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Deconvolution_functions.R
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#Deconvolution Functions
#install these packages if necessary...
if (!require("quadprog")) {
install.packages("quadprog", dependencies = TRUE, repos="http://cran.r-project.org")
}
if (!require("reshape")) {
install.packages("reshape", dependencies = TRUE, repos="http://cran.r-project.org")
}
if (!require("e1071")) {
install.packages("e1071", dependencies = TRUE, repos="http://cran.r-project.org")
}
if (!require("Seurat")) {
install.packages("Seurat", dependencies = TRUE, repos="http://cran.r-project.org")
}
if (!require("ROCR")) {
install.packages("ROCR", dependencies = TRUE, repos="http://cran.r-project.org")
}
if (!require("varhandle")) {
install.packages("varhandle", dependencies = TRUE, repos="http://cran.r-project.org")
}
if (!require("MAST")) {
source("https://bioconductor.org/biocLite.R")
biocLite("MAST")
}
#load packages
library(quadprog)
library(reshape)
library(e1071)
library(Seurat)
library(ROCR)
library(varhandle)
library(MAST)
#trim bulk and single-cell data to contain the same genes
trimData<-function(Signature,bulkData){
Genes<-intersect(rownames(Signature),names(bulkData))
B<-bulkData[Genes]
S<-Signature[Genes,]
return(list("sig"=S,"bulk"=B))
}
#solve using OLS, constrained such that cell type numbers>0
solveOLS<-function(S,B){
D<-t(S)%*%S
d<-t(S)%*%B
A<-cbind(diag(dim(S)[2]))
bzero<-c(rep(0,dim(S)[2]))
solution<-solve.QP(D,d,A,bzero)$solution
names(solution)<-colnames(S)
print(round(solution/sum(solution),5))
return(solution/sum(solution))
}
#return cell number, not proportion
#do not print output
solveOLSInternal<-function(S,B){
D<-t(S)%*%S
d<-t(S)%*%B
A<-cbind(diag(dim(S)[2]))
bzero<-c(rep(0,dim(S)[2]))
solution<-solve.QP(D,d,A,bzero)$solution
names(solution)<-colnames(S)
return(solution)
}
#solve using WLS with weights dampened by a certain dampening constant
solveDampenedWLS<-function(S,B){
#first solve OLS, use this solution to find a starting point for the weights
solution<-solveOLSInternal(S,B)
#now use dampened WLS, iterate weights until convergence
iterations<-0
changes<-c()
#find dampening constant for weights using cross-validation
j<-findDampeningConstant(S,B,solution)
change<-1
while(change>.01 & iterations<1000){
newsolution<-solveDampenedWLSj(S,B,solution,j)
#decrease step size for convergence
solutionAverage<-rowMeans(cbind(newsolution,matrix(solution,nrow = length(solution),ncol = 4)))
change<-norm(as.matrix(solutionAverage-solution))
solution<-solutionAverage
iterations<-iterations+1
changes<-c(changes,change)
}
print(round(solution/sum(solution),5))
return(solution/sum(solution))
}
#solve WLS given a dampening constant
solveDampenedWLSj<-function(S,B,goldStandard,j){
multiplier<-1*2^(j-1)
sol<-goldStandard
ws<-as.vector((1/(S%*%sol))^2)
wsScaled<-ws/min(ws)
wsDampened<-wsScaled
wsDampened[which(wsScaled>multiplier)]<-multiplier
W<-diag(wsDampened)
D<-t(S)%*%W%*%S
d<- t(S)%*%W%*%B
A<-cbind(diag(dim(S)[2]))
bzero<-c(rep(0,dim(S)[2]))
sc <- