZ[AllParti[i,]==j]),AlphaIn, BetaIn, do.call(cbind,RListSPNotInWithZ[AllParti[i,]==j]), NumOfEachGroupNA, log=T)), simplify=F) for(ii in 1:length(FListNA)) FListNA[[ii]]=matrix(FListNA[[ii]],nrow=length(AllNA.Ngorder)) FPartiLogNA=matrix(sapply(FListNA,rowSums),nrow=length(AllNA.Ngorder)) FMatNA=exp(FPartiLogNA+600) rownames(FMatNA)=rownames(DataListNotIn.unlistWithZ) PMatNA=matrix(rep(1,nrow(DataListNotIn.unlistWithZ)),ncol=1)%*%matrix(PIn,nrow=1) FmultiPNA=matrix(FMatNA*PMatNA,nrow=length(AllNA.Ngorder)) DenomNA=rowSums(FmultiPNA) ZEachNA=matrix(apply(FmultiPNA,2,function(i)i/DenomNA),nrow=length(AllNA.Ngorder)) rownames(ZEachNA)=IsoNamesIn[AllNA.Ngorder] AllZ=rbind(RealName.Z.output,ZEachNA) AllZ=AllZ[IsoNamesIn,] F.NotIn=FPartiLogNA rownames(F.NotIn)=IsoNamesIn[rownames(FMatNA)] AllF=rbind(RealName.F,F.NotIn) AllF=AllF[IsoNamesIn,] } colnames(AllZ)=rownames(AllParti) colnames(AllF)=rownames(AllParti) 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="") AllZWith0=matrix(NA,ncol=ncol(AllZ),nrow=nrow(Dataraw)) rownames(AllZWith0)=rownames(Dataraw) colnames(AllZWith0)=colnames(AllZ) if(is.null(AllZeroNames))AllZWith0=AllZ if(!is.null(AllZeroNames))AllZWith0[names(NotAllZeroNames),]=AllZ[names(NotAllZeroNames),] #############Result############################ Result=list(Alpha=UpdateAlpha,Beta=UpdateBeta,P=UpdateP,PFromZ=UpdatePFromZ, Z=RealName.Z.output,PoissonZ=ZEachNA, RList=RealName.EmpiricalRList, MeanList=RealName.MeanList, VarList=RealName.VarList, QList=RealName.QList, SPMean=RealName.SPMeanList, SPEstVar=RealName.SPVarList, PoolVar=RealName.PoolVarList , DataList=RealName.DataList,PPpattern=AllZ,f=AllF, AllParti=AllParti, PPMat=AllZ,PPMatWith0=AllZWith0, ConditionOrder=CondOut) } EBSeq/R/EBTest.R0000644000175100017510000005552012607264551014222 0ustar00biocbuildbiocbuildEBTest <- function(Data,NgVector=NULL,Conditions, sizeFactors, maxround, Pool=F, NumBin=1000,ApproxVal=10^-10, Alpha=NULL, Beta=NULL,PInput=NULL,RInput=NULL,PoolLower=.25, PoolUpper=.75,Print=T, Qtrm=1,QtrmCut=0) { expect_is(sizeFactors, c("numeric","integer")) expect_is(maxround, c("numeric","integer")) if(!is.factor(Conditions))Conditions=as.factor(Conditions) if(is.null(rownames(Data)))stop("Please add gene/isoform names to the data matrix") 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!") Conditions=as.factor(Conditions) Vect5End=Vect3End=CI=CIthre=tau=NULL Dataraw=Data #Normalized DataNorm=GetNormalizedMat(Data, sizeFactors) expect_is(DataNorm, "matrix") Levels=levels(as.factor(Conditions)) # Dixon Statistics # library(outliers) # normalized matrix for each condition # matC=sapply(1:length(Levels),function(i)DataNorm[,which(Conditions==Levels[i])]) # run dixon test for each isoform within condition # DixonP=sapply(1:length(matC),function(j) # apply(DataNorm,1,function(i){ # if(mean(i)==0)out=NA # else out=dixon.test(i)$p.value # out})) QuantileFor0=apply(DataNorm,1,function(i)quantile(i,Qtrm)) AllZeroNames=which(QuantileFor0<=QtrmCut) NotAllZeroNames=which(QuantileFor0>QtrmCut) if(length(AllZeroNames)>0 & Print==T) cat(paste0("Removing transcripts with ",Qtrm*100, " th quantile < = ",QtrmCut," \n", length(NotAllZeroNames)," transcripts will be tested\n")) if(length(NotAllZeroNames)==0)stop("0 transcript passed") 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)==length(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]])[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)==length(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)==length(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=T) F1Log=f1(Input1=DataListSP[[1]], Input2=DataListSP[[2]], AlphaIn=Alpha, BetaIn=Beta, EmpiricalRSP1=RMatSP[[1]], EmpiricalRSP2=RMatSP[[2]], NumOfGroup=NumEachGroup, log=T) 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)==length(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)sum(zNaNName%in%rownames(DataList[[i]]))) F0LogNA=f0(matrix(DataList.unlist[zNaNName,],ncol=ncol(DataList.unlist)), Alpha, Beta, R.NotIn, NumOfEachGroupNA, log=T) 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=T) 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==T)message(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)==length(Data))SizeFSP[[lv]]=sizeFactors[,Conditions==levels(Conditions)[lv]] MeanSP[[lv]]=rowMeans(DataListSP.dvd[[lv]]) names(MeanSP[[lv]])=rownames(DataListSP[[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)==length(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)==length(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=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=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=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 } ##################### # 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=T) 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=T) 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)==length(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)sum(zNaNName%in%rownames(DataList[[i]]))) F0LogNA=f0(matrix(DataList.unlist[zNaNName,], ncol=ncol(DataList.unlist)), Alpha, Beta, R.NotIn, NumOfEachGroupNA, log=T) 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=T) 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) } ##################### #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)) message(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]) message(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)==length(Data)) R.NotIn=R.NotIn.raw*sizeFactors[names(R.NotIn.raw),] 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=T) F1Log=f1(DataListSPNotInWithZ[[1]], DataListSPNotInWithZ[[2]], AlphaIn, BetaIn, R.NotIn1,R.NotIn2, NumOfEachGroupNA, log=T) 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, DataNorm=DataNorm) } EBSeq/R/GetDEResults.R0000644000175100017510000000457612607264551015413 0ustar00biocbuildbiocbuildGetDEResults<-function(EBPrelim, FDR=0.05, Method="robust", FDRMethod="hard", Threshold_FC=0.7, Threshold_FCRatio=0.3, SmallNum=0.01) { if(!"PPDE"%in%names(EBPrelim))stop("The input doesn't seem like an output from EBTest") ################# Conditions = EBPrelim$Conditions Levels = levels(as.factor(Conditions)) PPcut=FDR # normalized data GeneMat=EBPrelim$DataNorm ###Get DEfound by FDRMethod type PP=GetPPMat(EBPrelim) if(FDRMethod=="hard") {DEfound=rownames(PP)[which(PP[,"PPDE"]>=(1-PPcut))]} else{SoftThre=crit_fun(PP[,"PPEE"],PPcut) DEfound=rownames(PP)[which(PP[,"PPDE"]>=SoftThre)]} # classic if(Method=="classic"){ Gene_status=rep("EE",dim(GeneMat)[1]) names(Gene_status)=rownames(GeneMat) Gene_status[DEfound]="DE" NoTest_genes=rownames(GeneMat)[!(rownames(GeneMat)%in%rownames(PP))] Gene_status[NoTest_genes]="Filtered: Low Expression" PPMatWith0=EBPrelim$PPMatWith0 PPMatWith0[NoTest_genes,]=c(NA,NA) return(list(DEfound=DEfound,PPMat=PPMatWith0,Status=Gene_status)) } else{ ###Post_Foldchange PostFoldChange=PostFC(EBPrelim) PPFC=PostFoldChange$PostFC OldPPFC=PPFC[DEfound] OldPPFC[which(OldPPFC>1)]=1/OldPPFC[which(OldPPFC>1)] FilterFC=names(OldPPFC)[which(OldPPFC>Threshold_FC)] ###New Fold Change NewFC1=apply(matrix(GeneMat[DEfound,which(Conditions==Levels[[1]])]+SmallNum, nrow=length(DEfound)),1,median) NewFC2=apply(matrix(GeneMat[DEfound,which(Conditions==Levels[[2]])]+SmallNum, nrow=length(DEfound)),1,median) NewFC=NewFC1/NewFC2 NewFC[which(NewFC>1)]=1/NewFC[which(NewFC>1)] ###FC Ratio FCRatio=NewFC/OldPPFC FCRatio[which(OldPPFC1) out=unlist(sapply(1:NumNgGroup, function(j)EBMultiOut$SPMean[[j]][[i]]))[OutNames] out} ) colnames(CondMeans)=ConditionNames CondMeansPlus=CondMeans+SmallNum GeneRealMean=rowMeans(CondMeans) GeneR=unlist(EBMultiOut$RList) GeneR[GeneR<=0 | is.na(GeneR)]=GeneRealMean[GeneR<=0 | is.na(GeneR)]*.99/.01 GeneAlpha=EBMultiOut[[1]][nrow(EBMultiOut[[1]]),] GeneBeta=unlist(sapply(1:length(EBMultiOut$DataList), function(i)rep(EBMultiOut[[2]][nrow(EBMultiOut[[1]]),i], nrow(EBMultiOut$DataList[[i]])))) GeneBeta=as.vector(GeneBeta) FCMat=PostFCMat=matrix(0,ncol=choose(NumCondition,2),nrow=length(OutNames)) rownames(FCMat)=rownames(PostFCMat)=OutNames k=1 ColNames=rep(NA,choose(NumCondition,2)) for(i in 1:(NumCondition-1)){ for(j in (i+1):NumCondition) { ColNames[k]=paste(ConditionNames[i],"Over",ConditionNames[j],sep="") FCMat[,k]=CondMeansPlus[,i]/CondMeansPlus[,j] nC1=sum(EBMultiOut$ConditionOrder==ConditionNames[i]) nC2=sum(EBMultiOut$ConditionOrder==ConditionNames[j]) GenePostAlphaC1=GeneAlpha+nC1*GeneR GenePostAlphaC2=GeneAlpha+nC2*GeneR GenePostBetaC1=GeneBeta+nC1*CondMeans[,i] GenePostBetaC2=GeneBeta+nC2*CondMeans[,j] GenePostQC1=GenePostAlphaC1/(GenePostAlphaC1+GenePostBetaC1) GenePostQC2=GenePostAlphaC2/(GenePostAlphaC2+GenePostBetaC2) GenePostFC=((1-GenePostQC1)/(1-GenePostQC2))*(GenePostQC2/GenePostQC1) PostFCMat[,k]= GenePostFC k=k+1 } } colnames(FCMat)=colnames(PostFCMat)=ColNames Log2FCMat=log2(FCMat) Log2PostFCMat=log2(PostFCMat) Out=list(FCMat=FCMat,Log2FCMat=Log2FCMat, PostFCMat=PostFCMat, Log2PostFCMat=Log2PostFCMat, CondMeans=CondMeans, ConditionOrder=EBMultiOut$ConditionOrder) } EBSeq/R/GetMultiPP.R0000644000175100017510000000072012607264551015056 0ustar00biocbuildbiocbuildGetMultiPP <- function(EBout){ if(!"PPpattern"%in%names(EBout))stop("The input doesn't seem like an output from EBMultiTest") PP=EBout$PPpattern UnderFlow=which(is.na(rowSums(PP))) if(length(UnderFlow)!=0)Good=c(1:nrow(PP))[-UnderFlow] else Good=c(1:nrow(PP)) MAP=rep(NA,nrow(PP)) names(MAP)=rownames(PP) MAP[Good]=colnames(PP)[apply(PP[Good,],1,which.max)] MAP[UnderFlow]="NoTest" AllParti=EBout$AllParti out=list(PP=PP, MAP=MAP,Patterns=AllParti) } EBSeq/R/GetNg.R0000644000175100017510000000111312607264551014065 0ustar00biocbuildbiocbuildGetNg<- function(IsoformName, GeneName, TrunThre=3){ if(length(IsoformName)!=length(GeneName))stop("The length of IsoformName is not the same as the length of GeneName") GeneNg = tapply(IsoformName, GeneName, length) if(max(GeneNg)TrunThre]=TrunThre IsoformNgTrun=IsoformNg IsoformNgTrun[IsoformNgTrun>TrunThre]=TrunThre out=list( GeneNg=GeneNg, GeneNgTrun=GeneNgTrun, IsoformNg=IsoformNg, IsoformNgTrun=IsoformNgTrun) } EBSeq/R/GetNormalizedMat.R0000644000175100017510000000044512607264551016276 0ustar00biocbuildbiocbuildGetNormalizedMat<-function(Data, Sizes){ if(length(Sizes)!=length(Data) & length(Sizes)!=ncol(Data)) stop("The number of library size factors is not the same as the number of samples!") if(length(Sizes)==length(Data))Out=Data/Sizes if(length(Sizes)==ncol(Data))Out=t(t(Data)/Sizes) Out} EBSeq/R/GetPP.R0000644000175100017510000000020312607264551014037 0ustar00biocbuildbiocbuildGetPP <- function(EBout){ if(!"PPDE"%in%names(EBout))stop("The input doesn't seem like an output from EBTest") PP=EBout$PPDE } EBSeq/R/GetPPMat.R0000644000175100017510000000021012607264551014477 0ustar00biocbuildbiocbuildGetPPMat <- function(EBout){ if(!"PPMat"%in%names(EBout))stop("The input doesn't seem like an output from EBTest") PP=EBout$PPMat } EBSeq/R/GetPatterns.R0000644000175100017510000000064212607264551015327 0ustar00biocbuildbiocbuildGetPatterns<-function(Conditions){ if(!is.factor(Conditions))Conditions=as.factor(Conditions) NumCond=nlevels(Conditions) if(NumCond<3)stop("Less than 3 conditions!") CondLevels=levels(Conditions) AllPartiList=sapply(1:NumCond,function(i)nkpartitions(NumCond,i)) AllParti=do.call(rbind,AllPartiList) colnames(AllParti)=CondLevels rownames(AllParti)=paste("Pattern",1:nrow(AllParti),sep="") AllParti } EBSeq/R/Likefun.R0000644000175100017510000000137312607264551014466 0ustar00biocbuildbiocbuildLikefun <- function(ParamPool, InputPool) { NoneZeroLength=InputPool[[5]] AlphaIn=ParamPool[1] BetaIn=ParamPool[2:(1+NoneZeroLength)] PIn=ParamPool[2+NoneZeroLength] ZIn=InputPool[[4]] Input=InputPool[[3]] Input1=matrix(InputPool[[1]],nrow=nrow(Input)) Input2=matrix(InputPool[[2]],nrow=nrow(Input)) RIn=InputPool[[6]] RInSP1=matrix(InputPool[[7]],nrow=nrow(Input)) RInSP2=matrix(InputPool[[8]],nrow=nrow(Input)) NumIn=InputPool[[9]] ##Function here #LikelihoodFunction<- function(NoneZeroLength){ F0=f0(Input, AlphaIn, BetaIn, RIn, NumIn, log=T) F1=f1(Input1, Input2, AlphaIn, BetaIn, RInSP1,RInSP2, NumIn, log=T) F0[F0==Inf]=min(!is.na(F0[F0!=Inf])) F1[F1==Inf]=min(!is.na(F1[F1!=Inf])) -sum((1-ZIn)*F0+ (1-ZIn)* log(1-PIn) + ZIn*F1 + ZIn*log(PIn)) } EBSeq/R/LikefunMulti.R0000644000175100017510000000153112607264551015475 0ustar00biocbuildbiocbuildLikefunMulti <- function(ParamPool, InputPool) { NoneZeroLength=InputPool[[4]] AlphaIn=ParamPool[1] BetaIn=ParamPool[2:(1+NoneZeroLength)] PIn=ParamPool[(2+NoneZeroLength):length(ParamPool)] PInAll=c(1-sum(PIn),PIn) ZIn=InputPool[[3]] Input=InputPool[[2]] InputSP=InputPool[[1]] RIn=InputPool[[5]] RInSP=InputPool[[6]] NumIn=InputPool[[7]] AllParti=InputPool[[8]] PInMat=matrix(rep(1,nrow(Input)),ncol=1)%*%matrix(PInAll,nrow=1) ##Function here FList=sapply(1:nrow(AllParti),function(i)sapply(1:nlevels(as.factor(AllParti[i,])), function(j)f0(do.call(cbind,InputSP[AllParti[i,]==j]),AlphaIn, BetaIn, do.call(cbind,RInSP[AllParti[i,]==j]), NumIn, log=T)), simplify=F) FPartiLog=sapply(FList,rowSums) #FMat=exp(FPartiLog) FMat=FPartiLog -sum(ZIn*(FMat+log(PInMat))) } EBSeq/R/LogN.R0000644000175100017510000000451112607264551013725 0ustar00biocbuildbiocbuildLogN <- function(Input, InputSP, EmpiricalR, EmpiricalRSP, NumOfEachGroup, AlphaIn, BetaIn, PIn, NoneZeroLength) { #2 condition case (skip the loop then maybe run faster? Code multi condition cases later) #For each gene (m rows of Input---m genes) #Save each gene's F0, F1 for further likelihood calculation. #Get F0 for EE F0Log=f0(Input, AlphaIn, BetaIn, EmpiricalR, NumOfEachGroup, log=T) #Get F1 for DE F1Log=f1(InputSP[[1]], InputSP[[2]], AlphaIn, BetaIn, EmpiricalRSP[[1]],EmpiricalRSP[[2]], NumOfEachGroup, log=T) #Get z #Use data.list in logfunction F0LogMdf=F0Log+600 F1LogMdf=F1Log+600 F0Mdf=exp(F0LogMdf) F1Mdf=exp(F1LogMdf) z.list=PIn*F1Mdf/(PIn*F1Mdf+(1-PIn)*F0Mdf) zNaNName=names(z.list)[is.na(z.list)] zGood=which(!is.na(z.list)) if(length(zGood)==0){ #Min=min(min(F0Log[which(F0Log!=-Inf)]), # min(F1Log[which(F1Log!=-Inf)])) tmpMat=cbind(F0Log,F1Log) tmpMean=apply(tmpMat,1,mean) F0LogMdf=F0Log-tmpMean F1LogMdf=F1Log-tmpMean F0Mdf=exp(F0LogMdf) F1Mdf=exp(F1LogMdf) z.list=PIn*F1Mdf/(PIn*F1Mdf+(1-PIn)*F0Mdf) zNaNName=names(z.list)[is.na(z.list)] zGood=which(!is.na(z.list)) } ###Update P #PFromZ=sapply(1:NoneZeroLength,function(i) sum(z.list[[i]])/length(z.list[[i]])) PFromZ=sum(z.list[zGood])/length(z.list[zGood]) F0Good=F0Log[zGood] F1Good=F1Log[zGood] ### MLE Part #### # Since we dont wanna update p and Z in this step # Each Ng for one row NumGroupVector=rep(c(1:NoneZeroLength),NumOfEachGroup) NumGroupVector.zGood=NumGroupVector[zGood] NumOfEachGroup.zGood=tapply(NumGroupVector.zGood,NumGroupVector.zGood,length) StartValue=c(AlphaIn, BetaIn,PIn) Result<-optim(StartValue,Likefun,InputPool=list(InputSP[[1]][zGood,],InputSP[[2]][zGood,],Input[zGood,],z.list[zGood], NoneZeroLength,EmpiricalR[zGood, ],EmpiricalRSP[[1]][zGood,], EmpiricalRSP[[2]][zGood,], NumOfEachGroup.zGood)) #LikeOutput=Likelihood( StartValue, Input , InputSP , PNEW.list, z.list) AlphaNew= Result$par[1] BetaNew=Result$par[2:(1+NoneZeroLength)] PNew=Result$par[2+NoneZeroLength] ## Output=list(AlphaNew=AlphaNew,BetaNew=BetaNew,PNew=PNew,ZNew.list=z.list,PFromZ=PFromZ, zGood=zGood, zNaNName=zNaNName,F0Out=F0Good, F1Out=F1Good) Output } EBSeq/R/LogNMulti.R0000644000175100017510000000546512607264551014751 0ustar00biocbuildbiocbuildLogNMulti <- function(Input, InputSP, EmpiricalR, EmpiricalRSP, NumOfEachGroup, AlphaIn, BetaIn, PIn, NoneZeroLength, AllParti, Conditions) { #For each gene (m rows of Input---m genes) #Save each gene's F0, F1 for further likelihood calculation. FList=sapply(1:nrow(AllParti),function(i)sapply(1:nlevels(as.factor(AllParti[i,])), function(j)f0(do.call(cbind,InputSP[AllParti[i,]==j]),AlphaIn, BetaIn, do.call(cbind,EmpiricalRSP[AllParti[i,]==j]), NumOfEachGroup, log=T)), simplify=F) FPartiLog=sapply(FList,rowSums) FMat=exp(FPartiLog+600) rownames(FMat)=rownames(FPartiLog)=rownames(Input) #Get z #Use data.list in logfunction PInMat=matrix(rep(1,nrow(Input)),ncol=1)%*%matrix(PIn,nrow=1) FmultiP=FMat*PInMat Denom=rowSums(FmultiP) ZEach=apply(FmultiP,2,function(i)i/Denom) zNaNName1=names(Denom)[is.na(Denom)] # other NAs in LikeFun LF=ZEach*(log(FmultiP)) zNaNMore=rownames(LF)[which(is.na(rowSums(LF)))] zNaNName=unique(c(zNaNName1,zNaNMore)) zGood=which(!rownames(LF)%in%zNaNName) if(length(zGood)==0){ #Min=min(min(F0Log[which(F0Log!=-Inf)]), # min(F1Log[which(F1Log!=-Inf)])) tmpMat=FPartiLog tmpMean=apply(tmpMat,1,mean) FLogMdf=FPartiLog-tmpMean FMdf=exp(FLogMdf) FmultiPMdf=FMdf*PInMat DenomMdf=rowSums(FmultiPMdf) ZEach=apply(FmultiPMdf,2,function(i)i/DenomMdf) zNaNName1Mdf=names(DenomMdf)[is.na(DenomMdf)] # other NAs in LikeFun LFMdf=ZEach*(log(FmultiPMdf)) zNaNMoreMdf=rownames(LFMdf)[which(is.na(rowSums(LFMdf)))] zNaNNameMdf=unique(c(zNaNName1Mdf,zNaNMoreMdf)) zGood=which(!rownames(LFMdf)%in%zNaNNameMdf) } ZEachGood=ZEach[zGood,] ###Update P PFromZ=colSums(ZEach[zGood,])/length(zGood) FGood=FPartiLog[zGood,] ### MLE Part #### # Since we dont wanna update p and Z in this step # Each Ng for one row NumGroupVector=rep(c(1:NoneZeroLength),NumOfEachGroup) NumGroupVector.zGood=NumGroupVector[zGood] NumOfEachGroup.zGood=tapply(NumGroupVector.zGood,NumGroupVector.zGood,length) StartValue=c(AlphaIn, BetaIn,PIn[-1]) InputSPGood=sapply(1:length(InputSP),function(i)InputSP[[i]][zGood,],simplify=F) EmpiricalRSPGood=sapply(1:length(EmpiricalRSP),function(i)EmpiricalRSP[[i]][zGood,],simplify=F) Result<-optim(StartValue,LikefunMulti,InputPool=list(InputSPGood,Input[zGood,],ZEach[zGood,], NoneZeroLength,EmpiricalR[zGood, ],EmpiricalRSPGood, NumOfEachGroup.zGood, AllParti)) AlphaNew= Result$par[1] BetaNew=Result$par[2:(1+NoneZeroLength)] PNewNo1=Result$par[(2+NoneZeroLength):length(Result$par)] PNew=c(1-sum(PNewNo1),PNewNo1) ## Output=list(AlphaNew=AlphaNew,BetaNew=BetaNew,PNew=PNew,ZEachNew=ZEach, ZEachGood=ZEachGood, PFromZ=PFromZ, zGood=zGood, zNaNName=zNaNName,FGood=FGood) Output } EBSeq/R/MedianNorm.R0000644000175100017510000000107512607264551015121 0ustar00biocbuildbiocbuildMedianNorm <- function(Data, alternative=FALSE){ if(ncol(Data)==1)stop("Only 1 sample!") if(!alternative){ geomeans <- exp(rowMeans(log(Data))) out <- apply(Data, 2, function(cnts) median((cnts/geomeans)[geomeans > 0]))} if(alternative){ DataMatO <- Data N <- ncol(DataMatO) DataList0 <- sapply(1:N,function(i)DataMatO[,i]/DataMatO,simplify=F) DataEachMed0 <- sapply(1:N,function(i)apply(DataList0[[i]],2,function(j)median(j[which(j>0 & j 0) { out <- 1 - sort(PPEE)[index] } if (index == 0) { out <- 1 } names(out) <- NULL return(out) } EBSeq/R/f0.R0000644000175100017510000000172112607264551013373 0ustar00biocbuildbiocbuildf0 <- function(Input, AlphaIn, BetaIn, EmpiricalR, NumOfGroups, log) { BetaVect=do.call(c,sapply(1:length(BetaIn),function(i)rep(BetaIn[i],NumOfGroups[i]),simplify=F)) SampleNum=dim(Input)[2] #Product part ChooseParam1=round(Input+EmpiricalR-1) roundInput=round(Input) EachChoose0=matrix(sapply(1:SampleNum, function(i)lchoose(ChooseParam1[,i], roundInput[,i])),ncol=SampleNum) # numerical approximation to rescue -Inf ones NoNegInfMin=min(EachChoose0[which(EachChoose0!=-Inf)]) NoPosInfMax=max(EachChoose0[which(EachChoose0!=Inf)]) EachChoose=EachChoose0 EachChoose[which(EachChoose0==-Inf, arr.ind=T)]=NoNegInfMin EachChoose[which(EachChoose0==Inf, arr.ind=T)]=NoPosInfMax SumEachIso=rowSums(Input) param1=AlphaIn + rowSums(EmpiricalR) param2=BetaVect + SumEachIso LogConst=rowSums(EachChoose)+lbeta(param1, param2)-lbeta(AlphaIn, BetaVect) if (log==F) FinalResult=exp(LogConst) if (log==T) FinalResult=LogConst FinalResult } EBSeq/R/f1.R0000644000175100017510000000046312607264551013376 0ustar00biocbuildbiocbuildf1 <- function(Input1, Input2, AlphaIn, BetaIn, EmpiricalRSP1,EmpiricalRSP2,NumOfGroup, log){ F0.1=f0(Input1, AlphaIn, BetaIn, EmpiricalRSP1, NumOfGroup, log=log) F0.2=f0(Input2, AlphaIn, BetaIn, EmpiricalRSP2, NumOfGroup, log=log) if (log==F) Result=F0.1*F0.2 if (log==T) Result=F0.1+F0.2 Result } EBSeq/README.md0000644000175100017510000001112312607264551014016 0ustar00biocbuildbiocbuild# EBSeq Q & A ## ReadIn data csv file: ``` In=read.csv("FileName", stringsAsFactors=F, row.names=1, header=T) Data=data.matrix(In) ``` txt file: ``` In=read.table("FileName", stringsAsFactors=F, row.names=1, header=T) Data=data.matrix(In) ``` check str(Data) and make sure it is a matrix instead of data frame. You may need to play around with the row.names and header option depends on how the input file was generated. ## GetDEResults() function not found You may on an earlier version of EBSeq. The GetDEResults function was introduced since version 1.7.1. The latest release version could be found at: http://www.bioconductor.org/packages/devel/bioc/html/EBSeq.html And you may check your package version by typing packageVersion("EBSeq") ## Visualizing DE genes/isoforms To generate a heatmap, you may consider the heatmap.2 function in gplots package. For example, you may run ``` heatmap.2(NormalizedMatrix[GenesOfInterest,], scale="row", trace="none", Colv=F) ``` The normalized matrix may be obtained from GetNormalizedMat() function. ## My favorite gene/isoform has NA in PP (status "NoTest") The NoTest status comes from two sources 1) Using the default parameter settings of EBMultiTest(), the function will not test on genes with more than 75% values < 10 to ensure better model fitting. To disable this filter, you may set Qtrm=1 and QtrmCut=0. 2) numerical over/underflow in R. That happens when the within condition variance is extremely large or small. I did implemented a numerical approximation step to calculate the approximated PP for these genes with over/underflow. Here I use 10^-10 to approximate the parameter p in the NB distribution for these genes (I set it to a small value since I want to cover more over/underflow genes with low within-condition variation). You may try to tune this value (to a larger value) in the approximation by setting ApproxVal in EBTest() or EBMultiTest() function. ## Can I run more than 5 iterations when running EBSeq via RSEM wrapper? Yes you may modify the script rsem-for-ebseq-find-DE under RSEM/EBSeq change line 36 ``` EBOut <- EBTest(Data = DataMat, NgVector = ngvector, Conditions = conditions, sizeFactors = Sizes, maxround = 5) ``` to ``` EBOut <- EBTest(Data = DataMat, NgVector = ngvector, Conditions = conditions, sizeFactors = Sizes, maxround = 10) ``` If you are running multiple condition analysis, you will need to change line 53: ``` MultiOut <- EBMultiTest(Data = DataMat, NgVector = ngvector, Conditions = conditions, AllParti = patterns, sizeFactors = Sizes, maxround = 5) ``` ``` MultiOut <- EBMultiTest(Data = DataMat, NgVector = ngvector, Conditions = conditions, AllParti = patterns, sizeFactors = Sizes, maxround = 10) ``` You will need to redo make after you make the changes. ## I saw a gene has significant FC but is not called as DE by EBSeq, why does that happen? EBSeq calls a gene as DE (assign high PPDE) if the across-condition variability is significantly larger than the within-condition variability. In the cases that a gene has large within-condition variation, although the FC across two conditions is large (small), the across-condition difference could still be explained by biological variation within condition. In these cases the gene/isoform will have a moderate PPDE. ## Can I look at TPMs/RPKMs/FPKMs across samples? In general, it is not appropriate to perform cross sample comparisons using TPM, FPKM or RPKM without further normalization. Instead, you may use normalized counts (It can be generated by GetNormalizedMat() function from raw count, note EBSeq testing functions takes raw counts and library size factors) Here is an example: Suppose there are 2 samples S1 and S2 from different conditions. Each has 5 genes. For simplicity, we assume each of 5 genes contains only one isoform and all genes have the same length. Assume only gene 5 is DE and the gene expressions of these 5 genes are: |Sample|g1|g2|g3|g4|g5| |---|---|---|---|---|---| |S1|10|10|10|10|10| |S2| 20 | 20 | 20 | 20 | 100 | Then the TPM/FPKM/RPKM will be (note sum TPM/FPKM/RPKM of all genes should be 10^6 ): |Sample|g1|g2|g3|g4|g5| |---|---|---|---|---|---| | S1 | 2x10^5 | 2x10^5 | 2x10^5 | 2x10^5 | 2x10^5 | | S2 | 1.1x10^5| 1.1x10^5| 1.1x10^5| 1.1x10^5| 5.6x10^5| Based on TPM/FPKM/RPKM, an investigator may conclude that the first 4 genes are down-regulated and the 5th gene is up-regulated. Then we will get 4 false positive calls. Cross-sample TPM/FPKM/RPKM comparisons will be feasible only when no hypothetical DE genes present across samples (Or when assuming the DE genes are sort of 'symmetric' regarding up and down regulation). 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BG!Q(~? bG1Q (~?bG1Q (~?bG1Q (~?bG1Q (~?bG1Q (~?bG1Q (~?bG1Q (~?JRG)Q (~?JRG)Q (~?JRG)Q (~?JRG)Q (~?JRG)Q (~?JRG)Q (~?ʁrG9Q(~?ʁrG9Q(~?ʁrG9Q(~?ʁrG9Q(~?ʁrG9Q(~?ʁrG9Q(~?*JG%Q ~T?*JG%Q ~T?*JG%Q ~T?*JG%Q ~T?*JG%Q ~T?*JG%Q ~T?*jG5Q ~T?jG5Q ~T?jG5Q ~T?jG5Q ~T?jG5Q ~T?jG5Q ~T?jGC~W}EBSeq/demo/0000755000175100017510000000000012607264551013465 5ustar00biocbuildbiocbuildEBSeq/demo/00Index0000644000175100017510000000001412607264550014611 0ustar00biocbuildbiocbuildEBSeq demo EBSeq/demo/EBSeq.R0000644000175100017510000001536412607264551014560 0ustar00biocbuildbiocbuildlibrary(EBSeq) # 3.1 data(GeneMat) str(GeneMat) Sizes=MedianNorm(GeneMat) EBOut=EBTest(Data=GeneMat, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=Sizes, maxround=5) DEOut=GetDEResults(EBOut) str(DEOut) #3.2 data(IsoList) str(IsoList) IsoMat=IsoList$IsoMat str(IsoMat) IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames IsoSizes=MedianNorm(IsoMat) NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoNgTrun[c(1:3,201:203,601:603)] IsoEBOut=EBTest(Data=IsoMat, NgVector=IsoNgTrun, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=IsoSizes, maxround=5) IsoDE=GetDEResults(IsoEBOut) str(IsoDE) #3.3 data(MultiGeneMat) str(MultiGeneMat) Conditions=c("C1","C1","C2","C2","C3","C3") PosParti=GetPatterns(Conditions) PosParti Parti=PosParti[-3,] Parti MultiSize=MedianNorm(MultiGeneMat) MultiOut=EBMultiTest(MultiGeneMat,NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) MultiPP=GetMultiPP(MultiOut) names(MultiPP) MultiPP$PP[1:10,] MultiPP$MAP[1:10] MultiPP$Patterns #3.4 data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiSize=MedianNorm(IsoMultiMat) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C1","C2","C2","C3","C3","C4","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond IsoMultiOut=EBMultiTest(IsoMultiMat,NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize, maxround=5) IsoMultiPP=GetMultiPP(IsoMultiOut) names(MultiPP) IsoMultiPP$PP[1:10,] IsoMultiPP$MAP[1:10] IsoMultiPP$Patterns #4.1 data(GeneMat) str(GeneMat) Sizes=MedianNorm(GeneMat) EBOut=EBTest(Data=GeneMat, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=Sizes, maxround=5) DEOut=GetDEResults(EBOut) EBOut$Alpha EBOut$Beta EBOut$P GeneFC=PostFC(EBOut) str(GeneFC) par(mfrow=c(2,2)) QQP(EBOut) par(mfrow=c(2,2)) DenNHist(EBOut) PlotPostVsRawFC(EBOut,GeneFC) #4.2 data(IsoList) str(IsoList) IsoMat=IsoList$IsoMat str(IsoMat) IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames IsoSizes=MedianNorm(IsoMat) NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoNgTrun[c(1:3,201:203,601:603)] IsoEBOut=EBTest(Data=IsoMat, NgVector=IsoNgTrun, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=IsoSizes, maxround=5) IsoDE=GetDEResults(IsoEBOut) str(IsoDE) IsoEBOut$Alpha IsoEBOut$Beta IsoEBOut$P IsoFC=PostFC(IsoEBOut) str(IsoFC) PlotPostVsRawFC(IsoEBOut,IsoFC) par(mfrow=c(2,2)) PolyFitValue=vector("list",3) for(i in 1:3) PolyFitValue[[i]]=PolyFitPlot(IsoEBOut$C1Mean[[i]], IsoEBOut$C1EstVar[[i]],5) PolyAll=PolyFitPlot(unlist(IsoEBOut$C1Mean), unlist(IsoEBOut$C1EstVar),5) lines(log10(IsoEBOut$C1Mean[[1]][PolyFitValue[[1]]$sort]), PolyFitValue[[1]]$fit[PolyFitValue[[1]]$sort],col="yellow",lwd=2) lines(log10(IsoEBOut$C1Mean[[2]][PolyFitValue[[2]]$sort]), PolyFitValue[[2]]$fit[PolyFitValue[[2]]$sort],col="pink",lwd=2) lines(log10(IsoEBOut$C1Mean[[3]][PolyFitValue[[3]]$sort]), PolyFitValue[[3]]$fit[PolyFitValue[[3]]$sort],col="green",lwd=2) legend("topleft",c("All Isoforms","Ig = 1","Ig = 2","Ig = 3"), col=c("red","yellow","pink","green"),lty=1,lwd=3,box.lwd=2) par(mfrow=c(2,3)) QQP(IsoEBOut) par(mfrow=c(2,3)) DenNHist(IsoEBOut) #4.3 data(MultiGeneMat) str(MultiGeneMat) Conditions=c("C1","C1","C2","C2","C3","C3") PosParti=GetPatterns(Conditions) PosParti PlotPattern(PosParti) Parti=PosParti[-3,] Parti MultiSize=MedianNorm(MultiGeneMat) MultiOut=EBMultiTest(MultiGeneMat,NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) MultiPP=GetMultiPP(MultiOut) names(MultiPP) MultiPP$PP[1:10,] MultiPP$MAP[1:10] MultiPP$Patterns MultiFC=GetMultiFC(MultiOut) str(MultiFC) par(mfrow=c(2,2)) DenNHist(MultiOut) par(mfrow=c(2,2)) QQP(MultiOut) #4.4 data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiSize=MedianNorm(IsoMultiMat) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C1","C2","C2","C3","C3","C4","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond PlotPattern(PosParti.4Cond) Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond IsoMultiOut=EBMultiTest(IsoMultiMat,NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize, maxround=5) IsoMultiPP=GetMultiPP(IsoMultiOut) names(MultiPP) IsoMultiPP$PP[1:10,] IsoMultiPP$MAP[1:10] IsoMultiPP$Patterns IsoMultiFC=GetMultiFC(IsoMultiOut) str(IsoMultiFC) par(mfrow=c(3,4)) DenNHist(IsoMultiOut) par(mfrow=c(3,4)) QQP(IsoMultiOut) IsoMultiFC=GetMultiFC(IsoMultiOut) #4.5 data(GeneMat) GeneMat.norep=GeneMat[,c(1,6)] Sizes.norep=MedianNorm(GeneMat.norep) EBOut.norep=EBTest(Data=GeneMat.norep, Conditions=as.factor(rep(c("C1","C2"))),sizeFactors=Sizes.norep, maxround=5) DE.norep=GetDEResults(EBOut.norep) GeneFC.norep=PostFC(EBOut.norep) #4.6 data(IsoList) IsoMat=IsoList$IsoMat IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoMat.norep=IsoMat[,c(1,6)] IsoSizes.norep=MedianNorm(IsoMat.norep) IsoEBOut.norep=EBTest(Data=IsoMat.norep, NgVector=IsoNgTrun, Conditions=as.factor(c("C1","C2")),sizeFactors=IsoSizes.norep, maxround=5) IsoDE.norep=GetDEResults(IsoEBOut.norep) IsoFC.norep=PostFC(IsoEBOut.norep) #4.7 data(MultiGeneMat) MultiGeneMat.norep=MultiGeneMat[,c(1,3,5)] Conditions=c("C1","C2","C3") PosParti=GetPatterns(Conditions) Parti=PosParti[-3,] MultiSize.norep=MedianNorm(MultiGeneMat.norep) MultiOut.norep=EBMultiTest(MultiGeneMat.norep,NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize.norep, maxround=5) MultiPP.norep=GetMultiPP(MultiOut.norep) MultiFC.norep=GetMultiFC(MultiOut.norep) #4.8 data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiMat.norep=IsoMultiMat[,c(1,3,5,7)] IsoMultiSize.norep=MedianNorm(IsoMultiMat.norep) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C2","C3","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond IsoMultiOut.norep=EBMultiTest(IsoMultiMat.norep,NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize.norep, maxround=5) IsoMultiPP.norep=GetMultiPP(IsoMultiOut.norep) IsoMultiFC.norep=GetMultiFC(IsoMultiOut.norep) # EOF EBSeq/inst/0000755000175100017510000000000012607342353013513 5ustar00biocbuildbiocbuildEBSeq/inst/doc/0000755000175100017510000000000012607342353014260 5ustar00biocbuildbiocbuildEBSeq/inst/doc/EBSeq_Vignette.R0000644000175100017510000003627712607342353017226 0ustar00biocbuildbiocbuild### R code from vignette source 'EBSeq_Vignette.Rnw' ################################################### ### code chunk number 1: EBSeq_Vignette.Rnw:172-173 ################################################### library(EBSeq) ################################################### ### code chunk number 2: EBSeq_Vignette.Rnw:198-200 ################################################### data(GeneMat) str(GeneMat) ################################################### ### code chunk number 3: EBSeq_Vignette.Rnw:208-209 ################################################### Sizes=MedianNorm(GeneMat) ################################################### ### code chunk number 4: EBSeq_Vignette.Rnw:235-237 ################################################### EBOut=EBTest(Data=GeneMat, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=Sizes, maxround=5) ################################################### ### code chunk number 5: EBSeq_Vignette.Rnw:240-244 ################################################### EBDERes=GetDEResults(EBOut, FDR=0.05) str(EBDERes$DEfound) head(EBDERes$PPMat) str(EBDERes$Status) ################################################### ### code chunk number 6: EBSeq_Vignette.Rnw:289-295 ################################################### data(IsoList) str(IsoList) IsoMat=IsoList$IsoMat str(IsoMat) IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames ################################################### ### code chunk number 7: EBSeq_Vignette.Rnw:302-303 ################################################### IsoSizes=MedianNorm(IsoMat) ################################################### ### code chunk number 8: EBSeq_Vignette.Rnw:324-327 ################################################### NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoNgTrun[c(1:3,201:203,601:603)] ################################################### ### code chunk number 9: EBSeq_Vignette.Rnw:339-345 ################################################### IsoEBOut=EBTest(Data=IsoMat, NgVector=IsoNgTrun, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=IsoSizes, maxround=5) IsoEBDERes=GetDEResults(IsoEBOut, FDR=0.05) str(IsoEBDERes$DEfound) head(IsoEBDERes$PPMat) str(IsoEBDERes$Status) ################################################### ### code chunk number 10: EBSeq_Vignette.Rnw:368-370 ################################################### data(MultiGeneMat) str(MultiGeneMat) ################################################### ### code chunk number 11: EBSeq_Vignette.Rnw:378-381 ################################################### Conditions=c("C1","C1","C2","C2","C3","C3") PosParti=GetPatterns(Conditions) PosParti ################################################### ### code chunk number 12: EBSeq_Vignette.Rnw:389-391 ################################################### Parti=PosParti[-3,] Parti ################################################### ### code chunk number 13: EBSeq_Vignette.Rnw:396-399 ################################################### MultiSize=MedianNorm(MultiGeneMat) MultiOut=EBMultiTest(MultiGeneMat,NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) ################################################### ### code chunk number 14: EBSeq_Vignette.Rnw:403-408 ################################################### MultiPP=GetMultiPP(MultiOut) names(MultiPP) MultiPP$PP[1:10,] MultiPP$MAP[1:10] MultiPP$Patterns ################################################### ### code chunk number 15: EBSeq_Vignette.Rnw:427-435 ################################################### data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiSize=MedianNorm(IsoMultiMat) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C1","C2","C2","C3","C3","C4","C4") ################################################### ### code chunk number 16: EBSeq_Vignette.Rnw:441-443 ################################################### PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond ################################################### ### code chunk number 17: EBSeq_Vignette.Rnw:448-450 ################################################### Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond ################################################### ### code chunk number 18: EBSeq_Vignette.Rnw:455-459 ################################################### IsoMultiOut=EBMultiTest(IsoMultiMat, NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize, maxround=5) ################################################### ### code chunk number 19: EBSeq_Vignette.Rnw:463-468 ################################################### IsoMultiPP=GetMultiPP(IsoMultiOut) names(MultiPP) IsoMultiPP$PP[1:10,] IsoMultiPP$MAP[1:10] IsoMultiPP$Patterns ################################################### ### code chunk number 20: EBSeq_Vignette.Rnw:485-490 (eval = FALSE) ################################################### ## data(GeneMat) ## Sizes=MedianNorm(GeneMat) ## EBOut=EBTest(Data=GeneMat, ## Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=Sizes, maxround=5) ## EBDERes=GetDEResults(EBOut, FDR=0.05) ################################################### ### code chunk number 21: EBSeq_Vignette.Rnw:492-496 ################################################### EBDERes=GetDEResults(EBOut, FDR=0.05) str(EBDERes$DEfound) head(EBDERes$PPMat) str(EBDERes$Status) ################################################### ### code chunk number 22: EBSeq_Vignette.Rnw:506-509 ################################################### GeneFC=PostFC(EBOut) str(GeneFC) PlotPostVsRawFC(EBOut,GeneFC) ################################################### ### code chunk number 23: EBSeq_Vignette.Rnw:530-533 ################################################### EBOut$Alpha EBOut$Beta EBOut$P ################################################### ### code chunk number 24: EBSeq_Vignette.Rnw:552-554 ################################################### par(mfrow=c(1,2)) QQP(EBOut) ################################################### ### code chunk number 25: EBSeq_Vignette.Rnw:570-572 ################################################### par(mfrow=c(1,2)) DenNHist(EBOut) ################################################### ### code chunk number 26: EBSeq_Vignette.Rnw:593-598 (eval = FALSE) ################################################### ## data(IsoList) ## IsoMat=IsoList$IsoMat ## IsoNames=IsoList$IsoNames ## IsosGeneNames=IsoList$IsosGeneNames ## NgList=GetNg(IsoNames, IsosGeneNames, TrunThre=3) ################################################### ### code chunk number 27: EBSeq_Vignette.Rnw:600-603 ################################################### names(NgList) IsoNgTrun=NgList$IsoformNgTrun IsoNgTrun[c(1:3,201:203,601:603)] ################################################### ### code chunk number 28: EBSeq_Vignette.Rnw:634-635 (eval = FALSE) ################################################### ## IsoNgTrun = scan(file="output_name.ngvec", what=0, sep="\n") ################################################### ### code chunk number 29: EBSeq_Vignette.Rnw:648-652 (eval = FALSE) ################################################### ## IsoSizes=MedianNorm(IsoMat) ## IsoEBOut=EBTest(Data=IsoMat, NgVector=IsoNgTrun, ## Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=IsoSizes, maxround=5) ## IsoEBDERes=GetDEResults(IsoEBOut, FDR=0.05) ################################################### ### code chunk number 30: EBSeq_Vignette.Rnw:654-655 ################################################### str(IsoEBDERes) ################################################### ### code chunk number 31: EBSeq_Vignette.Rnw:660-662 ################################################### IsoFC=PostFC(IsoEBOut) str(IsoFC) ################################################### ### code chunk number 32: EBSeq_Vignette.Rnw:673-676 ################################################### IsoEBOut$Alpha IsoEBOut$Beta IsoEBOut$P ################################################### ### code chunk number 33: EBSeq_Vignette.Rnw:695-700 ################################################### par(mfrow=c(2,2)) PolyFitValue=vector("list",3) for(i in 1:3) PolyFitValue[[i]]=PolyFitPlot(IsoEBOut$C1Mean[[i]], IsoEBOut$C1EstVar[[i]],5) ################################################### ### code chunk number 34: EBSeq_Vignette.Rnw:713-722 ################################################### PolyAll=PolyFitPlot(unlist(IsoEBOut$C1Mean), unlist(IsoEBOut$C1EstVar),5) lines(log10(IsoEBOut$C1Mean[[1]][PolyFitValue[[1]]$sort]), PolyFitValue[[1]]$fit[PolyFitValue[[1]]$sort],col="yellow",lwd=2) lines(log10(IsoEBOut$C1Mean[[2]][PolyFitValue[[2]]$sort]), PolyFitValue[[2]]$fit[PolyFitValue[[2]]$sort],col="pink",lwd=2) lines(log10(IsoEBOut$C1Mean[[3]][PolyFitValue[[3]]$sort]), PolyFitValue[[3]]$fit[PolyFitValue[[3]]$sort],col="green",lwd=2) legend("topleft",c("All Isoforms","Ng = 1","Ng = 2","Ng = 3"), col=c("red","yellow","pink","green"),lty=1,lwd=3,box.lwd=2) ################################################### ### code chunk number 35: EBSeq_Vignette.Rnw:735-737 ################################################### par(mfrow=c(2,3)) QQP(IsoEBOut) ################################################### ### code chunk number 36: EBSeq_Vignette.Rnw:749-751 ################################################### par(mfrow=c(2,3)) DenNHist(IsoEBOut) ################################################### ### code chunk number 37: EBSeq_Vignette.Rnw:768-772 ################################################### Conditions=c("C1","C1","C2","C2","C3","C3") PosParti=GetPatterns(Conditions) PosParti PlotPattern(PosParti) ################################################### ### code chunk number 38: EBSeq_Vignette.Rnw:779-781 ################################################### Parti=PosParti[-3,] Parti ################################################### ### code chunk number 39: EBSeq_Vignette.Rnw:787-793 (eval = FALSE) ################################################### ## data(MultiGeneMat) ## MultiSize=MedianNorm(MultiGeneMat) ## MultiOut=EBMultiTest(MultiGeneMat, ## NgVector=NULL,Conditions=Conditions, ## AllParti=Parti, sizeFactors=MultiSize, ## maxround=5) ################################################### ### code chunk number 40: EBSeq_Vignette.Rnw:797-802 ################################################### MultiPP=GetMultiPP(MultiOut) names(MultiPP) MultiPP$PP[1:10,] MultiPP$MAP[1:10] MultiPP$Patterns ################################################### ### code chunk number 41: EBSeq_Vignette.Rnw:809-811 ################################################### MultiFC=GetMultiFC(MultiOut) str(MultiFC) ################################################### ### code chunk number 42: EBSeq_Vignette.Rnw:820-822 ################################################### par(mfrow=c(2,2)) QQP(MultiOut) ################################################### ### code chunk number 43: EBSeq_Vignette.Rnw:830-832 ################################################### par(mfrow=c(2,2)) DenNHist(MultiOut) ################################################### ### code chunk number 44: EBSeq_Vignette.Rnw:847-850 ################################################### Conditions=c("C1","C1","C2","C2","C3","C3","C4","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond ################################################### ### code chunk number 45: EBSeq_Vignette.Rnw:855-858 ################################################### PlotPattern(PosParti.4Cond) Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond ################################################### ### code chunk number 46: EBSeq_Vignette.Rnw:865-876 (eval = FALSE) ################################################### ## data(IsoMultiList) ## IsoMultiMat=IsoMultiList[[1]] ## IsoNames.Multi=IsoMultiList$IsoNames ## IsosGeneNames.Multi=IsoMultiList$IsosGeneNames ## IsoMultiSize=MedianNorm(IsoMultiMat) ## NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) ## IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun ## IsoMultiOut=EBMultiTest(IsoMultiMat,NgVector=IsoNgTrun.Multi,Conditions=Conditions, ## AllParti=Parti.4Cond, ## sizeFactors=IsoMultiSize, maxround=5) ## IsoMultiPP=GetMultiPP(IsoMultiOut) ################################################### ### code chunk number 47: EBSeq_Vignette.Rnw:878-883 ################################################### names(MultiPP) IsoMultiPP$PP[1:10,] IsoMultiPP$MAP[1:10] IsoMultiPP$Patterns IsoMultiFC=GetMultiFC(IsoMultiOut) ################################################### ### code chunk number 48: EBSeq_Vignette.Rnw:894-897 ################################################### par(mfrow=c(3,4)) QQP(IsoMultiOut) ################################################### ### code chunk number 49: EBSeq_Vignette.Rnw:907-909 ################################################### par(mfrow=c(3,4)) DenNHist(IsoMultiOut) ################################################### ### code chunk number 50: EBSeq_Vignette.Rnw:941-949 ################################################### data(GeneMat) GeneMat.norep=GeneMat[,c(1,6)] Sizes.norep=MedianNorm(GeneMat.norep) EBOut.norep=EBTest(Data=GeneMat.norep, Conditions=as.factor(rep(c("C1","C2"))), sizeFactors=Sizes.norep, maxround=5) EBDERes.norep=GetDEResults(EBOut.norep) GeneFC.norep=PostFC(EBOut.norep) ################################################### ### code chunk number 51: EBSeq_Vignette.Rnw:959-972 ################################################### data(IsoList) IsoMat=IsoList$IsoMat IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoMat.norep=IsoMat[,c(1,6)] IsoSizes.norep=MedianNorm(IsoMat.norep) IsoEBOut.norep=EBTest(Data=IsoMat.norep, NgVector=IsoNgTrun, Conditions=as.factor(c("C1","C2")), sizeFactors=IsoSizes.norep, maxround=5) IsoEBDERes.norep=GetDEResults(IsoEBOut.norep) IsoFC.norep=PostFC(IsoEBOut.norep) ################################################### ### code chunk number 52: EBSeq_Vignette.Rnw:981-993 ################################################### data(MultiGeneMat) MultiGeneMat.norep=MultiGeneMat[,c(1,3,5)] Conditions=c("C1","C2","C3") PosParti=GetPatterns(Conditions) Parti=PosParti[-3,] MultiSize.norep=MedianNorm(MultiGeneMat.norep) MultiOut.norep=EBMultiTest(MultiGeneMat.norep, NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize.norep, maxround=5) MultiPP.norep=GetMultiPP(MultiOut.norep) MultiFC.norep=GetMultiFC(MultiOut.norep) ################################################### ### code chunk number 53: EBSeq_Vignette.Rnw:1005-1024 ################################################### data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiMat.norep=IsoMultiMat[,c(1,3,5,7)] IsoMultiSize.norep=MedianNorm(IsoMultiMat.norep) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C2","C3","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond IsoMultiOut.norep=EBMultiTest(IsoMultiMat.norep, NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize.norep, maxround=5) IsoMultiPP.norep=GetMultiPP(IsoMultiOut.norep) IsoMultiFC.norep=GetMultiFC(IsoMultiOut.norep) EBSeq/inst/doc/EBSeq_Vignette.Rnw0000644000175100017510000013161212607342353017560 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{EBSeq Vignette} \documentclass{article} \usepackage{fullpage} \usepackage{graphicx, graphics, epsfig,setspace,amsmath, amsthm} \usepackage{hyperref} \usepackage{natbib} %\usepackage{listings} \usepackage{moreverb} \begin{document} \title{EBSeq: An R package for differential expression analysis using RNA-seq data} \author{Ning Leng, John Dawson, and Christina Kendziorski} \maketitle \tableofcontents \setcounter{tocdepth}{2} \section{Introduction} EBSeq may be used to identify differentially expressed (DE) genes and isoforms in an RNA-Seq experiment. As detailed in Leng {\it et al.}, 2013 \cite{Leng13}, EBSeq is an empirical Bayesian approach that models a number of features observed in RNA-seq data. Importantly, for isoform level inference, EBSeq directly accommodates isoform expression estimation uncertainty by modeling the differential variability observed in distinct groups of isoforms. Consider Figure 1, where we have plotted variance against mean for all isoforms using RNA-Seq expression data from Leng {\it et al.}, 2013 \cite{Leng13}. Also shown is the fit within three sub-groups of isoforms defined by the number of constituent isoforms of the parent gene. An isoform of gene $g$ is assigned to the $I_g=k$ group, where $k=1,2,3$, if the total number of isoforms from gene $g$ is $k$ (the $I_g=3$ group contains all isoforms from genes having 3 or more isoforms). As shown in Figure 1, there is decreased variability in the $I_g=1$ group, but increased variability in the others, due to the relative increase in uncertainty inherent in estimating isoform expression when multiple isoforms of a given gene are present. If this structure is not accommodated, there is reduced power for identifying isoforms in the $I_g=1$ group (since the true variances in that group are lower, on average, than that derived from the full collection of isoforms) as well as increased false discoveries in the $I_g=2$ and $I_g=3$ groups (since the true variances are higher, on average, than those derived from the full collection). EBSeq directly models differential variability as a function of $I_g$ providing a powerful approach for isoform level inference. As shown in Leng {\it et al.}, 2013 \cite{Leng13}, the model is also useful for identifying DE genes. We will briefly detail the model in Section \ref{sec:model} and then describe the flow of analysis in Section \ref{sec:quickstart} for both isoform and gene-level inference. \begin{figure}[t] \centering \includegraphics[width=0.6\textwidth]{PlotExample.png} \label{fig:GouldNg} \caption{Empirical variance vs. mean for each isoform profiled in the ESCs vs iPSCs experiment detailed in the Case Study section of Leng {\it et al.}, 2013 \cite{Leng13}. A spline fit to all isoforms is shown in red with splines fit within the $I_g=1$, $I_g=2$, and $I_g=3$ isoform groups shown in yellow, pink, and green, respectively.} \end{figure} \section{Citing this software} \label{sec:cite} Please cite the following article when reporting results from the software. \noindent Leng, N., J.A. Dawson, J.A. Thomson, V. Ruotti, A.I. Rissman, B.M.G. Smits, J.D. Haag, M.N. Gould, R.M. Stewart, and C. Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments, {\it Bioinformatics}, 2013. \section{The Model} \label{sec:model} \subsection{Two conditions} \label{sec:twocondmodel} We let $X_{g_i}^{C1} = X_{g_i,1} ,X_{g_i,2}, ...,X_{g_i,S_1}$ denote data from condition 1 and $ X_{g_i}^{C2} = X_{g_i,(S_1+1)},X_{g_i,(S_1+2)},...,X_{g_i,S}$ data from condition 2. We assume that counts within condition $C$ are distributed as Negative Binomial: $X_{g_i,s}^C|r_{g_i,s}, q_{g_i}^C \sim NB(r_{g_i,s}, q_{g_i}^C)$ where \begin{equation} P(X_{g_i,s}|r_{g_i,s},q_{g_i}^C) = {X_{g_i,s}+r_{g_i,s}-1\choose X_{g_i,s}}(1-q_{g_i}^C)^{X_{g_i,s}}(q_{g_i}^C)^{r_{g_i,s}}\label{eq:01} \end{equation} \noindent and $\mu_{g_i,s}^C=r_{g_i,s} (1-q_{g_i}^C)/q_{g_i}^C$; $(\sigma_{g_i,s}^C)^2=r_{g_i,s} (1-q_{g_i}^C)/(q_{g_i}^C)^2.$ \medskip We assume a prior distribution on $q_{g_i}^C$: $q_{g_i}^C|\alpha, \beta^{I_g} \sim Beta(\alpha, \beta^{I_g})$. The hyperparameter $\alpha$ is shared by all the isoforms and $\beta^{I_g}$ is $I_g$ specific (note this is an index, not a power). We further assume that $r_{g_i,s}=r_{g_i,0} l_s$, where $r_{g_i,0}$ is an isoform specific parameter common across conditions and $r_{g_i,s}$ depends on it through the sample-specific normalization factor $l_s$. Of interest in this two group comparison is distinguishing between two cases, or what we will refer to subsequently as two patterns of expression, namely equivalent expression (EE) and differential expression (DE): \begin{center} $H_0$ (EE) : $q_{g_i}^{C1}=q_{g_i}^{C2}$ vs $H_1$ (DE) : $q_{g_i}^{C1} \neq q_{g_i}^{C2}$. \end{center} Under the null hypothesis (EE), the data $X_{g_i}^{C1,C2} = X_{g_i}^{C1}, X_{g_i}^{C2}$ arises from the prior predictive distribution $f_0^{I_g}(X_{g_i}^{C1,C2})$: %\tiny \begin{equation} f_0^{I_g}(X_{g_i}^{C1,C2})=\Bigg[\prod_{s=1}^S {X_{g_i,s}+r_{g_i,s}-1\choose X_{g_i,s}}\Bigg] \frac{Beta(\alpha+\sum_{s=1}^S r_{g_i,s}, \beta^{I_g}+\sum_{s=1}^SX_{g_i,s} )}{Beta(\alpha, \beta^{I_g})}\label{eq:05} \end{equation} %\normalsize Alternatively (in a DE scenario), $X_{g_i}^{C1,C2}$ follows the prior predictive distribution $f_1^{I_g}(X_{g_i}^{C1,C2})$: \begin{equation} f_1^{I_g}(X_{g_i}^{C1,C2})=f_0^{I_g}(X_{g_i}^{C1})f_0^{I_g}(X_{g_i}^{C2}) \label{eq:06} \end{equation} Let the latent variable $Z_{g_i}$ be defined so that $Z_{g_i} = 1$ indicates that isoform $g_i$ is DE and $Z_{g_i} = 0$ indicates isoform $g_i$ is EE, and $Z_{g_i} \sim Bernoulli(p)$. Then, the marginal distribution of $X_{g_i}^{C1,C2}$ and $Z_{g_i}$ is: \begin{equation} (1-p)f_0^{I_g}(X_{g_i}^{C1,C2}) + pf_1^{I_g}(X_{g_i}^{C1,C2})\label{eq:07} \end{equation} \noindent The posterior probability of being DE at isoform $g_i$ is obtained by Bayes' rule: \begin{equation} \frac{pf_1^{I_g}(X_{g_i}^{C1,C2})}{(1-p)f_0^{I_g}(X_{g_i}^{C1,C2}) + pf_1^{I_g}(X_{g_i}^{C1,C2})}\label{eq:08} \end{equation} %\newpage \subsection{More than two conditions} \label{sec:multicondmodel} EBSeq naturally accommodates multiple condition comparisons. For example, in a study with 3 conditions, there are K=5 possible expression patterns (P1,...,P5), or ways in which latent levels of expression may vary across conditions: \begin{align} \textrm {P1:}& \hspace{0.05in} q_{g_i}^{C1} = q_{g_i}^{C2}=q_{g_i}^{C3} \nonumber \\ \textrm {P2:}& \hspace{0.05in} q_{g_i}^{C1} = q_{g_i}^{C2} \neq q_{g_i}^{C3} \nonumber \\ \textrm {P3:}& \hspace{0.05in} q_{g_i}^{C1} = q_{g_i}^{C3} \neq q_{g_i}^{C2} \nonumber \\ \textrm {P4:}& \hspace{0.05in} q_{g_i}^{C1} \neq q_{g_i}^{C2} = q_{g_i}^{C3} \nonumber \\ \textrm {P5:}& \hspace{0.05in} q_{g_i}^{C1} \neq q_{g_i}^{C2} \neq q_{g_i}^{C3} \textrm{ and } q_{g_i}^{C1} \neq q_{g_i}^{C3} \nonumber \end{align} \noindent The prior predictive distributions for these are given, respectively, by: \begin{align} g_1^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1,C2,C3}) \nonumber \\ g_2^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1,C2})f_0^{I_g}(X_{g_i}^{C3}) \nonumber \\ g_3^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1,C3})f_0^{I_g}(X_{g_i}^{C2}) \nonumber \\ g_4^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1})f_0^{I_g}(X_{g_i}^{C2,C3}) \nonumber \\ g_5^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1})f_0^{I_g}(X_{g_i}^{C2})f_0^{I_g}(X_{g_i}^{C3}) \nonumber \end{align} \noindent where $f_0^{I_g}$ is the same as in equation \ref{eq:05}. Then the marginal distribution in equation \ref{eq:07} becomes: \begin{equation} \sum_{k=1}^5 p_k g_k^{I_g}(X_{g_i}^{C1,C2,C3}) \label{eq:11} \end{equation} \noindent where $\sum_{k=1}^5 p_k = 1$. Thus, the posterior probability of isoform $g_i$ coming from pattern $K$ is readily obtained by: \begin{equation} \frac{p_K g_K^{I_g}(X_{g_i}^{C1,C2,C3})}{\sum_{k=1}^5 p_k g_k^{I_g}(X_{g_i}^{C1,C2,C3})} \label{eq:12} \end{equation} \subsection{Getting a false discovery rate (FDR) controlled list of genes or isoforms} \label{sec:fdrlist} To obtain a list of DE genes with false discovery rate (FDR) controlled at $\alpha$ in an experiment comparing two biological conditions, the genes with posterior probability of being DE (PPDE) greater than 1 - $\alpha$ should be used. For example, the genes with PPDE>=0.95 make up the list of DE genes with target FDR controlled at 5\%. With more than two biological conditions, there are multiple DE patterns (see Section \ref{sec:multicondmodel}). To obtain a list of genes in a specific DE pattern with target FDR $\alpha$, a user should take the genes with posterior probability of being in that pattern greater than 1 - $\alpha$. Isoform-based lists are obtained in the same way. \newpage \section{Quick Start} \label{sec:quickstart} Before analysis can proceed, the EBSeq package must be loaded into the working space: <<>>= library(EBSeq) @ \subsection{Gene level DE analysis (two conditions)} \label{sec:startgenede} \subsubsection{Required input} \label{sec:startgenedeinput} \begin{flushleft} {\bf Data}: The object \verb+Data+ should be a $G-by-S$ matrix containing the expression values for each gene and each sample, where $G$ is the number of genes and $S$ is the number of samples. These values should exhibit raw counts, without normalization across samples. Counts of this nature may be obtained from RSEM \cite{Li11b}, Cufflinks \cite{Trapnell12}, or a similar approach. \vspace{5 mm} {\bf Conditions}: The object \verb+Conditions+ should be a Factor vector of length $S$ that indicates to which condition each sample belongs. For example, if there are two conditions and three samples in each, $S=6$ and \verb+Conditions+ may be given by \verb+as.factor(c("C1","C1","C1","C2","C2","C2"))+ \end{flushleft} \noindent The object \verb+GeneMat+ is a simulated data matrix containing 1,000 rows of genes and 10 columns of samples. The genes are named \verb+Gene_1, Gene_2 ...+ <<>>= data(GeneMat) str(GeneMat) @ \subsubsection{Library size factor} \label{sec:startgenedesize} As detailed in Section \ref{sec:model}, EBSeq requires the library size factor $l_s$ for each sample $s$. Here, $l_s$ may be obtained via the function \verb+MedianNorm+, which reproduces the median normalization approach in DESeq \citep{Anders10}. <<>>= Sizes=MedianNorm(GeneMat) @ \noindent If quantile normalization is preferred, $l_s$ may be obtained via the function \verb+QuantileNorm+. (e.g. \verb+QuantileNorm(GeneMat,.75)+ for Upper-Quantile Normalization in \cite{Bullard10}) \subsubsection{Running EBSeq on gene expression estimates} \label{sec:startgenederun} The function \verb+EBTest+ is used to detect DE genes. For gene-level data, we don't need to specify the parameter \verb+NgVector+ since there are no differences in $I_g$ structure among the different genes. Here, we simulated the first five samples to be in condition 1 and the other five in condition 2, so define: \verb+Conditions=as.factor(rep(c("C1","C2"),each=5))+ \noindent \verb+sizeFactors+ is used to define the library size factor of each sample. It could be obtained by summing up the total number of reads within each sample, Median Normalization \citep{Anders10}, scaling normalization \citep{Robinson10}, Upper-Quantile Normalization \cite{Bullard10}, or some other such approach. These in hand, we run the EM algorithm, setting the number of iterations to five via \verb+maxround=5+ for demonstration purposes. However, we note that in practice, additional iterations are usually required. Convergence should always be checked (see Section \ref{sec:detailedgenedeconverge} for details). Please note this may take several minutes: <<>>= EBOut=EBTest(Data=GeneMat, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=Sizes, maxround=5) @ \noindent The list of DE genes and the posterior probabilities of being DE are obtained as follows <<>>= EBDERes=GetDEResults(EBOut, FDR=0.05) str(EBDERes$DEfound) head(EBDERes$PPMat) str(EBDERes$Status) @ \noindent \verb+EBDERes$DEfound+ is a list of genes identified with 5\% FDR. EBSeq found 95 genes. The matrix \verb+EBDERes$PPMat+ contains two columns \verb+PPEE+ and \verb+PPDE+, corresponding to the posterior probabilities of being EE or DE for each gene. \verb+EBDERes$Status+ contains each gene's status called by EBSeq. \noindent Note the \verb+GetDEResults()+ was incorporated in EBSeq since version 1.7.1. By using the default settings, the number of genes identified in any given analysis may differ slightly from the previous version. The updated algorithm is more robust to outliers and transcripts with low variance. To obtain results that are comparable to results from earlier versions of EBSeq ($\le$ 1.7.0), a user may set \verb+Method="classic"+ in \verb+GetDEResults()+ function, or use the \verb+GetPPMat()+ function. \subsection{Isoform level DE analysis (two conditions)} \label{sec:startisode} \subsubsection{Required inputs} \label{sec:startisodeinput} \begin{flushleft} {\bf Data}: The object \verb+Data+ should be a $I-by-S$ matrix containing the expression values for each isoform and each sample, where $I$ is the number of isoforms and $S$ is the number of sample. As in the gene-level analysis, these values should exhibit raw data, without normalization across samples. \vspace{5 mm} {\bf Conditions}: The object \verb+Conditions+ should be a vector with length $S$ to indicate the condition of each sample. \vspace{5 mm} {\bf IsoformNames}: The object \verb+IsoformNames+ should be a vector with length $I$ to indicate the isoform names. \vspace{5 mm} {\bf IsosGeneNames}: The object \verb+IsosGeneNames+ should be a vector with length $I$ to indicate the gene name of each isoform. (in the same order as \verb+IsoformNames+.) \end{flushleft} \noindent \verb+IsoList+ contains 1,200 simulated isoforms. In which \verb+IsoList$IsoMat+ is a data matrix containing 1,200 rows of isoforms and 10 columns of samples; \verb+IsoList$IsoNames+ contains the isoform names; \verb+IsoList$IsosGeneNames+ contains the names of the genes the isoforms belong to. <<>>= data(IsoList) str(IsoList) IsoMat=IsoList$IsoMat str(IsoMat) IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames @ \subsubsection{Library size factor} \label{sec:startisodesize} Similar to the gene-level analysis presented above, we may obtain the isoform-level library size factors via \verb+MedianNorm+: <<>>= IsoSizes=MedianNorm(IsoMat) @ \subsubsection{The $I_g$ vector} \label{sec:startisodeNg} While working on isoform level data, EBSeq fits different prior parameters for different uncertainty groups (defined as $I_g$ groups). The default setting to define the uncertainty groups consists of using the number of isoforms the host gene contains ($N_g$) for each isoform. The default settings will provide three uncertainty groups: $I_g=1$ group: Isoforms with $N_g=1$; $I_g=2$ group: Isoforms with $N_g=2$; $I_g=3$ group: Isoforms with $N_g \geq 3$. The $N_g$ and $I_g$ group assignment can be obtained using the function \verb+GetNg+. The required inputs of \verb+GetNg+ are the isoform names (\verb+IsoformNames+) and their corresponding gene names (\verb+IsosGeneNames+). <<>>= NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoNgTrun[c(1:3,201:203,601:603)] @ More details could be found in Section \ref{sec:detailedisode}. \subsubsection{Running EBSeq on isoform expression estimates} \label{sec:startisoderun} The \verb+EBTest+ function is also used to run EBSeq for two condition comparisons on isoform-level data. Below we use 5 iterations to demonstrate. However, as in the gene level analysis, we advise that additional iterations will likely be required in practice (see Section \ref{sec:detailedisodeconverge} for details). <<>>= IsoEBOut=EBTest(Data=IsoMat, NgVector=IsoNgTrun, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=IsoSizes, maxround=5) IsoEBDERes=GetDEResults(IsoEBOut, FDR=0.05) str(IsoEBDERes$DEfound) head(IsoEBDERes$PPMat) str(IsoEBDERes$Status) @ \noindent We see that EBSeq found 104 DE isoforms at the target FDR of 0.05. \noindent Note the \verb+GetDEResults()+ was incorporated in EBSeq since version 1.7.1. By using the default settings, the number of transcripts identified in any given analysis may differ slightly from the previous version. The updated algorithm is more robust to outliers and transcripts with low variance. To obtain results that are comparable to results from earlier versions of EBSeq ($\le$ 1.7.0), a user may set \verb+Method="classic"+ in \verb+GetDEResults()+ function, or use the \verb+GetPPMat()+ function. \subsection{Gene level DE analysis (more than two conditions)} \label{sec:startmulticond} \noindent The object \verb+MultiGeneMat+ is a matrix containing 500 simulated genes with 6 samples: the first two samples are from condition 1; the second and the third sample are from condition 2; the last two samples are from condition 3. <<>>= data(MultiGeneMat) str(MultiGeneMat) @ In analysis where the data are spread over more than two conditions, the set of possible patterns for each gene is more complicated than simply EE and DE. As noted in Section \ref{sec:model}, when we have 3 conditions, there are 5 expression patterns to consider. In the simulated data, we have 6 samples, 2 in each of 3 conditions. The function \verb+GetPatterns+ allows the user to generate all possible patterns given the conditions. For example: <<>>= Conditions=c("C1","C1","C2","C2","C3","C3") PosParti=GetPatterns(Conditions) PosParti @ \noindent where the first row means all three conditions have the same latent mean expression level; the second row means C1 and C2 have the same latent mean expression level but that of C3 is different; and the last row corresponds to the case where the three conditions all have different latent mean expression levels. The user may use all or only some of these possible patterns as an input to \verb+EBMultiTest+. For example, if we were interested in Patterns 1, 2, 4 and 5 only, we'd define: <<>>= Parti=PosParti[-3,] Parti @ Moving on to the analysis, \verb+MedianNorm+ or one of its competitors should be used to determine the normalization factors. Once this is done, the formal test is performed by \verb+EBMultiTest+. <<>>= MultiSize=MedianNorm(MultiGeneMat) MultiOut=EBMultiTest(MultiGeneMat,NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) @ \noindent The posterior probability of being in each pattern for every gene is obtained by using the function \verb+GetMultiPP+: <<>>= MultiPP=GetMultiPP(MultiOut) names(MultiPP) MultiPP$PP[1:10,] MultiPP$MAP[1:10] MultiPP$Patterns @ \noindent where \verb+MultiPP$PP+ provides the posterior probability of being in each pattern for every gene. \verb+MultiPP$MAP+ provides the most likely pattern of each gene based on the posterior probabilities. \verb+MultiPP$Patterns+ provides the details of the patterns. \subsection{Isoform level DE analysis (more than two conditions)} \label{sec:startisomulticond} \noindent Similar to \verb+IsoList+, the object \verb+IsoMultiList+ is an object containing the isoform expression estimates matrix, the isoform names, and the gene names of the isoforms' host genes. \verb+IsoMultiList$IsoMultiMat+ contains 300 simulated isoforms with 8 samples. The first two samples are from condition 1; the second and the third sample are from condition 2; the fifth and sixth sample are from condition 3; the last two samples are from condition 4. Similar to Section \ref{sec:startisode}, the function \verb+MedianNorm+ and \verb+GetNg+ could be used for normalization and calculating the $N_g$'s. <<>>= data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiSize=MedianNorm(IsoMultiMat) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C1","C2","C2","C3","C3","C4","C4") @ Here we have 4 conditions, there are 15 expression patterns to consider. The function \verb+GetPatterns+ allows the user to generate all possible patterns given the conditions. For example: <<>>= PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond @ \noindent If we were interested in Patterns 1, 2, 3, 8 and 15 only, we'd define: <<>>= Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond @ \noindent Moving on to the analysis, \verb+EBMultiTest+ could be used to perform the test: <<>>= IsoMultiOut=EBMultiTest(IsoMultiMat, NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize, maxround=5) @ \noindent The posterior probability of being in each pattern for every gene is obtained by using the function \verb+GetMultiPP+: <<>>= IsoMultiPP=GetMultiPP(IsoMultiOut) names(MultiPP) IsoMultiPP$PP[1:10,] IsoMultiPP$MAP[1:10] IsoMultiPP$Patterns @ \noindent where \verb+MultiPP$PP+ provides the posterior probability of being in each pattern for every gene. \verb+MultiPP$MAP+ provides the most likely pattern of each gene based on the posterior probabilities. \verb+MultiPP$Patterns+ provides the details of the patterns. \newpage \section{More detailed examples} \label{sec:detailed} \subsection{Gene level DE analysis (two conditions)} \label{sec:detailedgenede} \subsubsection{Running EBSeq on simulated gene expression estimates} \label{sec:detailedgenederun} EBSeq is applied as described in Section \ref{sec:startgenederun}. <>= data(GeneMat) Sizes=MedianNorm(GeneMat) EBOut=EBTest(Data=GeneMat, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=Sizes, maxround=5) EBDERes=GetDEResults(EBOut, FDR=0.05) @ <<>>= EBDERes=GetDEResults(EBOut, FDR=0.05) str(EBDERes$DEfound) head(EBDERes$PPMat) str(EBDERes$Status) @ \noindent EBSeq found 95 DE genes at a target FDR of 0.05.\\ \subsubsection{Calculating FC} \label{sec:detailedgenedefc} The function \verb+PostFC+ may be used to calculate the Fold Change (FC) of the raw data as well as the posterior FC of the normalized data. \begin{figure}[h!] \centering <>= GeneFC=PostFC(EBOut) str(GeneFC) PlotPostVsRawFC(EBOut,GeneFC) @ \caption{ FC vs. Posterior FC for 1,000 gene expression estimates} \label{fig:GeneFC} \end{figure} Figure \ref{fig:GeneFC} shows the FC vs. Posterior FC on 1,000 gene expression estimates. The genes are ranked by their cross-condition mean (adjusted by the normalization factors). The posterior FC tends to shrink genes with low expressions (small rank); in this case the differences are minor. \newpage \subsubsection{Checking convergence} \label{sec:detailedgenedeconverge} As detailed in Section \ref{sec:model}, we assume the prior distribution of $q_g^C$ is $Beta(\alpha,\beta)$. The EM algorithm is used to estimate the hyper-parameters $\alpha,\beta$ and the mixture parameter $p$. The optimized parameters at each iteration may be obtained as follows (recall we are using 5 iterations for demonstration purposes): <<>>= EBOut$Alpha EBOut$Beta EBOut$P @ In this case the differences between the 4th and 5th iterations are always less than 0.01. \subsubsection{Checking the model fit and other diagnostics} \label{sec:detailedgenedeplot} As noted in Leng {\it et al.}, 2013 \cite{Leng13}, EBSeq relies on parametric assumptions that should be checked following each analysis. The \verb+QQP+ function may be used to assess prior assumptions. In practice, \verb+QQP+ generates the Q-Q plot of the empirical $q$'s vs. the simulated $q$'s from the Beta prior distribution with estimated hyper-parameters. Figure \ref{fig:GeneQQ} shows that the data points lie on the $y=x$ line for both conditions, which indicates that the Beta prior is appropriate. \begin{figure}[h!] \centering <>= par(mfrow=c(1,2)) QQP(EBOut) @ \caption{QQ-plots for checking the assumption of a Beta prior (upper panels) as well as the model fit using data from condition 1 and condition 2 (lower panels)} \label{fig:GeneQQ} \end{figure} \newpage \noindent Likewise, the \verb+DenNHist+ function may be used to check the density plot of empirical $q$'s vs the simulated $q$'s from the fitted Beta prior distribution. Figure \ref{fig:GeneDenNHist} also shows our estimated distribution fits the data very well. \begin{figure}[h!] \centering <>= par(mfrow=c(1,2)) DenNHist(EBOut) @ \caption{Density plots for checking the model fit using data from condition 1 and condition 2} \label{fig:GeneDenNHist} \end{figure} \newpage \subsection{Isoform level DE analysis (two conditions)} \label{sec:detailedisode} \subsubsection{The $I_g$ vector} \label{sec:detailedisodeNg} Since EBSeq fits rely on $I_g$, we need to obtain the $I_g$ for each isoform. This can be done using the function \verb+GetNg+. The required inputs of \verb+GetNg+ are the isoform names (\verb+IsoformNames+) and their corresponding gene names (\verb+IsosGeneNames+), described above. In the simulated data, we assume that the isoforms in the $I_g=1$ group belong to genes \verb+Gene_1, ... , Gene_200+; The isoforms in the $I_g=2$ group belong to genes \verb+Gene_201, ..., Gene_400+; and isoforms in the $I_g=3$ group belong to \verb+Gene_401, ..., Gene_600+. <>= data(IsoList) IsoMat=IsoList$IsoMat IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames NgList=GetNg(IsoNames, IsosGeneNames, TrunThre=3) @ <<>>= names(NgList) IsoNgTrun=NgList$IsoformNgTrun IsoNgTrun[c(1:3,201:203,601:603)] @ The output of \verb+GetNg+ contains 4 vectors. \verb+GeneNg+ (\verb+IsoformNg+) provides the number of isoforms $N_g$ within each gene (within each isoform's host gene). \verb+GeneNgTrun+ (\verb+IsoformNgTrun+) provides the $I_g$ group assignments. The default number of groups is 3, which means the isoforms with $N_g$ greater than 3 will be assigned to $I_g=3$ group. We use 3 in the case studies since the number of isoforms with $N_g$ larger than 3 is relatively small and the small sample size may induce poor parameter fitting if we treat them as separate groups. In practice, if there is evidence that the $N_g=4,5,6...$ groups should be treated as separate groups, a user can change \verb+TrunThre+ to define a different truncation threshold. \subsubsection{Using mappability ambiguity clusters instead of the $I_g$ vector when the gene-isoform relationship is unknown} \label{sec:detailedisodeNoNg} When working with a de-novo assembled transcriptome, in which case the gene-isoform relationship is unknown, a user can use read mapping ambiguity cluster information instead of Ng, as provided by RSEM \cite{Li11b} in the output file \verb+output_name.ngvec+. The file contains a vector with the same length as the total number of transcripts. Each transcript has been assigned to one of 3 levels (1, 2, or 3) to indicate the mapping uncertainty level of that transcript. The mapping ambiguity clusters are partitioned via a k-means algorithm on the unmapability scores that are provided by RSEM. A user can read in the mapping ambiguity cluster information using: <>= IsoNgTrun = scan(file="output_name.ngvec", what=0, sep="\n") @\\ Where \verb+"output_name.ngvec"+ is the output file obtained from RSEM function rsem-generate-ngvector. More details on using the RSEM-EBSeq pipeline on de novo assembled transcriptomes can be found at \url{http://deweylab.biostat.wisc.edu/rsem/README.html#de}. Other unmappability scores and other cluster methods (e.g. Gaussian Mixed Model) could also be used to form the uncertainty clusters. \subsubsection{Running EBSeq on simulated isoform expression estimates} \label{sec:detailedisoderun} EBSeq can be applied as described in Section \ref{sec:startisoderun}. <>= IsoSizes=MedianNorm(IsoMat) IsoEBOut=EBTest(Data=IsoMat, NgVector=IsoNgTrun, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=IsoSizes, maxround=5) IsoEBDERes=GetDEResults(IsoEBOut, FDR=0.05) @ <<>>= str(IsoEBDERes) @ \noindent We see that EBSeq found 104 DE isoforms at a target FDR of 0.05. The function \verb+PostFC+ could also be used here to calculate the Fold Change (FC) as well as the posterior FC on the normalization factor adjusted data. <<>>= IsoFC=PostFC(IsoEBOut) str(IsoFC) @ \subsubsection{Checking convergence} \label{sec:detailedisodeconverge} For isoform level data, we assume the prior distribution of $q_{gi}^C$ is $Beta(\alpha,\beta^{I_g})$. As in Section \ref{sec:detailedgenedeconverge}, the optimized parameters at each iteration may be obtained as follows (recall we are using 5 iterations for demonstration purposes): <<>>= IsoEBOut$Alpha IsoEBOut$Beta IsoEBOut$P @ Here we have 3 $\beta$'s in each iteration corresponding to $\beta^{I_g=1},\beta^{I_g=2},\beta^{I_g=3}$. We see that parameters are changing less than $10^{-2}$ or $10^{-3}$. In practice, we require changes less than $10^{-3}$ to declare convergence. \subsubsection{Checking the model fit and other diagnostics} \label{sec:detailedisodeplot} In Leng {\it et al.}, 2013\citep{Leng13}, we showed the mean-variance differences across different isoform groups on multiple data sets. In practice, if it is of interest to check differences among isoform groups defined by truncated $I_g$ (such as those shown here in Figure 1), the function \verb+PolyFitPlot+ may be used. The following code generates the three panels shown in Figure \ref{fig:IsoSimuNgEach} (if condition 2 is of interest, a user could change each \verb+C1+ to \verb+C2+.): \begin{figure}[h!] \centering <>= par(mfrow=c(2,2)) PolyFitValue=vector("list",3) for(i in 1:3) PolyFitValue[[i]]=PolyFitPlot(IsoEBOut$C1Mean[[i]], IsoEBOut$C1EstVar[[i]],5) @ \caption{ The mean-variance fitting plot for each Ng group} \label{fig:IsoSimuNgEach} \end{figure} \newpage Superimposing all $I_g$ groups using the code below will generate the figure (shown here in Figure \ref{fig:IsoSimuNg}), which is similar in structure to Figure 1: \begin{figure}[h!] \centering <>= PolyAll=PolyFitPlot(unlist(IsoEBOut$C1Mean), unlist(IsoEBOut$C1EstVar),5) lines(log10(IsoEBOut$C1Mean[[1]][PolyFitValue[[1]]$sort]), PolyFitValue[[1]]$fit[PolyFitValue[[1]]$sort],col="yellow",lwd=2) lines(log10(IsoEBOut$C1Mean[[2]][PolyFitValue[[2]]$sort]), PolyFitValue[[2]]$fit[PolyFitValue[[2]]$sort],col="pink",lwd=2) lines(log10(IsoEBOut$C1Mean[[3]][PolyFitValue[[3]]$sort]), PolyFitValue[[3]]$fit[PolyFitValue[[3]]$sort],col="green",lwd=2) legend("topleft",c("All Isoforms","Ng = 1","Ng = 2","Ng = 3"), col=c("red","yellow","pink","green"),lty=1,lwd=3,box.lwd=2) @ \caption{The mean-variance plot for each Ng group} \label{fig:IsoSimuNg} \end{figure} \newpage \noindent To generate a QQ-plot of the fitted Beta prior distribution and the $\hat{q}^C$'s within condition, a user may use the following code to generate 6 panels (as shown in Figure \ref{fig:IsoQQ}). \begin{figure}[h!] \centering <>= par(mfrow=c(2,3)) QQP(IsoEBOut) @ \caption{ QQ-plots of the fitted prior distributions within each condition and each Ig group} \label{fig:IsoQQ} \end{figure} \newpage \noindent And in order to produce the plot of the fitted Beta prior densities and the histograms of $\hat{q}^C$'s within each condition, the following may be used (it generates Figure \ref{fig:IsoDenNHist}): \begin{figure}[h] \centering <>= par(mfrow=c(2,3)) DenNHist(IsoEBOut) @ \caption{ Prior distribution fit within each condition and each Ig group. (Note only a small set of isoforms are considered here for demonstration. Better fitting should be expected while using full set of isoforms.)} \label{fig:IsoDenNHist} \end{figure} \clearpage \subsection{Gene level DE analysis (more than two conditions)} \label{sec:detailedmulticond} As described in Section \ref{sec:startmulticond}, the function \verb+GetPatterns+ allows the user to generate all possible patterns given the conditions. To visualize the patterns, the function \verb+PlotPattern+ may be used. \begin{figure}[h!] \centering <>= Conditions=c("C1","C1","C2","C2","C3","C3") PosParti=GetPatterns(Conditions) PosParti PlotPattern(PosParti) @ \caption{ All possible patterns} \label{fig:Patterns} \end{figure} \newpage \noindent If we were interested in Patterns 1, 2, 4 and 5 only, we'd define: <<>>= Parti=PosParti[-3,] Parti @ \noindent Moving on to the analysis, \verb+MedianNorm+ or one of its competitors should be used to determine the normalization factors. Once this is done, the formal test is performed by \verb+EBMultiTest+. <>= data(MultiGeneMat) MultiSize=MedianNorm(MultiGeneMat) MultiOut=EBMultiTest(MultiGeneMat, NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) @ \noindent The posterior probability of being in each pattern for every gene is obtained using the function \verb+GetMultiPP+: <<>>= MultiPP=GetMultiPP(MultiOut) names(MultiPP) MultiPP$PP[1:10,] MultiPP$MAP[1:10] MultiPP$Patterns @ \noindent where \verb+MultiPP$PP+ provides the posterior probability of being in each pattern for every gene. \verb+MultiPP$MAP+ provides the most likely pattern of each gene based on the posterior probabilities. \verb+MultiPP$Patterns+ provides the details of the patterns. The FC and posterior FC for multiple condition data can be obtained by the function \verb+GetMultiFC+: <<>>= MultiFC=GetMultiFC(MultiOut) str(MultiFC) @ \noindent To generate a QQ-plot of the fitted Beta prior distribution and the $\hat{q}^C$'s within condition, a user could also use function \verb+DenNHist+ and \verb+QQP+. \begin{figure}[h!] \centering <>= par(mfrow=c(2,2)) QQP(MultiOut) @ \caption{ QQ-plots of the fitted prior distributions within each condition and each Ig group} \label{fig:GeneMultiQQ} \end{figure} \begin{figure}[h] \centering <>= par(mfrow=c(2,2)) DenNHist(MultiOut) @ \caption{ Prior distributions fit within each condition. (Note only a small set of genes are considered here for demonstration. Better fitting should be expected while using full set of genes.)} \label{fig:GeneMultiDenNHist} \end{figure} \newpage \clearpage \newpage \subsection{Isoform level DE analysis (more than two conditions)} \label{sec:detailedisomulticond} Similar to Section \ref{sec:startmulticond}, the function \verb+GetPatterns+ allows a user to generate all possible patterns given the conditions. To visualize the patterns, the function \verb+PlotPattern+ may be used. <<>>= Conditions=c("C1","C1","C2","C2","C3","C3","C4","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond @ \newpage \begin{figure}[h!] \centering <>= PlotPattern(PosParti.4Cond) Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond @ \caption{All possible patterns for 4 conditions} \label{fig:Patterns4Cond} \end{figure} \newpage <>= data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiSize=MedianNorm(IsoMultiMat) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun IsoMultiOut=EBMultiTest(IsoMultiMat,NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize, maxround=5) IsoMultiPP=GetMultiPP(IsoMultiOut) @ <<>>= names(MultiPP) IsoMultiPP$PP[1:10,] IsoMultiPP$MAP[1:10] IsoMultiPP$Patterns IsoMultiFC=GetMultiFC(IsoMultiOut) @ The FC and posterior FC for multiple condition data can be obtained by the function \verb+GetMultiFC+: \noindent To generate a QQ-plot of the fitted Beta prior distribution and the $\hat{q}^C$'s within condition, a user could also use the functions \verb+DenNHist+ and \verb+QQP+. \newpage \begin{figure}[h!] \centering <>= par(mfrow=c(3,4)) QQP(IsoMultiOut) @ \caption{ QQ-plots of the fitted prior distributions within each condition and Ig group. (Note only a small set of isoforms are considered here for demonstration. Better fitting should be expected while using full set of isoforms.)} \label{fig:IsoMultiQQ} \end{figure} \begin{figure}[h] \centering <>= par(mfrow=c(3,4)) DenNHist(IsoMultiOut) @ \caption{ Prior distributions fit within each condition and Ig group. (Note only a small set of isoforms are considered here for demonstration. Better fitting should be expected while using full set of isoforms.)} \label{fig:IsoMultiDenNHist} \end{figure} \clearpage \newpage \newpage \subsection{Working without replicates} When replicates are not available, it is difficult to estimate the transcript specific variance. In this case, EBSeq estimates the variance by pooling similar genes together. Specifically, we take genes with FC in the 25\% - 75\% quantile of all FC's as candidate genes. By defining \verb+NumBin = 1000+ (default in \verb+EBTest+), EBSeq will group genes with similar means into 1,000 bins. For each candidate gene, we use the across-condition variance estimate as its variance estimate. For each bin, the bin-wise variance estimation is taken to be the median of the across-condition variance estimates of the candidate genes within that bin. For each non-candidate gene, we use the bin-wise variance estimate of the host bin (the bin containing this gene) as its variance estimate. This approach works well when there are no more than 50\% DE genes in the data set. \subsubsection{Gene counts with two conditions} \label{sec:norepgenede} To generate a data set with no replicates, we take the first sample of each condition. For example, using the data from Section \ref{sec:detailedgenede}, we take sample 1 from condition 1 and sample 6 from condition 2. Functions \verb+MedianNorm+, \verb+GetDEResults+ and \verb+PostFC+ may be used on data without replicates. <<>>= data(GeneMat) GeneMat.norep=GeneMat[,c(1,6)] Sizes.norep=MedianNorm(GeneMat.norep) EBOut.norep=EBTest(Data=GeneMat.norep, Conditions=as.factor(rep(c("C1","C2"))), sizeFactors=Sizes.norep, maxround=5) EBDERes.norep=GetDEResults(EBOut.norep) GeneFC.norep=PostFC(EBOut.norep) @ \subsubsection{Isoform counts with two conditions} \label{norepisode} To generate an isoform level data set with no replicates, we also take sample 1 and sample 6 in the data we used in Section \ref{sec:detailedisode}. Example codes are shown below. <<>>= data(IsoList) IsoMat=IsoList$IsoMat IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoMat.norep=IsoMat[,c(1,6)] IsoSizes.norep=MedianNorm(IsoMat.norep) IsoEBOut.norep=EBTest(Data=IsoMat.norep, NgVector=IsoNgTrun, Conditions=as.factor(c("C1","C2")), sizeFactors=IsoSizes.norep, maxround=5) IsoEBDERes.norep=GetDEResults(IsoEBOut.norep) IsoFC.norep=PostFC(IsoEBOut.norep) @ \subsubsection{Gene counts with more than two conditions} \label{norepisode} To generate a data set with multiple conditions and no replicates, we take the first sample from each condition (sample 1, 3 and 5) in the data we used in Section \ref{sec:detailedmulticond}. Example codes are shown below. <<>>= data(MultiGeneMat) MultiGeneMat.norep=MultiGeneMat[,c(1,3,5)] Conditions=c("C1","C2","C3") PosParti=GetPatterns(Conditions) Parti=PosParti[-3,] MultiSize.norep=MedianNorm(MultiGeneMat.norep) MultiOut.norep=EBMultiTest(MultiGeneMat.norep, NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize.norep, maxround=5) MultiPP.norep=GetMultiPP(MultiOut.norep) MultiFC.norep=GetMultiFC(MultiOut.norep) @ \subsubsection{Isoform counts with more than two conditions} \label{sec:norepmulticond} To generate an isoform level data set with multiple conditions and no replicates, we take the first sample from each condition (sample 1, 3, 5 and 7) in the data we used in Section \ref{sec:detailedisomulticond}. Example codes are shown below. <<>>= data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiMat.norep=IsoMultiMat[,c(1,3,5,7)] IsoMultiSize.norep=MedianNorm(IsoMultiMat.norep) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C2","C3","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond IsoMultiOut.norep=EBMultiTest(IsoMultiMat.norep, NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize.norep, maxround=5) IsoMultiPP.norep=GetMultiPP(IsoMultiOut.norep) IsoMultiFC.norep=GetMultiFC(IsoMultiOut.norep) @ \section{EBSeq pipelines and extensions} \subsection{RSEM-EBSeq pipeline: from raw reads to differential expression analysis results} EBSeq is coupled with RSEM \cite{Li11b} as an RSEM-EBSeq pipeline which provides quantification and DE testing on both gene and isoform levels. For more details, see \url{http://deweylab.biostat.wisc.edu/rsem/README.html#de} \subsection{EBSeq interface: A user-friendly graphical interface for differetial expression analysis} EBSeq interface provides a graphical interface implementation for users who are not familiar with the R programming language. It takes .xls, .xlsx and .csv files as input. Additional packages need be downloaded; they may be found at \url{http://www.biostat.wisc.edu/~ningleng/EBSeq_Package/EBSeq_Interface/} \subsection{EBSeq Galaxy tool shed} EBSeq tool shed contains EBSeq wrappers for a local Galaxy implementation. For more details, see \url{http://www.biostat.wisc.edu/~ningleng/EBSeq_Package/EBSeq_Galaxy_toolshed/} \section{Acknowledgment} We would like to thank Haolin Xu for checking the package and proofreading the vignette. \section{News} 2014-1-30: In EBSeq 1.3.3, the default setting of EBTest function will remove low expressed genes (genes whose 75th quantile of normalized counts is less than 10) before identifying DE genes. These two thresholds can be changed in EBTest function. Because low expressed genes are disproportionately noisy, removing these genes prior to downstream analyses can improve model fitting and increase robustness (e.g. by removing outliers). 2014-5-22: In EBSeq 1.5.2, numerical approximations are implemented to deal with underflow. The underflow is likely due to large number of samples. 2015-1-29: In EBSeq 1.7.1, EBSeq incorporates a new function GetDEResults() which may be used to obtain a list of transcripts under a target FDR in a two-condition experiment. The results obtained by applying this function with its default setting will be more robust to transcripts with low variance and potential outliers. By using the default settings in this function, the number of genes identified in any given analysis may differ slightly from the previous version (1.7.0 or order). To obtain results that are comparable to results from earlier versions of EBSeq (1.7.0 or older), a user may set Method="classic" in GetDEResults() function, or use the original GetPPMat() function. The GeneDEResults() function also allows a user to modify thresholds to target genes/isoforms with a pre-specified posterior fold change. Also, in EBSeq 1.7.1, the default settings in EBTest() and EBMultiTest() function will only remove transcripts with all 0's (instead of removing transcripts with 75th quantile less than 10 in version 1.3.3-1.7.0). To obtain a list of transcripts comparable to the results generated by EBSeq version 1.3.3-1.7.0, a user may change Qtrm = 0.75 and QtrmCut = 10 when applying EBTest() or EBMultiTest() function. \section{Common Q and A} \subsection{Read in data} csv file: \verb+In=read.csv("FileName", stringsAsFactors=F, row.names=1, header=T)+ \verb+Data=data.matrix(In)+ \noindent txt file: \verb+In=read.table("FileName", stringsAsFactors=F, row.names=1, header=T)+ \verb+Data=data.matrix(In)+ \noindent Check \verb+str(Data)+ and make sure it is a matrix instead of data frame. You may need to play around with the \verb+row.names+ and \verb+header+ option depends on how the input file was generated. \subsection{GetDEResults() function not found} You may on an earlier version of EBSeq. The GetDEResults function was introduced since version 1.7.1. The latest release version could be found at: \url{http://www.bioconductor.org/packages/release/bioc/html/EBSeq.html} \noindent The latest devel version: \url{http://www.bioconductor.org/packages/devel/bioc/html/EBSeq.html} \noindent And you may check your package version by typing \verb+packageVersion("EBSeq")+. \subsection{Visualizing DE genes/isoforms} To generate a heatmap, you may consider the heatmap.2 function in gplots package. For example, you may run \verb+heatmap.2(NormalizedMatrix[GenesOfInterest,], scale="row", trace="none", Colv=F)+ The normalized matrix may be obtained from \verb+GetNormalizedMat()+ function. \subsection{My favorite gene/isoform has NA in PP (status "NoTest")} \indent The NoTest status comes from two sources: 1) In version 1.3.3-1.7.0, using the default parameter settings of EBMultiTest(), the function will not test on genes with more than 75\% values $\le$ 10 to ensure better model fitting. To disable this filter, you may set Qtrm=1 and QtrmCut=0. 2) numerical over/underflow in R. That happens when the within condition variance is extremely large or small. we did implemented a numerical approximation step to calculate the approximated PP for these genes with over/underflow. Here we use $10^{-10}$ to approximate the parameter p in the NB distribution for these genes (we set it to a small value since we want to cover more over/underflow genes with low within-condition variation). You may try to tune this value (to a larger value) in the approximation by setting \verb+ApproxVal+ in \verb+EBTest()+ or \verb+EBMultiTest()+ function. \pagebreak \bibliographystyle{plain} \bibliography{lengetal} \end{document} EBSeq/inst/doc/EBSeq_Vignette.pdf0000644000175100017510000347100212607342353017566 0ustar00biocbuildbiocbuild%PDF-1.5 % 150 0 obj << /Length 1608 /Filter /FlateDecode >> stream xZKsH+tV%ل6xO[]q`9cƖ(N dCAy}uO<=y$s5Q8*R Yao'w|^bR?!AI9<>Y8R?v>% -'\S>a kMnYXZs9=h_E2DDIQH3:| ӢCloYb$԰V!,D % WHnƃ%Zupc?hx (%K_sȏ{c 恂KS&?+ [=!Qv!8`-tnyA[ܚWڏD} 14Bq` ZF <{䬧=(վ~TM5^R@/ huNc* rڠ^&FaMM%9fljlsCrAf{5Fw:~1jzUar cra]&l^PVzLhFضz+!k!ۂIf%LQlco #eT ryцQ,<(o艴2\)W )7WD_{_T9pQS&xzGMgV*\m}j B>gɒIP|:r隺I~~#o,=l~Qi@y^Wܲh,|db?@vn ?2 F=މWl:YB>l$%"1by~= ۳0 juxnh]@8n8u:وbM*i"%y Pv@C:j09 A6۷JDg]|S%W$]8r]n +{QRonN d(m`Okm{IcXOHWʛ,ބOsRr_N! x)*OrRCimJ=6ulw]wvv_ >6JÍ$XkUU7[ϕ* .BG7}Z6o> stream x[[۶~ϯ􉚱I>uxRg'}H]M$q+J@vmq"C\;/ͪ*bv"Zʔ$O: 2{l~ ÿiOYrh~+/*tUu'W]$OLB%fei *CĶH! 3wIϳ292OX0yHre_Lr o'X.q7>52l"g͇=Nr,^^N]&y:,fns~~BP x׶ ”l_c@;¾le=Ul}k ˀ1LؼAfw#.eBx3Rr(-EHȅe_r~@V8 .+`uAn |&YkὩW^G IWdJ q)쩷':8W;6.PK rntu;GWӬmdǚI@ڼH€pi'{y&UA* ki?4Gy.uZ**r%|3ӥ`唟B?KǗU(ZItz )JO".ԟQS. ]嚐o 'QHj|hѮ 0/'n+&/7w.'=<~ϏbK$_eEs p[Sn? 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IJډqAJCzˇRqer!J%S0Ja˾'o>9&_rup]|y( 4,K=XE^DA@ή|.ȭ"`pR=R*N?,:o— h6AYǏ`GNuNxauyVvcp=@k$,pz|#dVvFvokHxHp E,hGAe3ǃB_>;2B u#H#AC&5,!nwRbNW]r@1VEӓ>% ^={zgIZHCg*cZ^˷ >z^?N&*/#}U/˥-M luuy>ϦѾ ''/¢;?~ePr*nw. ܓ|./Krtr>2&H=zUyM䰘̏`K}} OO*{H~P5'94#1cm nA1wテ;I4Nj|D]r߯)MRk {jU^^}He|#BֶRf+HEb07koyx}8^ϭ]E[+ŧk^%K9>B -Rۧy&ue|gO-WI$n#HćrEF,S-uddiȲE/kʽ-e +dSLe=a /+RNFߥ"# ^ 3ou2JFV׮/?ˇ{yU κ礝gx)fxȂ{2{掸X(6闫ńyٻMaغM.AE8nN#U-]{1ɟ\]&MVa䲞|TʯX}TjUN[9nsXT|ZEjM R&;=JS} {, 8rԖ1kcyt[- qűᖉ{ɞe- `̬:lqn6{j|w{؇-{~HqMͧw V[Ί{ں- nvϴ5n ܶۺ=ljvp[ ϴ[6fܺ&5 n=nQtp{66S endstream endobj 621 0 obj << /Author()/Title()/Subject()/Creator(LaTeX with hyperref package)/Producer(pdfTeX-1.40.14)/Keywords() /CreationDate (D:20151013225850-04'00') /ModDate (D:20151013225850-04'00') /Trapped /False /PTEX.Fullbanner (This is pdfTeX, Version 3.1415926-2.5-1.40.14 (TeX Live 2013/Debian) kpathsea version 6.1.1) >> endobj 593 0 obj << /Type /ObjStm /N 31 /First 262 /Length 1252 /Filter /FlateDecode >> stream xڝXK6WHǐtTU h}g(K7o>(YB Pz #ZjV5NxTl % x2@Zl(` %#B)N(lB!8By%WRh@4]h*0BEyK`g(2*".#A V߯nӶ'-0^}şN=!ԗHYDڐBI,Mp8RuCgJVT T2YľHb/|Ib>wͧN`x8wp v2]ۀ&4wb~? ~`5|N3Ny=rcswwT &tml6*ZeHFfuouI?ǶIJ7DĈA7]Ȝ+“SMJPReCI"5Ia3đ:NYILmyB(@}@DB{@%*rp"2"9 492M0"ܵR@EQH=a2FI޾ލ/^m^J6[x?(hlJu̙CbmSSBٰ )?Ӿ9yȵV)A (Z_s_8?:JNI]~yl ] /Length 1520 /Filter /FlateDecode >> stream x%KlUU:}IҖSZZR^Z(u -UtdB111C#쐘8 4Î15&F &aֿw={ qa`AMoWAhD4m@)aHu:izH 6v4 rh.VJ U*Pvm4j΢-ւ: hK""i#(,by&\\@ZΑnmhhgIAet،vmlA[@;CΣ&[fIcЋn}hkhHvu`4'Hw!RIjpt'c`8C`LG Я5~zqHKU TUM%VQ S[봎e"Uc*sD,J*=~#J(q?*,^Iz<ͫ2.z(ȫg|ss6?U JoתX>1.,-͠ւ?go`t 5D|+?V Z,,:wo[mﱰko)ljºz=`W , T?|YqIc[E! {_\1ǢCOVw=>-s5ix׳xX8yYcQ`ᱯbl3(]>, } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \keyword{ package } \seealso{ EBTest, EBMultiTest } \examples{ data(GeneMat) GeneMat.small = GeneMat[c(1:10,511:550),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data=GeneMat.small, Conditions=as.factor(rep(c("C1","C2"), each=5)), sizeFactors=Sizes, maxround=5) } EBSeq/man/EBTest.Rd0000644000175100017510000001431012607264551014730 0ustar00biocbuildbiocbuild\name{EBTest} \alias{EBTest} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Using EM algorithm to calculate the posterior probabilities of being DE } \description{ Base on the assumption of NB-Beta Empirical Bayes model, the EM algorithm is used to get the posterior probability of being DE. } \usage{ EBTest(Data, NgVector = NULL, Conditions, sizeFactors, maxround, Pool = F, NumBin = 1000, ApproxVal = 10^-10, Alpha = NULL, Beta = NULL, PInput = NULL, RInput = NULL, PoolLower = .25, PoolUpper = .75, Print = T, Qtrm = 1,QtrmCut=0) } \arguments{ \item{Data}{A data matrix contains expression values for each transcript (gene or isoform level). In which rows should be transcripts and columns should be samples.} \item{NgVector}{A vector indicates the uncertainty group assignment of each isoform. e.g. if we use number of isoforms in the host gene to define the uncertainty groups, suppose the isoform is in a gene with 2 isoforms, Ng of this isoform should be 2. The length of this vector should be the same as the number of rows in Data. If it's gene level data, Ngvector could be left as NULL.} \item{Conditions}{A factor indicates the condition which each sample belongs to. } \item{sizeFactors}{The normalization factors. It should be a vector with lane specific numbers (the length of the vector should be the same as the number of samples, with the same order as the columns of Data).} \item{maxround}{Number of iterations. The default value is 5. Users should always check the convergency by looking at the Alpha and Beta in output. If the hyper-parameter estimations are not converged in 5 iterations, larger number is suggested.} \item{Pool}{While working without replicates, user could define the Pool = TRUE in the EBTest function to enable pooling.} \item{NumBin}{By defining NumBin = 1000, EBSeq will group the genes with similar means together into 1,000 bins.} \item{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 FC's 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. } \item{ApproxVal}{The variances of the transcripts with mean < var will be approximated as mean/(1-ApproxVal). } \item{Alpha, Beta, PInput, RInput}{If the parameters are known and the user doesn't want to estimate them from the data, user could specify them here.} \item{Print}{Whether print the elapsed-time while running the test.} \item{Qtrm, QtrmCut}{ Transcripts with Qtrm th quantile < = QtrmCut will be removed before testing. The default value is Qtrm = 1 and QtrmCut=0. By default setting, transcripts with all 0's won't be tested. } } \details{For each transcript gi within condition, the model assumes: X_{gis}|mu_{gi} ~ NB (r_{gi0} * l_s, q_{gi}) q_gi|alpha, beta^N_g ~ Beta (alpha, beta^N_g) In which the l_s is the sizeFactors of samples. The function will test "H0: q_{gi}^{C1} = q_{gi}^{C2}" and "H1: q_{gi}^{C1} != q_{gi}^{C2}." } \value{ \item{Alpha}{Fitted parameter alpha of the prior beta distribution. Rows are the values for each iteration.} \item{Beta}{Fitted parameter beta of the prior beta distribution. Rows are the values for each iteration.} \item{P, PFromZ}{The bayes estimator of being DE. Rows are the values for each iteration.} \item{Z, PoissonZ}{The Posterior Probability of being DE for each transcript(Maybe not in the same order of input). } \item{RList}{The fitted values of r for each transcript.} \item{MeanList}{The mean of each transcript (across conditions).} \item{VarList}{The variance of each transcript (across conditions).} \item{QListi1}{The fitted q values of each transcript within condition 1.} \item{QListi2}{The fitted q values of each transcript within condition 2.} \item{C1Mean}{The mean of each transcript within Condition 1 (adjusted by normalization factors).} \item{C2Mean}{The mean of each transcript within Condition 2 (adjusted by normalization factors).} \item{C1EstVar}{The estimated variance of each transcript within Condition 1 (adjusted by normalization factors).} \item{C2EstVar}{The estimated variance of each transcript within Condition 2 (adjusted by normalization factors).} \item{PoolVar}{The variance of each transcript (The pooled value of within condition EstVar).} \item{DataList}{A List of data that grouped with Ng.} \item{PPDE}{The Posterior Probability of being DE for each transcript (The same order of input).} \item{f0,f1}{The likelihood of the prior predictive distribution of being EE or DE (in log scale).} \item{AllZeroIndex}{The transcript with expression 0 for all samples (which are not tested).} \item{PPMat}{A matrix contains posterior probabilities of being EE (the first column) or DE (the second column). Rows are transcripts. Transcripts with expression 0 for all samples are not shown in this matrix.} \item{PPMatWith0}{A matrix contains posterior probabilities of being EE (the first column) or DE (the second column). Rows are transcripts. Transcripts with expression 0 for all samples are shown as PP(EE) = PP(DE) = NA in this matrix. The transcript order is exactly the same as the order of the input data.} \item{ConditionOrder}{The condition assignment for C1Mean, C2Mean, etc.} \item{Conditions}{The input conditions.} \item{DataNorm}{Normalized expression matrix.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ EBMultiTest, PostFC, GetPPMat } \examples{ data(GeneMat) str(GeneMat) GeneMat.small = GeneMat[c(1:10,511:550),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each = 5)), sizeFactors = Sizes, maxround = 5) PP = GetPPMat(EBOut) } \keyword{ DE } \keyword{ Two condition }% __ONLY ONE__ keyword per line EBSeq/man/GeneMat.Rd0000644000175100017510000000107312607264551015124 0ustar00biocbuildbiocbuild\name{GeneMat} \alias{GeneMat} \docType{data} \title{ The simulated data for two condition gene DE analysis } \description{ 'GeneMat' gives the simulated data for two condition gene DE analysis. } \usage{data(GeneMat)} \source{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \seealso{ IsoList } \examples{ data(GeneMat) } \keyword{datasets} EBSeq/man/GetDEResults.Rd0000644000175100017510000001165112607264551016121 0ustar00biocbuildbiocbuild\name{GetDEResults} \alias{GetDEResults} \title{ Obtain Differential Expression Analysis Results in a Two-condition Test } \description{ Obtain DE analysis results in a two-condition test using the output of EBTest() } \usage{ GetDEResults(EBPrelim, FDR=0.05, Method="robust", FDRMethod="hard", Threshold_FC=0.7, Threshold_FCRatio=0.3, SmallNum=0.01) } \arguments{ \item{EBPrelim}{Output from the function EBTest().} \item{FDR}{Target FDR, defaut is 0.05.} \item{FDRMethod}{"hard" or "soft". Giving a target FDR alpha, either hard threshold and soft threshold may be used. If the hard threshold is preferred, DE transcripts are defined as the the transcripts with PP(DE) greater than (1-alpha). Using the hard threshold, any DE transcript in the list has FDR <= alpha. If the soft threshold is preferred, the DE transcripts are defined as the transcripts with PP(DE) greater than crit_fun(PPEE, alpha). Using the soft threshold, the list of DE transcripts has average FDR alpha. Based on results from our simulation studies, hard thresholds provide a better-controlled empirical FDR when sample size is relatively small(Less than 10 samples in each condition). User may consider the soft threshold when sample size is large to improve power.} \item{Method}{"robust" or "classic". Using the "robust" option, EBSeq is more robust to genes with outliers and genes with extremely small variances. Using the "classic" option, the results will be more comparable to those obtained by using the GetPPMat() function from earlier version (<= 1.7.0) of EBSeq. Default is "robust".} \item{Threshold_FC}{Threshold for the fold change (FC) statistics. The default is 0.7. The FC statistics are calculated as follows. First the posterior FC estimates are calculated using PostFC() function. The FC statistics is defined as exp(-|log posterior FC|) and therefore is always less than or equal to 1. The default threshold was selected as the optimal threshold learned from our simulation studies. By setting the threshold as 0.7, the expected FC for a DE transcript is less than 0.7 (or greater than 1/0.7=1.4). User may specify their own threshold here. A higher (less conservative) threshold may be used here when sample size is large. Our simulation results indicated that when there are more than or equal to 5 samples in each condition, a less conservative threshold will improve the power when the FDR is still well-controlled. The parameter will be ignored if Method is set as "classic".} \item{Threshold_FCRatio}{Threshold for the fold change ratio (FCRatio) statistics. The default is 0.3. The FCRatio statistics are calculated as follows. First we get another revised fold change statistic called Median-FC statistic for each transcript. For each transcript, we calculate the median of normalized expression values within each condition. The MedianFC is defined as exp(-|log((C1Median+SmallNum)/(C2Median+SmallNum))|). Note a small number is added to avoid Inf and NA. See SmallNum for more details. The FCRatio is calculated as exp(-|log(FCstatistics/MedianFC)|). Therefore it is always less than or equal to 1. The default threshold was selected as the optimal threshold learned from our simulation studies. By setting the threshold as 0.3, the FCRatio for a DE transcript is expected to be larger than 0.3. } \item{SmallNum}{When calculating the FCRatio (or Median-FC), a small number is added for each transcript in each condition to avoid Inf and NA. Default is 0.01.} } \details{ GetDEResults() function takes output from EBTest() function and output a list of DE transcripts under a target FDR. It also provides posterior probability estimates for each transcript. } \value{ \item{DEfound}{A list of DE transcripts.} \item{PPMat}{Posterior probability matrix. Transcripts are following the same order as in the input matrix. Transcripts that were filtered by magnitude (in EBTest function), FC, or FCR are assigned with NA for both PPDE and PPEE.} \item{Status}{Each transcript will be assigned with one of the following values: "DE", "EE", "Filtered: Low Expression", "Filtered: Fold Change" and "Filtered: Fold Change Ratio". Transcripts are following the same order as in the input matrix.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng, Yuan Li } \seealso{ EBTest } \examples{ data(GeneMat) str(GeneMat) GeneMat.small = GeneMat[c(1:10,511:550),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each = 5)), sizeFactors = Sizes, maxround = 5) Out = GetDEResults(EBOut) } \keyword{ DE } \keyword{ Two condition } EBSeq/man/GetMultiFC.Rd0000644000175100017510000000400712607264551015547 0ustar00biocbuildbiocbuild\name{GetMultiFC} \alias{GetMultiFC} \title{ Calculate the Fold Changes for Multiple Conditions } \description{ 'GetMultiFC' calculates the Fold Changes for each pair of conditions in a multiple condition study.} \usage{ GetMultiFC(EBMultiOut, SmallNum = 0.01) } \arguments{ \item{EBMultiOut}{The output of EBMultiTest function.} \item{SmallNum}{A small number will be added for each transcript in each condition to avoid Inf and NA. Default is 0.01.} } \details{ Provide the FC (adjusted by the normalization factors) for each pair of comparisons. A small number will be added for each transcript in each condition to avoid Inf and NA. Default is set to be 0.01. } \value{ \item{FCMat}{The FC of each pair of comparison (adjusted by the normalization factors).} \item{Log2FCMat}{The log 2 FC of each pair of comparison (adjusted by the normalization factors).} \item{PostFCMat}{The posterior FC of each pair of comparison.} \item{Log2PostFCMat}{The log 2 posterior FC of each pair of comparison.} \item{CondMean}{The mean of each transcript within each condition (adjusted by the normalization factors).} \item{ConditionOrder}{The condition assignment for C1Mean, C2Mean, etc.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ EBMultiTest, PostFC } \examples{ data(MultiGeneMat) MultiGeneMat.small = MultiGeneMat[201:210,] Conditions = c("C1","C1","C2","C2","C3","C3") PosParti = GetPatterns(Conditions) Parti = PosParti[-3,] MultiSize = MedianNorm(MultiGeneMat.small) MultiOut = EBMultiTest(MultiGeneMat.small, NgVector=NULL, Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) MultiFC = GetMultiFC(MultiOut) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ Posterior Probability } EBSeq/man/GetMultiPP.Rd0000644000175100017510000000240012607264551015571 0ustar00biocbuildbiocbuild\name{GetMultiPP} \alias{GetMultiPP} \title{ Posterior Probability of Each Transcript } \description{ 'GetMultiPP' generates the Posterior Probability of being each pattern of each transcript based on the EBMultiTest output. } \usage{ GetMultiPP(EBout) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{EBout}{The output of EBMultiTest function.} } \value{ \item{PP}{The poster probabilities of being each pattern.} \item{MAP}{Gives the most likely pattern.} \item{Patterns}{The Patterns.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{GetPPMat} \examples{ data(MultiGeneMat) MultiGeneMat.small = MultiGeneMat[201:210,] Conditions = c("C1","C1","C2","C2","C3","C3") PosParti = GetPatterns(Conditions) Parti = PosParti[-3,] MultiSize = MedianNorm(MultiGeneMat.small) MultiOut = EBMultiTest(MultiGeneMat.small, NgVector=NULL, Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) MultiPP = GetMultiPP(MultiOut) } \keyword{ Posterior Probability } EBSeq/man/GetNg.Rd0000644000175100017510000000316012607264551014607 0ustar00biocbuildbiocbuild\name{GetNg} \alias{GetNg} \title{ Ng Vector } \description{ 'GetNg' generates the Ng vector for the isoform level data. (While using the number of isoform in the host gene to define the uncertainty groups.) } \usage{ GetNg(IsoformName, GeneName, TrunThre = 3) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{IsoformName}{A vector contains the isoform names.} \item{GeneName}{The gene names of the isoforms in IsoformNames (Should be in the same order).} \item{TrunThre}{The number of uncertainty groups the user wish to define. The default is 3.} } \value{ \item{GeneNg}{The number of isoforms that are contained in each gene. } \item{GeneNgTrun}{The truncated Ng of each gene. (The genes contain more than 3 isoforms are with Ng 3.) } \item{IsoformNg}{The Ng of each isoform.} \item{IsoformNgTrun}{The truncated Ng of each isoform (could be used to define the uncertainty group assignment).} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ data(IsoList) IsoMat = IsoList$IsoMat IsoNames = IsoList$IsoNames IsosGeneNames = IsoList$IsosGeneNames IsoSizes = MedianNorm(IsoMat) NgList = GetNg(IsoNames, IsosGeneNames) #IsoNgTrun = NgList$IsoformNgTrun #IsoEBOut = EBTest(Data = IsoMat, NgVector = IsoNgTrun, # Conditions = as.factor(rep(c("C1","C2"), each=5)), # sizeFactors = IsoSizes, maxround = 5) } \keyword{ Ng } EBSeq/man/GetNormalizedMat.Rd0000644000175100017510000000204212607264551017007 0ustar00biocbuildbiocbuild\name{GetNormalizedMat} \alias{GetNormalizedMat} \title{ Calculate normalized expression matrix } \description{ 'GetNormalizedMat' calculates the normalized expression matrix. (Note: this matrix is only used for visualization etc. EBTes and EBMultiTest request *un-adjusted* expressions and normalization factors.) } \usage{ GetNormalizedMat(Data, Sizes) } \arguments{ \item{Data}{The data matrix with transcripts in rows and lanes in columns.} \item{Sizes}{A vector contains the normalization factor for each lane.} } \value{The function will return a normalized matrix.} \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ data(GeneMat) str(GeneMat) Sizes = MedianNorm(GeneMat) NormData = GetNormalizedMat(GeneMat, Sizes) } \keyword{ Normalization }% __ONLY ONE__ keyword per line EBSeq/man/GetPP.Rd0000644000175100017510000000221712607264551014564 0ustar00biocbuildbiocbuild\name{GetPP} \alias{GetPP} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Generate the Posterior Probability of each transcript. } \description{ 'GetPP' generates the Posterior Probability of being DE of each transcript based on the EBTest output. } \usage{ GetPP(EBout) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{EBout}{The output of EBTest function.} } \value{The poster probabilities of being DE. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{GetPPMat } \examples{ data(GeneMat) GeneMat.small = GeneMat[c(1:10,500:550),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each=5)), sizeFactors = Sizes, maxround = 5) PPDE = GetPP(EBOut) str(PPDE) head(PPDE) } \keyword{ Posterior Probability } EBSeq/man/GetPPMat.Rd0000644000175100017510000000207512607264551015230 0ustar00biocbuildbiocbuild\name{GetPPMat} \alias{GetPPMat} \title{ Posterior Probability of Transcripts } \description{ 'GetPPMat' generates the Posterior Probability of being each pattern of each transcript based on the EBTest output. } \usage{ GetPPMat(EBout) } \arguments{ \item{EBout}{The output of EBTest function.} } \value{The poster probabilities of being EE (first column) and DE (second column). } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ data(GeneMat) GeneMat.small = GeneMat[c(500:550),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each=5)), sizeFactors = Sizes, maxround = 5) PP = GetPPMat(EBOut) str(PP) head(PP) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ Posterior Probability } EBSeq/man/GetPatterns.Rd0000644000175100017510000000140412607264551016042 0ustar00biocbuildbiocbuild\name{GetPatterns} \alias{GetPatterns} \title{ Generate all possible patterns in a multiple condition study } \description{ 'GetPatterns' generates all possible patterns in a multiple condition study. } \usage{ GetPatterns(Conditions) } \arguments{ \item{Conditions}{The names of the Conditions in the study.} } \value{A matrix describe all possible patterns. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ Conditions = c("C1","C1","C2","C2","C3","C3") PosParti = GetPatterns(Conditions) } EBSeq/man/IsoList.Rd0000644000175100017510000000107712607264551015176 0ustar00biocbuildbiocbuild\name{IsoList} \alias{IsoList} \docType{data} \title{ The simulated data for two condition isoform DE analysis } \description{ 'IsoList' gives the simulated data for two condition isoform DE analysis. } \usage{data(IsoList)} \source{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \seealso{GeteMat} \examples{ data(IsoList) } \keyword{datasets} EBSeq/man/IsoMultiList.Rd0000644000175100017510000000114712607264551016207 0ustar00biocbuildbiocbuild\name{IsoMultiList} \alias{IsoMultiList} \docType{data} \title{ The simulated data for multiple condition isoform DE analysis } \description{ 'IsoMultiList' gives a set of simulated data for multiple condition isoform DE analysis. } \usage{data(IsoMultiList)} \source{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \seealso{IsoList } \examples{ data(IsoMultiList) } \keyword{datasets} EBSeq/man/Likefun.Rd0000644000175100017510000000171412607264551015203 0ustar00biocbuildbiocbuild\name{Likefun} \alias{Likefun} \title{ Likelihood Function of the NB-Beta Model } \description{ 'Likefun' specifies the Likelihood Function of the NB-Beta Model. } \usage{ Likefun(ParamPool, InputPool) } \arguments{ \item{ParamPool}{The parameters that will be estimated in EM.} \item{InputPool}{The control parameters that will not be estimated in EM.} } \value{The function will return the log-likelihood. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ #x1 = c(.6,.7,.3) #Input = matrix(rnorm(100,100,1), ncol=10) #RIn = matrix(rnorm(100,200,1), ncol=10) #InputPool = list(Input[,1:5], Input[,6:10], Input, # rep(.1,100), 1, RIn, RIn[,1:5], RIn[,6:10], 100) #Likefun(x1, InputPool) } EBSeq/man/LikefunMulti.Rd0000644000175100017510000000214712607264551016217 0ustar00biocbuildbiocbuild\name{LikefunMulti} \alias{LikefunMulti} \title{ Likelihood Function of the NB-Beta Model In Multiple Condition Test } \description{ 'LikefunMulti' specifies the Likelihood Function of the NB-Beta Model In Multiple Condition Test. } \usage{ LikefunMulti(ParamPool, InputPool) } \arguments{ \item{ParamPool}{The parameters that will be estimated in EM.} \item{InputPool}{The control parameters that will not be estimated in EM.} } \value{The function will return the log-likelihood.} \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ #x1 = c(.6,.7,.3) #Input = matrix(rnorm(100,100,1),ncol=10) #RIn = matrix(rnorm(100,200,1),ncol=10) #InputPool = list(list(Input[,1:5],Input[,6:10]), # Input, cbind(rep(.1, 10), rep(.9,10)), 1, # RIn, list(RIn[,1:5],RIn[,6:10]), # 10, rbind(c(1,1),c(1,2))) #LikefunMulti(x1, InputPool) } EBSeq/man/LogN.Rd0000644000175100017510000000221112607264551014436 0ustar00biocbuildbiocbuild\name{LogN} \alias{LogN} \title{ The function to run EM (one round) algorithm for the NB-beta model. } \description{ 'LogN' specifies the function to run (one round of) the EM algorithm for the NB-beta model. } \usage{ LogN(Input, InputSP, EmpiricalR, EmpiricalRSP, NumOfEachGroup, AlphaIn, BetaIn, PIn, NoneZeroLength) } \arguments{ \item{Input, InputSP}{The expressions among all the samples.} \item{NumOfEachGroup}{Number of genes in each Ng group.} \item{AlphaIn, PIn, BetaIn, EmpiricalR, EmpiricalRSP}{The parameters from the last EM step.} \item{NoneZeroLength}{Number of Ng groups.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ #Input = matrix(rnorm(100,100,1), ncol=10) #rownames(Input) = paste("g",1:10) #RIn = matrix(rnorm(100,200,1), ncol=10) #res = LogN(Input, list(Input[,1:5], Input[,6:10]), # RIn, list(RIn[,1:5], RIn[,6:10]), # 10, .6, .7, .3, 1) } EBSeq/man/LogNMulti.Rd0000644000175100017510000000270712607264551015463 0ustar00biocbuildbiocbuild\name{LogNMulti} \alias{LogNMulti} %- Also NEED an '\alias' for EACH other topic documented here. \title{ EM algorithm for the NB-beta model in the multiple condition test } \description{ 'LogNMulti' specifies the function to run (one round of) the EM algorithm for the NB-beta model in the multiple condition test.} \usage{ LogNMulti(Input, InputSP, EmpiricalR, EmpiricalRSP, NumOfEachGroup, AlphaIn, BetaIn, PIn, NoneZeroLength, AllParti, Conditions) } \arguments{ \item{Input, InputSP}{The expressions among all the samples.} \item{NumOfEachGroup}{Number of genes in each Ng group.} \item{AlphaIn, PIn, BetaIn, EmpiricalR, EmpiricalRSP}{The parameters from the last EM step.} \item{NoneZeroLength}{Number of Ng groups.} \item{AllParti}{The patterns of interests.} \item{Conditions}{The condition assignment for each sample.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ # #Input = matrix(rnorm(100,100,1),ncol=10) #rownames(Input) = paste("g",1:10) #RIn = matrix(rnorm(100,200,1), ncol=10) #res = LogNMulti(Input, list(Input[,1:5], Input[,6:10]), # RIn, list(RIn[,1:5], RIn[,6:10]), 10, .6, .7, # c(.3,.7), 1, rbind(c(1,1), c(1,2)), # as.factor(rep(c("C1","C2"), each=5))) } EBSeq/man/MedianNorm.Rd0000644000175100017510000000162012607264551015633 0ustar00biocbuildbiocbuild\name{MedianNorm} \alias{MedianNorm} \title{ Median Normalization } \description{ 'MedianNorm' specifies the median normalization function from Anders et. al., 2010. } \usage{ MedianNorm(Data, alternative = FALSE) } \arguments{ \item{Data}{The data matrix with transcripts in rows and lanes in columns.} \item{alternative}{if alternative = TRUE, the alternative version of median normalization will be applied.} } \value{The function will return a vector contains the normalization factor for each lane.} \references{ Simon Anders and Wolfgang Huber. Differential expression analysis for sequence count data. Genome Biology (2010) 11:R106 (open access) } \author{ Ning Leng } \seealso{ QuantileNorm } \examples{ data(GeneMat) Sizes = MedianNorm(GeneMat) #EBOut = EBTest(Data = GeneMat, # Conditions = as.factor(rep(c("C1","C2"), each=5)), # sizeFactors = Sizes, maxround = 5) } \keyword{ Normalization } EBSeq/man/MultiGeneMat.Rd0000644000175100017510000000115312607264551016136 0ustar00biocbuildbiocbuild\name{MultiGeneMat} \alias{MultiGeneMat} \docType{data} \title{ The simulated data for multiple condition gene DE analysis } \description{ 'MultiGeneMat' generates a set of the simulated data for multiple condition gene DE analysis. } \usage{data(MultiGeneMat)} \source{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \seealso{ GeneMat } \examples{ data(MultiGeneMat) } \keyword{datasets} EBSeq/man/PlotPattern.Rd0000644000175100017510000000142212607264551016056 0ustar00biocbuildbiocbuild\name{PlotPattern} \alias{PlotPattern} \title{ Visualize the patterns } \description{ 'PlotPattern' generates the visualized patterns before the multiple condition test. } \usage{ PlotPattern(Patterns) } \arguments{ \item{Patterns}{ The output of GetPatterns function. } } \value{ A heatmap to visualize the patterns of interest. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ Conditions = c("C1","C1","C2","C2","C3","C3") Patterns = GetPatterns(Conditions) PlotPattern(Patterns) } \keyword{ patterns } EBSeq/man/PlotPostVsRawFC.Rd0000644000175100017510000000202412607264551016561 0ustar00biocbuildbiocbuild\name{PlotPostVsRawFC} \alias{PlotPostVsRawFC} \title{ Plot Posterior FC vs FC } \description{ 'PlotPostVsRawFC' helps the users visualize the posterior FC vs FC in a two condition study. } \usage{ PlotPostVsRawFC(EBOut, FCOut) } \arguments{ \item{EBOut}{ The output of EBMultiTest function. } \item{FCOut}{The output of PostFC function.} } \value{ A figure shows fold change vs posterior fold change. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ PostFC } \examples{ data(GeneMat) GeneMat.small = GeneMat[c(500:600),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each=5)), sizeFactors = Sizes, maxround = 5) FC = PostFC(EBOut) PlotPostVsRawFC(EBOut,FC) } \keyword{ Posterior Probability } EBSeq/man/PolyFitPlot.Rd0000644000175100017510000000536012607264550016033 0ustar00biocbuildbiocbuild\name{PolyFitPlot} \alias{PolyFitPlot} \title{ Fit the mean-var relationship using polynomial regression } \description{ 'PolyFitPlot' fits the mean-var relationship using polynomial regression. } \usage{ PolyFitPlot(X, Y, nterms, xname = "Estimated Mean", yname = "Estimated Var", pdfname = "", xlim = c(-1,5), ylim = c(-1,7), ChangeXY = F, col = "red") } %- maybe also 'usage' for other objects documented here. \arguments{ \item{X}{ The first group of values want to be fitted by the polynomial regression (e.g Mean of the data). } \item{Y}{ The second group of values want to be fitted by the polynomial regression (e.g. variance of the data). The length of Y should be the same as the length of X. } \item{nterms}{ How many polynomial terms want to be used. } \item{xname}{ Name of the x axis. } \item{yname}{ Name of the y axis. } \item{pdfname}{ Name of the plot. } \item{xlim}{ The x limits of the plot. } \item{ylim}{ The y limits of the plot. } \item{ChangeXY}{ If ChangeXY is setted to be TRUE, X will be treated as the dependent variable and Y will be treated as the independent one. Default is FALSE. } \item{col}{ Color of the fitted line. } } \value{The PolyFitPlot function provides a smooth scatter plot of two variables and their best fitting line of polynomial regression. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ data(IsoList) str(IsoList) IsoMat = IsoList$IsoMat IsoNames = IsoList$IsoNames IsosGeneNames = IsoList$IsosGeneNames IsoSizes = MedianNorm(IsoMat) NgList = GetNg(IsoNames, IsosGeneNames) IsoNgTrun = NgList$IsoformNgTrun #IsoEBOut = EBTest(Data = IsoMat.small, # NgVector = IsoNgTrun, # Conditions = as.factor(rep(c("C1","C2"), each=5)), # sizeFactors = IsoSizes, maxround = 5) #par(mfrow=c(2,2)) #PolyFitValue = vector("list",3) #for(i in 1:3) # PolyFitValue[[i]] = PolyFitPlot(IsoEBOut$C1Mean[[i]], # IsoEBOut$C1EstVar[[i]], 5) #PolyAll = PolyFitPlot(unlist(IsoEBOut$C1Mean), # unlist(IsoEBOut$C1EstVar), 5) #lines(log10(IsoEBOut$C1Mean[[1]][PolyFitValue[[1]]$sort]), # PolyFitValue[[1]]$fit[PolyFitValue[[1]]$sort], # col="yellow", lwd=2) #lines(log10(IsoEBOut$C1Mean[[2]][PolyFitValue[[2]]$sort]), # PolyFitValue[[2]]$fit[PolyFitValue[[2]]$sort], # col="pink", lwd=2) #lines(log10(IsoEBOut$C1Mean[[3]][PolyFitValue[[3]]$sort]), # PolyFitValue[[3]]$fit[PolyFitValue[[3]]$sort], # col="green", lwd=2) #legend("topleft",c("All Isoforms","Ng = 1","Ng = 2","Ng = 3"), # col = c("red","yellow","pink","green"), # lty=1, lwd=3, box.lwd=2) } EBSeq/man/PostFC.Rd0000644000175100017510000000306512607264551014745 0ustar00biocbuildbiocbuild\name{PostFC} \alias{PostFC} \title{ Calculate the posterior fold change for each transcript across conditions } \description{ 'PostFC' calculates the posterior fold change for each transcript across conditions. } \usage{ PostFC(EBoutput, SmallNum = 0.01) } \arguments{ \item{EBoutput}{ The ourput from function EBTest. } \item{SmallNum}{A small number will be added for each transcript in each condition to avoid Inf and NA. Default is 0.01.} } \value{ Provide both FC and posterior FC across two conditions. FC is calculated as (MeanC1+SmallNum)/(MeanC2+SmallNum). And Posterior FC is calculated as: # Post alpha P_a_C1 = alpha + r_C1 * n_C1 # Post beta P_b_C1 = beta + Mean_C1 * n_C1 # P_q_C1 = P_a_C1 / (P_a_C1 + P_b_C1) # Post FC = ((1-P_q_C1)/P_q_c1) / ( (1-P_q_c2)/P_q_c2) \item{PostFC}{The posterior FC across two conditions.} \item{RealFC}{The FC across two conditions (adjusted by the normalization factors).} \item{Direction}{The diretion of FC calculation.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ EBTest, GetMultiFC } \examples{ data(GeneMat) GeneMat.small = GeneMat[c(500:550),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each=5)), sizeFactors = Sizes, maxround = 5) FC=PostFC(EBOut) } \keyword{ Fold Change } EBSeq/man/QQP.Rd0000644000175100017510000000230612607264551014245 0ustar00biocbuildbiocbuild\name{QQP} \alias{QQP} \title{ The Quantile-Quantile Plot to compare the empirical q's and simulated q's from fitted beta distribution } \description{ 'QQP' gives the Quantile-Quantile Plot to compare the empirical q's and simulated q's from fitted beta distribution. } \usage{ QQP(EBOut, GeneLevel = F) } \arguments{ \item{EBOut}{The output of EBTest or EBMultiTest. } \item{GeneLevel}{Indicate whether the results are from data at gene level.} } \value{ For data with n1 conditions and n2 uncertainty groups, n1*n2 plots will be generated. Each plot represents a subset of the data. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ EBTest, EBMultiTest, DenNHist } \examples{ data(GeneMat) GeneMat.small = GeneMat[c(500:1000),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each=5)), sizeFactors = Sizes, maxround = 5) par(mfrow=c(2,2)) QQP(EBOut) } \keyword{ Q-Q plot } EBSeq/man/QuantileNorm.Rd0000644000175100017510000000173512607264551016227 0ustar00biocbuildbiocbuild\name{QuantileNorm} \alias{QuantileNorm} \title{ Quantile Normalization } \description{ 'QuantileNorm' gives the quantile normalization. } \usage{ QuantileNorm(Data, Quantile) } \arguments{ \item{Data}{ The data matrix with transcripts in rows and lanes in columns. } \item{Quantile}{ The quantile the user wishs to use. Should be a number between 0 and 1. } } \details{ Use a quantile point to normalize the data. } \value{ The function will return a vector contains the normalization factor for each lane. % ... } \references{ Bullard, James H., et al. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC bioinformatics 11.1 (2010): 94. } \author{ Ning Leng } \seealso{ MedianNorm } \examples{ data(GeneMat) Sizes = QuantileNorm(GeneMat,.75) #EBOut = EBTest(Data = GeneMat, # Conditions = as.factor(rep(c("C1","C2"), each=5)), # sizeFactors = Sizes, maxround = 5) } \keyword{ Normalization }% __ONLY ONE__ keyword per line EBSeq/man/RankNorm.Rd0000644000175100017510000000127212607264551015334 0ustar00biocbuildbiocbuild\name{RankNorm} \alias{RankNorm} \title{ Rank Normalization } \description{ 'RankNorm' gives the rank normalization. } \usage{ RankNorm(Data) } \arguments{ \item{Data}{ The data matrix with transcripts in rows and lanes in columns. } } \value{ The function will return a matrix contains the normalization factor for each lane and each transcript. } \author{ Ning Leng } \seealso{ MedianNorm, QuantileNorm } \examples{ data(GeneMat) Sizes = RankNorm(GeneMat) # Run EBSeq # EBres = EBTest(Data = GeneData, NgVector = rep(1,10^4), # Vect5End = rep(1,10^4), Vect3End = rep(1,10^4), # Conditions = as.factor(rep(c(1,2), each=5)), # sizeFactors = Sizes, maxround=5) } \keyword{ Normalization } EBSeq/man/beta.mom.Rd0000644000175100017510000000151412607264551015306 0ustar00biocbuildbiocbuild\name{beta.mom} \alias{beta.mom} \title{ Fit the beta distribution by method of moments } \description{ 'beta.mom' fits the beta distribution by method of moments. } \usage{ beta.mom(qs.in) } \arguments{ \item{qs.in}{A vector contains the numbers that are assumed to follow a beta distribution.} } \value{ \item{alpha.hat}{Returns the estimation of alpha.} \item{beta.hat}{Returns the estimation of beta.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ DenNHist, DenNHistTable } \examples{ #tmp = rbeta(5, 5, 100) #param = beta.mom(tmp) } \keyword{ beta } EBSeq/man/crit_fun.Rd0000644000175100017510000000305212607264551015414 0ustar00biocbuildbiocbuild\name{crit_fun} \alias{crit_fun} \title{ Calculate the soft threshold for a target FDR } \description{ 'crit_fun' calculates the soft threshold for a target FDR. } \usage{ crit_fun(PPEE, thre) } \arguments{ \item{PPEE}{The posterior probabilities of being EE.} \item{thre}{The target FDR.} } \details{ Regarding a target FDR alpha, both hard threshold and soft threshold could be used. If the hard threshold is preferred, user could simply take the transcripts with PP(DE) greater than (1-alpha). Using the hard threshold, any DE transcript in the list is with FDR <= alpha. If the soft threshold is preferred, user could take the transcripts with PP(DE) greater than crit_fun(PPEE, alpha). Using the soft threshold, the list of DE transcripts is with average FDR alpha. } \value{ The adjusted FDR threshold of target FDR. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ data(GeneMat) GeneMat.small = GeneMat[c(1:10, 500:600),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each=5)), sizeFactors = Sizes, maxround = 5) PP = GetPPMat(EBOut) DEfound = rownames(PP)[which(PP[,"PPDE"] >= 0.95)] str(DEfound) SoftThre = crit_fun(PP[,"PPEE"], 0.05) DEfound_soft = rownames(PP)[which(PP[,"PPDE"] >= SoftThre)] } \keyword{ FDR } EBSeq/man/f0.Rd0000644000175100017510000000205112607264551014106 0ustar00biocbuildbiocbuild\name{f0} \alias{f0} %- Also NEED an '\alias' for EACH other topic documented here. \title{ The Prior Predictive Distribution of being EE } \description{ 'f0' gives the Prior Predictive Distribution of being EE. } \usage{ f0(Input, AlphaIn, BetaIn, EmpiricalR, NumOfGroups, log) } \arguments{ \item{Input}{Expression Values.} \item{AlphaIn, BetaIn, EmpiricalR}{The parameters estimated from last iteration of EM.} \item{NumOfGroups}{How many transcripts within each Ng group.} \item{log}{If true, will give the log of the output.} } \value{ The function will return the prior predictive distribution values of being EE. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ f1 } \examples{ # #f0(matrix(rnorm(100,100,1),ncol=10), .5, .6, # matrix(rnorm(100,200,1),ncol=10), 100, TRUE) } EBSeq/man/f1.Rd0000644000175100017510000000222712607264551014114 0ustar00biocbuildbiocbuild\name{f1} \alias{f1} \title{ The Prior Predictive Distribution of being DE } \description{ 'f1' gives the Prior Predictive Distribution of DE. } \usage{ f1(Input1, Input2, AlphaIn, BetaIn, EmpiricalRSP1, EmpiricalRSP2, NumOfGroup, log) } \arguments{ \item{Input1}{Expressions from Condition1.} \item{Input2}{Expressions from Condition2.} \item{AlphaIn, BetaIn, EmpiricalRSP1, EmpiricalRSP2}{The parameters estimated from last iteration of EM.} \item{NumOfGroup}{ How many transcripts within each Ng group.} \item{log}{If true, will give the log of the output.} } \value{ The function will return the prior predictive distribution values of being DE. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ f0 } \examples{ #f1(matrix(rnorm(100,100,1),ncol=10), # matrix(rnorm(100,100,1),ncol=10), .5, .6, # matrix(rnorm(100,200,1),ncol=10), # matrix(rnorm(100,200,1),ncol=10), 100, TRUE) } EBSeq/vignettes/0000755000175100017510000000000012607342353014546 5ustar00biocbuildbiocbuildEBSeq/vignettes/EBSeq_Vignette.Rnw0000644000175100017510000013161212607264551020051 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{EBSeq Vignette} \documentclass{article} \usepackage{fullpage} \usepackage{graphicx, graphics, epsfig,setspace,amsmath, amsthm} \usepackage{hyperref} \usepackage{natbib} %\usepackage{listings} \usepackage{moreverb} \begin{document} \title{EBSeq: An R package for differential expression analysis using RNA-seq data} \author{Ning Leng, John Dawson, and Christina Kendziorski} \maketitle \tableofcontents \setcounter{tocdepth}{2} \section{Introduction} EBSeq may be used to identify differentially expressed (DE) genes and isoforms in an RNA-Seq experiment. As detailed in Leng {\it et al.}, 2013 \cite{Leng13}, EBSeq is an empirical Bayesian approach that models a number of features observed in RNA-seq data. Importantly, for isoform level inference, EBSeq directly accommodates isoform expression estimation uncertainty by modeling the differential variability observed in distinct groups of isoforms. Consider Figure 1, where we have plotted variance against mean for all isoforms using RNA-Seq expression data from Leng {\it et al.}, 2013 \cite{Leng13}. Also shown is the fit within three sub-groups of isoforms defined by the number of constituent isoforms of the parent gene. An isoform of gene $g$ is assigned to the $I_g=k$ group, where $k=1,2,3$, if the total number of isoforms from gene $g$ is $k$ (the $I_g=3$ group contains all isoforms from genes having 3 or more isoforms). As shown in Figure 1, there is decreased variability in the $I_g=1$ group, but increased variability in the others, due to the relative increase in uncertainty inherent in estimating isoform expression when multiple isoforms of a given gene are present. If this structure is not accommodated, there is reduced power for identifying isoforms in the $I_g=1$ group (since the true variances in that group are lower, on average, than that derived from the full collection of isoforms) as well as increased false discoveries in the $I_g=2$ and $I_g=3$ groups (since the true variances are higher, on average, than those derived from the full collection). EBSeq directly models differential variability as a function of $I_g$ providing a powerful approach for isoform level inference. As shown in Leng {\it et al.}, 2013 \cite{Leng13}, the model is also useful for identifying DE genes. We will briefly detail the model in Section \ref{sec:model} and then describe the flow of analysis in Section \ref{sec:quickstart} for both isoform and gene-level inference. \begin{figure}[t] \centering \includegraphics[width=0.6\textwidth]{PlotExample.png} \label{fig:GouldNg} \caption{Empirical variance vs. mean for each isoform profiled in the ESCs vs iPSCs experiment detailed in the Case Study section of Leng {\it et al.}, 2013 \cite{Leng13}. A spline fit to all isoforms is shown in red with splines fit within the $I_g=1$, $I_g=2$, and $I_g=3$ isoform groups shown in yellow, pink, and green, respectively.} \end{figure} \section{Citing this software} \label{sec:cite} Please cite the following article when reporting results from the software. \noindent Leng, N., J.A. Dawson, J.A. Thomson, V. Ruotti, A.I. Rissman, B.M.G. Smits, J.D. Haag, M.N. Gould, R.M. Stewart, and C. Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments, {\it Bioinformatics}, 2013. \section{The Model} \label{sec:model} \subsection{Two conditions} \label{sec:twocondmodel} We let $X_{g_i}^{C1} = X_{g_i,1} ,X_{g_i,2}, ...,X_{g_i,S_1}$ denote data from condition 1 and $ X_{g_i}^{C2} = X_{g_i,(S_1+1)},X_{g_i,(S_1+2)},...,X_{g_i,S}$ data from condition 2. We assume that counts within condition $C$ are distributed as Negative Binomial: $X_{g_i,s}^C|r_{g_i,s}, q_{g_i}^C \sim NB(r_{g_i,s}, q_{g_i}^C)$ where \begin{equation} P(X_{g_i,s}|r_{g_i,s},q_{g_i}^C) = {X_{g_i,s}+r_{g_i,s}-1\choose X_{g_i,s}}(1-q_{g_i}^C)^{X_{g_i,s}}(q_{g_i}^C)^{r_{g_i,s}}\label{eq:01} \end{equation} \noindent and $\mu_{g_i,s}^C=r_{g_i,s} (1-q_{g_i}^C)/q_{g_i}^C$; $(\sigma_{g_i,s}^C)^2=r_{g_i,s} (1-q_{g_i}^C)/(q_{g_i}^C)^2.$ \medskip We assume a prior distribution on $q_{g_i}^C$: $q_{g_i}^C|\alpha, \beta^{I_g} \sim Beta(\alpha, \beta^{I_g})$. The hyperparameter $\alpha$ is shared by all the isoforms and $\beta^{I_g}$ is $I_g$ specific (note this is an index, not a power). We further assume that $r_{g_i,s}=r_{g_i,0} l_s$, where $r_{g_i,0}$ is an isoform specific parameter common across conditions and $r_{g_i,s}$ depends on it through the sample-specific normalization factor $l_s$. Of interest in this two group comparison is distinguishing between two cases, or what we will refer to subsequently as two patterns of expression, namely equivalent expression (EE) and differential expression (DE): \begin{center} $H_0$ (EE) : $q_{g_i}^{C1}=q_{g_i}^{C2}$ vs $H_1$ (DE) : $q_{g_i}^{C1} \neq q_{g_i}^{C2}$. \end{center} Under the null hypothesis (EE), the data $X_{g_i}^{C1,C2} = X_{g_i}^{C1}, X_{g_i}^{C2}$ arises from the prior predictive distribution $f_0^{I_g}(X_{g_i}^{C1,C2})$: %\tiny \begin{equation} f_0^{I_g}(X_{g_i}^{C1,C2})=\Bigg[\prod_{s=1}^S {X_{g_i,s}+r_{g_i,s}-1\choose X_{g_i,s}}\Bigg] \frac{Beta(\alpha+\sum_{s=1}^S r_{g_i,s}, \beta^{I_g}+\sum_{s=1}^SX_{g_i,s} )}{Beta(\alpha, \beta^{I_g})}\label{eq:05} \end{equation} %\normalsize Alternatively (in a DE scenario), $X_{g_i}^{C1,C2}$ follows the prior predictive distribution $f_1^{I_g}(X_{g_i}^{C1,C2})$: \begin{equation} f_1^{I_g}(X_{g_i}^{C1,C2})=f_0^{I_g}(X_{g_i}^{C1})f_0^{I_g}(X_{g_i}^{C2}) \label{eq:06} \end{equation} Let the latent variable $Z_{g_i}$ be defined so that $Z_{g_i} = 1$ indicates that isoform $g_i$ is DE and $Z_{g_i} = 0$ indicates isoform $g_i$ is EE, and $Z_{g_i} \sim Bernoulli(p)$. Then, the marginal distribution of $X_{g_i}^{C1,C2}$ and $Z_{g_i}$ is: \begin{equation} (1-p)f_0^{I_g}(X_{g_i}^{C1,C2}) + pf_1^{I_g}(X_{g_i}^{C1,C2})\label{eq:07} \end{equation} \noindent The posterior probability of being DE at isoform $g_i$ is obtained by Bayes' rule: \begin{equation} \frac{pf_1^{I_g}(X_{g_i}^{C1,C2})}{(1-p)f_0^{I_g}(X_{g_i}^{C1,C2}) + pf_1^{I_g}(X_{g_i}^{C1,C2})}\label{eq:08} \end{equation} %\newpage \subsection{More than two conditions} \label{sec:multicondmodel} EBSeq naturally accommodates multiple condition comparisons. For example, in a study with 3 conditions, there are K=5 possible expression patterns (P1,...,P5), or ways in which latent levels of expression may vary across conditions: \begin{align} \textrm {P1:}& \hspace{0.05in} q_{g_i}^{C1} = q_{g_i}^{C2}=q_{g_i}^{C3} \nonumber \\ \textrm {P2:}& \hspace{0.05in} q_{g_i}^{C1} = q_{g_i}^{C2} \neq q_{g_i}^{C3} \nonumber \\ \textrm {P3:}& \hspace{0.05in} q_{g_i}^{C1} = q_{g_i}^{C3} \neq q_{g_i}^{C2} \nonumber \\ \textrm {P4:}& \hspace{0.05in} q_{g_i}^{C1} \neq q_{g_i}^{C2} = q_{g_i}^{C3} \nonumber \\ \textrm {P5:}& \hspace{0.05in} q_{g_i}^{C1} \neq q_{g_i}^{C2} \neq q_{g_i}^{C3} \textrm{ and } q_{g_i}^{C1} \neq q_{g_i}^{C3} \nonumber \end{align} \noindent The prior predictive distributions for these are given, respectively, by: \begin{align} g_1^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1,C2,C3}) \nonumber \\ g_2^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1,C2})f_0^{I_g}(X_{g_i}^{C3}) \nonumber \\ g_3^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1,C3})f_0^{I_g}(X_{g_i}^{C2}) \nonumber \\ g_4^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1})f_0^{I_g}(X_{g_i}^{C2,C3}) \nonumber \\ g_5^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1})f_0^{I_g}(X_{g_i}^{C2})f_0^{I_g}(X_{g_i}^{C3}) \nonumber \end{align} \noindent where $f_0^{I_g}$ is the same as in equation \ref{eq:05}. Then the marginal distribution in equation \ref{eq:07} becomes: \begin{equation} \sum_{k=1}^5 p_k g_k^{I_g}(X_{g_i}^{C1,C2,C3}) \label{eq:11} \end{equation} \noindent where $\sum_{k=1}^5 p_k = 1$. Thus, the posterior probability of isoform $g_i$ coming from pattern $K$ is readily obtained by: \begin{equation} \frac{p_K g_K^{I_g}(X_{g_i}^{C1,C2,C3})}{\sum_{k=1}^5 p_k g_k^{I_g}(X_{g_i}^{C1,C2,C3})} \label{eq:12} \end{equation} \subsection{Getting a false discovery rate (FDR) controlled list of genes or isoforms} \label{sec:fdrlist} To obtain a list of DE genes with false discovery rate (FDR) controlled at $\alpha$ in an experiment comparing two biological conditions, the genes with posterior probability of being DE (PPDE) greater than 1 - $\alpha$ should be used. For example, the genes with PPDE>=0.95 make up the list of DE genes with target FDR controlled at 5\%. With more than two biological conditions, there are multiple DE patterns (see Section \ref{sec:multicondmodel}). To obtain a list of genes in a specific DE pattern with target FDR $\alpha$, a user should take the genes with posterior probability of being in that pattern greater than 1 - $\alpha$. Isoform-based lists are obtained in the same way. \newpage \section{Quick Start} \label{sec:quickstart} Before analysis can proceed, the EBSeq package must be loaded into the working space: <<>>= library(EBSeq) @ \subsection{Gene level DE analysis (two conditions)} \label{sec:startgenede} \subsubsection{Required input} \label{sec:startgenedeinput} \begin{flushleft} {\bf Data}: The object \verb+Data+ should be a $G-by-S$ matrix containing the expression values for each gene and each sample, where $G$ is the number of genes and $S$ is the number of samples. These values should exhibit raw counts, without normalization across samples. Counts of this nature may be obtained from RSEM \cite{Li11b}, Cufflinks \cite{Trapnell12}, or a similar approach. \vspace{5 mm} {\bf Conditions}: The object \verb+Conditions+ should be a Factor vector of length $S$ that indicates to which condition each sample belongs. For example, if there are two conditions and three samples in each, $S=6$ and \verb+Conditions+ may be given by \verb+as.factor(c("C1","C1","C1","C2","C2","C2"))+ \end{flushleft} \noindent The object \verb+GeneMat+ is a simulated data matrix containing 1,000 rows of genes and 10 columns of samples. The genes are named \verb+Gene_1, Gene_2 ...+ <<>>= data(GeneMat) str(GeneMat) @ \subsubsection{Library size factor} \label{sec:startgenedesize} As detailed in Section \ref{sec:model}, EBSeq requires the library size factor $l_s$ for each sample $s$. Here, $l_s$ may be obtained via the function \verb+MedianNorm+, which reproduces the median normalization approach in DESeq \citep{Anders10}. <<>>= Sizes=MedianNorm(GeneMat) @ \noindent If quantile normalization is preferred, $l_s$ may be obtained via the function \verb+QuantileNorm+. (e.g. \verb+QuantileNorm(GeneMat,.75)+ for Upper-Quantile Normalization in \cite{Bullard10}) \subsubsection{Running EBSeq on gene expression estimates} \label{sec:startgenederun} The function \verb+EBTest+ is used to detect DE genes. For gene-level data, we don't need to specify the parameter \verb+NgVector+ since there are no differences in $I_g$ structure among the different genes. Here, we simulated the first five samples to be in condition 1 and the other five in condition 2, so define: \verb+Conditions=as.factor(rep(c("C1","C2"),each=5))+ \noindent \verb+sizeFactors+ is used to define the library size factor of each sample. It could be obtained by summing up the total number of reads within each sample, Median Normalization \citep{Anders10}, scaling normalization \citep{Robinson10}, Upper-Quantile Normalization \cite{Bullard10}, or some other such approach. These in hand, we run the EM algorithm, setting the number of iterations to five via \verb+maxround=5+ for demonstration purposes. However, we note that in practice, additional iterations are usually required. Convergence should always be checked (see Section \ref{sec:detailedgenedeconverge} for details). Please note this may take several minutes: <<>>= EBOut=EBTest(Data=GeneMat, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=Sizes, maxround=5) @ \noindent The list of DE genes and the posterior probabilities of being DE are obtained as follows <<>>= EBDERes=GetDEResults(EBOut, FDR=0.05) str(EBDERes$DEfound) head(EBDERes$PPMat) str(EBDERes$Status) @ \noindent \verb+EBDERes$DEfound+ is a list of genes identified with 5\% FDR. EBSeq found 95 genes. The matrix \verb+EBDERes$PPMat+ contains two columns \verb+PPEE+ and \verb+PPDE+, corresponding to the posterior probabilities of being EE or DE for each gene. \verb+EBDERes$Status+ contains each gene's status called by EBSeq. \noindent Note the \verb+GetDEResults()+ was incorporated in EBSeq since version 1.7.1. By using the default settings, the number of genes identified in any given analysis may differ slightly from the previous version. The updated algorithm is more robust to outliers and transcripts with low variance. To obtain results that are comparable to results from earlier versions of EBSeq ($\le$ 1.7.0), a user may set \verb+Method="classic"+ in \verb+GetDEResults()+ function, or use the \verb+GetPPMat()+ function. \subsection{Isoform level DE analysis (two conditions)} \label{sec:startisode} \subsubsection{Required inputs} \label{sec:startisodeinput} \begin{flushleft} {\bf Data}: The object \verb+Data+ should be a $I-by-S$ matrix containing the expression values for each isoform and each sample, where $I$ is the number of isoforms and $S$ is the number of sample. As in the gene-level analysis, these values should exhibit raw data, without normalization across samples. \vspace{5 mm} {\bf Conditions}: The object \verb+Conditions+ should be a vector with length $S$ to indicate the condition of each sample. \vspace{5 mm} {\bf IsoformNames}: The object \verb+IsoformNames+ should be a vector with length $I$ to indicate the isoform names. \vspace{5 mm} {\bf IsosGeneNames}: The object \verb+IsosGeneNames+ should be a vector with length $I$ to indicate the gene name of each isoform. (in the same order as \verb+IsoformNames+.) \end{flushleft} \noindent \verb+IsoList+ contains 1,200 simulated isoforms. In which \verb+IsoList$IsoMat+ is a data matrix containing 1,200 rows of isoforms and 10 columns of samples; \verb+IsoList$IsoNames+ contains the isoform names; \verb+IsoList$IsosGeneNames+ contains the names of the genes the isoforms belong to. <<>>= data(IsoList) str(IsoList) IsoMat=IsoList$IsoMat str(IsoMat) IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames @ \subsubsection{Library size factor} \label{sec:startisodesize} Similar to the gene-level analysis presented above, we may obtain the isoform-level library size factors via \verb+MedianNorm+: <<>>= IsoSizes=MedianNorm(IsoMat) @ \subsubsection{The $I_g$ vector} \label{sec:startisodeNg} While working on isoform level data, EBSeq fits different prior parameters for different uncertainty groups (defined as $I_g$ groups). The default setting to define the uncertainty groups consists of using the number of isoforms the host gene contains ($N_g$) for each isoform. The default settings will provide three uncertainty groups: $I_g=1$ group: Isoforms with $N_g=1$; $I_g=2$ group: Isoforms with $N_g=2$; $I_g=3$ group: Isoforms with $N_g \geq 3$. The $N_g$ and $I_g$ group assignment can be obtained using the function \verb+GetNg+. The required inputs of \verb+GetNg+ are the isoform names (\verb+IsoformNames+) and their corresponding gene names (\verb+IsosGeneNames+). <<>>= NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoNgTrun[c(1:3,201:203,601:603)] @ More details could be found in Section \ref{sec:detailedisode}. \subsubsection{Running EBSeq on isoform expression estimates} \label{sec:startisoderun} The \verb+EBTest+ function is also used to run EBSeq for two condition comparisons on isoform-level data. Below we use 5 iterations to demonstrate. However, as in the gene level analysis, we advise that additional iterations will likely be required in practice (see Section \ref{sec:detailedisodeconverge} for details). <<>>= IsoEBOut=EBTest(Data=IsoMat, NgVector=IsoNgTrun, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=IsoSizes, maxround=5) IsoEBDERes=GetDEResults(IsoEBOut, FDR=0.05) str(IsoEBDERes$DEfound) head(IsoEBDERes$PPMat) str(IsoEBDERes$Status) @ \noindent We see that EBSeq found 104 DE isoforms at the target FDR of 0.05. \noindent Note the \verb+GetDEResults()+ was incorporated in EBSeq since version 1.7.1. By using the default settings, the number of transcripts identified in any given analysis may differ slightly from the previous version. The updated algorithm is more robust to outliers and transcripts with low variance. To obtain results that are comparable to results from earlier versions of EBSeq ($\le$ 1.7.0), a user may set \verb+Method="classic"+ in \verb+GetDEResults()+ function, or use the \verb+GetPPMat()+ function. \subsection{Gene level DE analysis (more than two conditions)} \label{sec:startmulticond} \noindent The object \verb+MultiGeneMat+ is a matrix containing 500 simulated genes with 6 samples: the first two samples are from condition 1; the second and the third sample are from condition 2; the last two samples are from condition 3. <<>>= data(MultiGeneMat) str(MultiGeneMat) @ In analysis where the data are spread over more than two conditions, the set of possible patterns for each gene is more complicated than simply EE and DE. As noted in Section \ref{sec:model}, when we have 3 conditions, there are 5 expression patterns to consider. In the simulated data, we have 6 samples, 2 in each of 3 conditions. The function \verb+GetPatterns+ allows the user to generate all possible patterns given the conditions. For example: <<>>= Conditions=c("C1","C1","C2","C2","C3","C3") PosParti=GetPatterns(Conditions) PosParti @ \noindent where the first row means all three conditions have the same latent mean expression level; the second row means C1 and C2 have the same latent mean expression level but that of C3 is different; and the last row corresponds to the case where the three conditions all have different latent mean expression levels. The user may use all or only some of these possible patterns as an input to \verb+EBMultiTest+. For example, if we were interested in Patterns 1, 2, 4 and 5 only, we'd define: <<>>= Parti=PosParti[-3,] Parti @ Moving on to the analysis, \verb+MedianNorm+ or one of its competitors should be used to determine the normalization factors. Once this is done, the formal test is performed by \verb+EBMultiTest+. <<>>= MultiSize=MedianNorm(MultiGeneMat) MultiOut=EBMultiTest(MultiGeneMat,NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) @ \noindent The posterior probability of being in each pattern for every gene is obtained by using the function \verb+GetMultiPP+: <<>>= MultiPP=GetMultiPP(MultiOut) names(MultiPP) MultiPP$PP[1:10,] MultiPP$MAP[1:10] MultiPP$Patterns @ \noindent where \verb+MultiPP$PP+ provides the posterior probability of being in each pattern for every gene. \verb+MultiPP$MAP+ provides the most likely pattern of each gene based on the posterior probabilities. \verb+MultiPP$Patterns+ provides the details of the patterns. \subsection{Isoform level DE analysis (more than two conditions)} \label{sec:startisomulticond} \noindent Similar to \verb+IsoList+, the object \verb+IsoMultiList+ is an object containing the isoform expression estimates matrix, the isoform names, and the gene names of the isoforms' host genes. \verb+IsoMultiList$IsoMultiMat+ contains 300 simulated isoforms with 8 samples. The first two samples are from condition 1; the second and the third sample are from condition 2; the fifth and sixth sample are from condition 3; the last two samples are from condition 4. Similar to Section \ref{sec:startisode}, the function \verb+MedianNorm+ and \verb+GetNg+ could be used for normalization and calculating the $N_g$'s. <<>>= data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiSize=MedianNorm(IsoMultiMat) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C1","C2","C2","C3","C3","C4","C4") @ Here we have 4 conditions, there are 15 expression patterns to consider. The function \verb+GetPatterns+ allows the user to generate all possible patterns given the conditions. For example: <<>>= PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond @ \noindent If we were interested in Patterns 1, 2, 3, 8 and 15 only, we'd define: <<>>= Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond @ \noindent Moving on to the analysis, \verb+EBMultiTest+ could be used to perform the test: <<>>= IsoMultiOut=EBMultiTest(IsoMultiMat, NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize, maxround=5) @ \noindent The posterior probability of being in each pattern for every gene is obtained by using the function \verb+GetMultiPP+: <<>>= IsoMultiPP=GetMultiPP(IsoMultiOut) names(MultiPP) IsoMultiPP$PP[1:10,] IsoMultiPP$MAP[1:10] IsoMultiPP$Patterns @ \noindent where \verb+MultiPP$PP+ provides the posterior probability of being in each pattern for every gene. \verb+MultiPP$MAP+ provides the most likely pattern of each gene based on the posterior probabilities. \verb+MultiPP$Patterns+ provides the details of the patterns. \newpage \section{More detailed examples} \label{sec:detailed} \subsection{Gene level DE analysis (two conditions)} \label{sec:detailedgenede} \subsubsection{Running EBSeq on simulated gene expression estimates} \label{sec:detailedgenederun} EBSeq is applied as described in Section \ref{sec:startgenederun}. <>= data(GeneMat) Sizes=MedianNorm(GeneMat) EBOut=EBTest(Data=GeneMat, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=Sizes, maxround=5) EBDERes=GetDEResults(EBOut, FDR=0.05) @ <<>>= EBDERes=GetDEResults(EBOut, FDR=0.05) str(EBDERes$DEfound) head(EBDERes$PPMat) str(EBDERes$Status) @ \noindent EBSeq found 95 DE genes at a target FDR of 0.05.\\ \subsubsection{Calculating FC} \label{sec:detailedgenedefc} The function \verb+PostFC+ may be used to calculate the Fold Change (FC) of the raw data as well as the posterior FC of the normalized data. \begin{figure}[h!] \centering <>= GeneFC=PostFC(EBOut) str(GeneFC) PlotPostVsRawFC(EBOut,GeneFC) @ \caption{ FC vs. Posterior FC for 1,000 gene expression estimates} \label{fig:GeneFC} \end{figure} Figure \ref{fig:GeneFC} shows the FC vs. Posterior FC on 1,000 gene expression estimates. The genes are ranked by their cross-condition mean (adjusted by the normalization factors). The posterior FC tends to shrink genes with low expressions (small rank); in this case the differences are minor. \newpage \subsubsection{Checking convergence} \label{sec:detailedgenedeconverge} As detailed in Section \ref{sec:model}, we assume the prior distribution of $q_g^C$ is $Beta(\alpha,\beta)$. The EM algorithm is used to estimate the hyper-parameters $\alpha,\beta$ and the mixture parameter $p$. The optimized parameters at each iteration may be obtained as follows (recall we are using 5 iterations for demonstration purposes): <<>>= EBOut$Alpha EBOut$Beta EBOut$P @ In this case the differences between the 4th and 5th iterations are always less than 0.01. \subsubsection{Checking the model fit and other diagnostics} \label{sec:detailedgenedeplot} As noted in Leng {\it et al.}, 2013 \cite{Leng13}, EBSeq relies on parametric assumptions that should be checked following each analysis. The \verb+QQP+ function may be used to assess prior assumptions. In practice, \verb+QQP+ generates the Q-Q plot of the empirical $q$'s vs. the simulated $q$'s from the Beta prior distribution with estimated hyper-parameters. Figure \ref{fig:GeneQQ} shows that the data points lie on the $y=x$ line for both conditions, which indicates that the Beta prior is appropriate. \begin{figure}[h!] \centering <>= par(mfrow=c(1,2)) QQP(EBOut) @ \caption{QQ-plots for checking the assumption of a Beta prior (upper panels) as well as the model fit using data from condition 1 and condition 2 (lower panels)} \label{fig:GeneQQ} \end{figure} \newpage \noindent Likewise, the \verb+DenNHist+ function may be used to check the density plot of empirical $q$'s vs the simulated $q$'s from the fitted Beta prior distribution. Figure \ref{fig:GeneDenNHist} also shows our estimated distribution fits the data very well. \begin{figure}[h!] \centering <>= par(mfrow=c(1,2)) DenNHist(EBOut) @ \caption{Density plots for checking the model fit using data from condition 1 and condition 2} \label{fig:GeneDenNHist} \end{figure} \newpage \subsection{Isoform level DE analysis (two conditions)} \label{sec:detailedisode} \subsubsection{The $I_g$ vector} \label{sec:detailedisodeNg} Since EBSeq fits rely on $I_g$, we need to obtain the $I_g$ for each isoform. This can be done using the function \verb+GetNg+. The required inputs of \verb+GetNg+ are the isoform names (\verb+IsoformNames+) and their corresponding gene names (\verb+IsosGeneNames+), described above. In the simulated data, we assume that the isoforms in the $I_g=1$ group belong to genes \verb+Gene_1, ... , Gene_200+; The isoforms in the $I_g=2$ group belong to genes \verb+Gene_201, ..., Gene_400+; and isoforms in the $I_g=3$ group belong to \verb+Gene_401, ..., Gene_600+. <>= data(IsoList) IsoMat=IsoList$IsoMat IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames NgList=GetNg(IsoNames, IsosGeneNames, TrunThre=3) @ <<>>= names(NgList) IsoNgTrun=NgList$IsoformNgTrun IsoNgTrun[c(1:3,201:203,601:603)] @ The output of \verb+GetNg+ contains 4 vectors. \verb+GeneNg+ (\verb+IsoformNg+) provides the number of isoforms $N_g$ within each gene (within each isoform's host gene). \verb+GeneNgTrun+ (\verb+IsoformNgTrun+) provides the $I_g$ group assignments. The default number of groups is 3, which means the isoforms with $N_g$ greater than 3 will be assigned to $I_g=3$ group. We use 3 in the case studies since the number of isoforms with $N_g$ larger than 3 is relatively small and the small sample size may induce poor parameter fitting if we treat them as separate groups. In practice, if there is evidence that the $N_g=4,5,6...$ groups should be treated as separate groups, a user can change \verb+TrunThre+ to define a different truncation threshold. \subsubsection{Using mappability ambiguity clusters instead of the $I_g$ vector when the gene-isoform relationship is unknown} \label{sec:detailedisodeNoNg} When working with a de-novo assembled transcriptome, in which case the gene-isoform relationship is unknown, a user can use read mapping ambiguity cluster information instead of Ng, as provided by RSEM \cite{Li11b} in the output file \verb+output_name.ngvec+. The file contains a vector with the same length as the total number of transcripts. Each transcript has been assigned to one of 3 levels (1, 2, or 3) to indicate the mapping uncertainty level of that transcript. The mapping ambiguity clusters are partitioned via a k-means algorithm on the unmapability scores that are provided by RSEM. A user can read in the mapping ambiguity cluster information using: <>= IsoNgTrun = scan(file="output_name.ngvec", what=0, sep="\n") @\\ Where \verb+"output_name.ngvec"+ is the output file obtained from RSEM function rsem-generate-ngvector. More details on using the RSEM-EBSeq pipeline on de novo assembled transcriptomes can be found at \url{http://deweylab.biostat.wisc.edu/rsem/README.html#de}. Other unmappability scores and other cluster methods (e.g. Gaussian Mixed Model) could also be used to form the uncertainty clusters. \subsubsection{Running EBSeq on simulated isoform expression estimates} \label{sec:detailedisoderun} EBSeq can be applied as described in Section \ref{sec:startisoderun}. <>= IsoSizes=MedianNorm(IsoMat) IsoEBOut=EBTest(Data=IsoMat, NgVector=IsoNgTrun, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=IsoSizes, maxround=5) IsoEBDERes=GetDEResults(IsoEBOut, FDR=0.05) @ <<>>= str(IsoEBDERes) @ \noindent We see that EBSeq found 104 DE isoforms at a target FDR of 0.05. The function \verb+PostFC+ could also be used here to calculate the Fold Change (FC) as well as the posterior FC on the normalization factor adjusted data. <<>>= IsoFC=PostFC(IsoEBOut) str(IsoFC) @ \subsubsection{Checking convergence} \label{sec:detailedisodeconverge} For isoform level data, we assume the prior distribution of $q_{gi}^C$ is $Beta(\alpha,\beta^{I_g})$. As in Section \ref{sec:detailedgenedeconverge}, the optimized parameters at each iteration may be obtained as follows (recall we are using 5 iterations for demonstration purposes): <<>>= IsoEBOut$Alpha IsoEBOut$Beta IsoEBOut$P @ Here we have 3 $\beta$'s in each iteration corresponding to $\beta^{I_g=1},\beta^{I_g=2},\beta^{I_g=3}$. We see that parameters are changing less than $10^{-2}$ or $10^{-3}$. In practice, we require changes less than $10^{-3}$ to declare convergence. \subsubsection{Checking the model fit and other diagnostics} \label{sec:detailedisodeplot} In Leng {\it et al.}, 2013\citep{Leng13}, we showed the mean-variance differences across different isoform groups on multiple data sets. In practice, if it is of interest to check differences among isoform groups defined by truncated $I_g$ (such as those shown here in Figure 1), the function \verb+PolyFitPlot+ may be used. The following code generates the three panels shown in Figure \ref{fig:IsoSimuNgEach} (if condition 2 is of interest, a user could change each \verb+C1+ to \verb+C2+.): \begin{figure}[h!] \centering <>= par(mfrow=c(2,2)) PolyFitValue=vector("list",3) for(i in 1:3) PolyFitValue[[i]]=PolyFitPlot(IsoEBOut$C1Mean[[i]], IsoEBOut$C1EstVar[[i]],5) @ \caption{ The mean-variance fitting plot for each Ng group} \label{fig:IsoSimuNgEach} \end{figure} \newpage Superimposing all $I_g$ groups using the code below will generate the figure (shown here in Figure \ref{fig:IsoSimuNg}), which is similar in structure to Figure 1: \begin{figure}[h!] \centering <>= PolyAll=PolyFitPlot(unlist(IsoEBOut$C1Mean), unlist(IsoEBOut$C1EstVar),5) lines(log10(IsoEBOut$C1Mean[[1]][PolyFitValue[[1]]$sort]), PolyFitValue[[1]]$fit[PolyFitValue[[1]]$sort],col="yellow",lwd=2) lines(log10(IsoEBOut$C1Mean[[2]][PolyFitValue[[2]]$sort]), PolyFitValue[[2]]$fit[PolyFitValue[[2]]$sort],col="pink",lwd=2) lines(log10(IsoEBOut$C1Mean[[3]][PolyFitValue[[3]]$sort]), PolyFitValue[[3]]$fit[PolyFitValue[[3]]$sort],col="green",lwd=2) legend("topleft",c("All Isoforms","Ng = 1","Ng = 2","Ng = 3"), col=c("red","yellow","pink","green"),lty=1,lwd=3,box.lwd=2) @ \caption{The mean-variance plot for each Ng group} \label{fig:IsoSimuNg} \end{figure} \newpage \noindent To generate a QQ-plot of the fitted Beta prior distribution and the $\hat{q}^C$'s within condition, a user may use the following code to generate 6 panels (as shown in Figure \ref{fig:IsoQQ}). \begin{figure}[h!] \centering <>= par(mfrow=c(2,3)) QQP(IsoEBOut) @ \caption{ QQ-plots of the fitted prior distributions within each condition and each Ig group} \label{fig:IsoQQ} \end{figure} \newpage \noindent And in order to produce the plot of the fitted Beta prior densities and the histograms of $\hat{q}^C$'s within each condition, the following may be used (it generates Figure \ref{fig:IsoDenNHist}): \begin{figure}[h] \centering <>= par(mfrow=c(2,3)) DenNHist(IsoEBOut) @ \caption{ Prior distribution fit within each condition and each Ig group. (Note only a small set of isoforms are considered here for demonstration. Better fitting should be expected while using full set of isoforms.)} \label{fig:IsoDenNHist} \end{figure} \clearpage \subsection{Gene level DE analysis (more than two conditions)} \label{sec:detailedmulticond} As described in Section \ref{sec:startmulticond}, the function \verb+GetPatterns+ allows the user to generate all possible patterns given the conditions. To visualize the patterns, the function \verb+PlotPattern+ may be used. \begin{figure}[h!] \centering <>= Conditions=c("C1","C1","C2","C2","C3","C3") PosParti=GetPatterns(Conditions) PosParti PlotPattern(PosParti) @ \caption{ All possible patterns} \label{fig:Patterns} \end{figure} \newpage \noindent If we were interested in Patterns 1, 2, 4 and 5 only, we'd define: <<>>= Parti=PosParti[-3,] Parti @ \noindent Moving on to the analysis, \verb+MedianNorm+ or one of its competitors should be used to determine the normalization factors. Once this is done, the formal test is performed by \verb+EBMultiTest+. <>= data(MultiGeneMat) MultiSize=MedianNorm(MultiGeneMat) MultiOut=EBMultiTest(MultiGeneMat, NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) @ \noindent The posterior probability of being in each pattern for every gene is obtained using the function \verb+GetMultiPP+: <<>>= MultiPP=GetMultiPP(MultiOut) names(MultiPP) MultiPP$PP[1:10,] MultiPP$MAP[1:10] MultiPP$Patterns @ \noindent where \verb+MultiPP$PP+ provides the posterior probability of being in each pattern for every gene. \verb+MultiPP$MAP+ provides the most likely pattern of each gene based on the posterior probabilities. \verb+MultiPP$Patterns+ provides the details of the patterns. The FC and posterior FC for multiple condition data can be obtained by the function \verb+GetMultiFC+: <<>>= MultiFC=GetMultiFC(MultiOut) str(MultiFC) @ \noindent To generate a QQ-plot of the fitted Beta prior distribution and the $\hat{q}^C$'s within condition, a user could also use function \verb+DenNHist+ and \verb+QQP+. \begin{figure}[h!] \centering <>= par(mfrow=c(2,2)) QQP(MultiOut) @ \caption{ QQ-plots of the fitted prior distributions within each condition and each Ig group} \label{fig:GeneMultiQQ} \end{figure} \begin{figure}[h] \centering <>= par(mfrow=c(2,2)) DenNHist(MultiOut) @ \caption{ Prior distributions fit within each condition. (Note only a small set of genes are considered here for demonstration. Better fitting should be expected while using full set of genes.)} \label{fig:GeneMultiDenNHist} \end{figure} \newpage \clearpage \newpage \subsection{Isoform level DE analysis (more than two conditions)} \label{sec:detailedisomulticond} Similar to Section \ref{sec:startmulticond}, the function \verb+GetPatterns+ allows a user to generate all possible patterns given the conditions. To visualize the patterns, the function \verb+PlotPattern+ may be used. <<>>= Conditions=c("C1","C1","C2","C2","C3","C3","C4","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond @ \newpage \begin{figure}[h!] \centering <>= PlotPattern(PosParti.4Cond) Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond @ \caption{All possible patterns for 4 conditions} \label{fig:Patterns4Cond} \end{figure} \newpage <>= data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiSize=MedianNorm(IsoMultiMat) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun IsoMultiOut=EBMultiTest(IsoMultiMat,NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize, maxround=5) IsoMultiPP=GetMultiPP(IsoMultiOut) @ <<>>= names(MultiPP) IsoMultiPP$PP[1:10,] IsoMultiPP$MAP[1:10] IsoMultiPP$Patterns IsoMultiFC=GetMultiFC(IsoMultiOut) @ The FC and posterior FC for multiple condition data can be obtained by the function \verb+GetMultiFC+: \noindent To generate a QQ-plot of the fitted Beta prior distribution and the $\hat{q}^C$'s within condition, a user could also use the functions \verb+DenNHist+ and \verb+QQP+. \newpage \begin{figure}[h!] \centering <>= par(mfrow=c(3,4)) QQP(IsoMultiOut) @ \caption{ QQ-plots of the fitted prior distributions within each condition and Ig group. (Note only a small set of isoforms are considered here for demonstration. Better fitting should be expected while using full set of isoforms.)} \label{fig:IsoMultiQQ} \end{figure} \begin{figure}[h] \centering <>= par(mfrow=c(3,4)) DenNHist(IsoMultiOut) @ \caption{ Prior distributions fit within each condition and Ig group. (Note only a small set of isoforms are considered here for demonstration. Better fitting should be expected while using full set of isoforms.)} \label{fig:IsoMultiDenNHist} \end{figure} \clearpage \newpage \newpage \subsection{Working without replicates} When replicates are not available, it is difficult to estimate the transcript specific variance. In this case, EBSeq estimates the variance by pooling similar genes together. Specifically, we take genes with FC in the 25\% - 75\% quantile of all FC's as candidate genes. By defining \verb+NumBin = 1000+ (default in \verb+EBTest+), EBSeq will group genes with similar means into 1,000 bins. For each candidate gene, we use the across-condition variance estimate as its variance estimate. For each bin, the bin-wise variance estimation is taken to be the median of the across-condition variance estimates of the candidate genes within that bin. For each non-candidate gene, we use the bin-wise variance estimate of the host bin (the bin containing this gene) as its variance estimate. This approach works well when there are no more than 50\% DE genes in the data set. \subsubsection{Gene counts with two conditions} \label{sec:norepgenede} To generate a data set with no replicates, we take the first sample of each condition. For example, using the data from Section \ref{sec:detailedgenede}, we take sample 1 from condition 1 and sample 6 from condition 2. Functions \verb+MedianNorm+, \verb+GetDEResults+ and \verb+PostFC+ may be used on data without replicates. <<>>= data(GeneMat) GeneMat.norep=GeneMat[,c(1,6)] Sizes.norep=MedianNorm(GeneMat.norep) EBOut.norep=EBTest(Data=GeneMat.norep, Conditions=as.factor(rep(c("C1","C2"))), sizeFactors=Sizes.norep, maxround=5) EBDERes.norep=GetDEResults(EBOut.norep) GeneFC.norep=PostFC(EBOut.norep) @ \subsubsection{Isoform counts with two conditions} \label{norepisode} To generate an isoform level data set with no replicates, we also take sample 1 and sample 6 in the data we used in Section \ref{sec:detailedisode}. Example codes are shown below. <<>>= data(IsoList) IsoMat=IsoList$IsoMat IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoMat.norep=IsoMat[,c(1,6)] IsoSizes.norep=MedianNorm(IsoMat.norep) IsoEBOut.norep=EBTest(Data=IsoMat.norep, NgVector=IsoNgTrun, Conditions=as.factor(c("C1","C2")), sizeFactors=IsoSizes.norep, maxround=5) IsoEBDERes.norep=GetDEResults(IsoEBOut.norep) IsoFC.norep=PostFC(IsoEBOut.norep) @ \subsubsection{Gene counts with more than two conditions} \label{norepisode} To generate a data set with multiple conditions and no replicates, we take the first sample from each condition (sample 1, 3 and 5) in the data we used in Section \ref{sec:detailedmulticond}. Example codes are shown below. <<>>= data(MultiGeneMat) MultiGeneMat.norep=MultiGeneMat[,c(1,3,5)] Conditions=c("C1","C2","C3") PosParti=GetPatterns(Conditions) Parti=PosParti[-3,] MultiSize.norep=MedianNorm(MultiGeneMat.norep) MultiOut.norep=EBMultiTest(MultiGeneMat.norep, NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize.norep, maxround=5) MultiPP.norep=GetMultiPP(MultiOut.norep) MultiFC.norep=GetMultiFC(MultiOut.norep) @ \subsubsection{Isoform counts with more than two conditions} \label{sec:norepmulticond} To generate an isoform level data set with multiple conditions and no replicates, we take the first sample from each condition (sample 1, 3, 5 and 7) in the data we used in Section \ref{sec:detailedisomulticond}. Example codes are shown below. <<>>= data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiMat.norep=IsoMultiMat[,c(1,3,5,7)] IsoMultiSize.norep=MedianNorm(IsoMultiMat.norep) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C2","C3","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond IsoMultiOut.norep=EBMultiTest(IsoMultiMat.norep, NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize.norep, maxround=5) IsoMultiPP.norep=GetMultiPP(IsoMultiOut.norep) IsoMultiFC.norep=GetMultiFC(IsoMultiOut.norep) @ \section{EBSeq pipelines and extensions} \subsection{RSEM-EBSeq pipeline: from raw reads to differential expression analysis results} EBSeq is coupled with RSEM \cite{Li11b} as an RSEM-EBSeq pipeline which provides quantification and DE testing on both gene and isoform levels. For more details, see \url{http://deweylab.biostat.wisc.edu/rsem/README.html#de} \subsection{EBSeq interface: A user-friendly graphical interface for differetial expression analysis} EBSeq interface provides a graphical interface implementation for users who are not familiar with the R programming language. It takes .xls, .xlsx and .csv files as input. Additional packages need be downloaded; they may be found at \url{http://www.biostat.wisc.edu/~ningleng/EBSeq_Package/EBSeq_Interface/} \subsection{EBSeq Galaxy tool shed} EBSeq tool shed contains EBSeq wrappers for a local Galaxy implementation. For more details, see \url{http://www.biostat.wisc.edu/~ningleng/EBSeq_Package/EBSeq_Galaxy_toolshed/} \section{Acknowledgment} We would like to thank Haolin Xu for checking the package and proofreading the vignette. \section{News} 2014-1-30: In EBSeq 1.3.3, the default setting of EBTest function will remove low expressed genes (genes whose 75th quantile of normalized counts is less than 10) before identifying DE genes. These two thresholds can be changed in EBTest function. Because low expressed genes are disproportionately noisy, removing these genes prior to downstream analyses can improve model fitting and increase robustness (e.g. by removing outliers). 2014-5-22: In EBSeq 1.5.2, numerical approximations are implemented to deal with underflow. The underflow is likely due to large number of samples. 2015-1-29: In EBSeq 1.7.1, EBSeq incorporates a new function GetDEResults() which may be used to obtain a list of transcripts under a target FDR in a two-condition experiment. The results obtained by applying this function with its default setting will be more robust to transcripts with low variance and potential outliers. By using the default settings in this function, the number of genes identified in any given analysis may differ slightly from the previous version (1.7.0 or order). To obtain results that are comparable to results from earlier versions of EBSeq (1.7.0 or older), a user may set Method="classic" in GetDEResults() function, or use the original GetPPMat() function. The GeneDEResults() function also allows a user to modify thresholds to target genes/isoforms with a pre-specified posterior fold change. Also, in EBSeq 1.7.1, the default settings in EBTest() and EBMultiTest() function will only remove transcripts with all 0's (instead of removing transcripts with 75th quantile less than 10 in version 1.3.3-1.7.0). To obtain a list of transcripts comparable to the results generated by EBSeq version 1.3.3-1.7.0, a user may change Qtrm = 0.75 and QtrmCut = 10 when applying EBTest() or EBMultiTest() function. \section{Common Q and A} \subsection{Read in data} csv file: \verb+In=read.csv("FileName", stringsAsFactors=F, row.names=1, header=T)+ \verb+Data=data.matrix(In)+ \noindent txt file: \verb+In=read.table("FileName", stringsAsFactors=F, row.names=1, header=T)+ \verb+Data=data.matrix(In)+ \noindent Check \verb+str(Data)+ and make sure it is a matrix instead of data frame. You may need to play around with the \verb+row.names+ and \verb+header+ option depends on how the input file was generated. \subsection{GetDEResults() function not found} You may on an earlier version of EBSeq. The GetDEResults function was introduced since version 1.7.1. The latest release version could be found at: \url{http://www.bioconductor.org/packages/release/bioc/html/EBSeq.html} \noindent The latest devel version: \url{http://www.bioconductor.org/packages/devel/bioc/html/EBSeq.html} \noindent And you may check your package version by typing \verb+packageVersion("EBSeq")+. \subsection{Visualizing DE genes/isoforms} To generate a heatmap, you may consider the heatmap.2 function in gplots package. For example, you may run \verb+heatmap.2(NormalizedMatrix[GenesOfInterest,], scale="row", trace="none", Colv=F)+ The normalized matrix may be obtained from \verb+GetNormalizedMat()+ function. \subsection{My favorite gene/isoform has NA in PP (status "NoTest")} \indent The NoTest status comes from two sources: 1) In version 1.3.3-1.7.0, using the default parameter settings of EBMultiTest(), the function will not test on genes with more than 75\% values $\le$ 10 to ensure better model fitting. To disable this filter, you may set Qtrm=1 and QtrmCut=0. 2) numerical over/underflow in R. That happens when the within condition variance is extremely large or small. we did implemented a numerical approximation step to calculate the approximated PP for these genes with over/underflow. Here we use $10^{-10}$ to approximate the parameter p in the NB distribution for these genes (we set it to a small value since we want to cover more over/underflow genes with low within-condition variation). 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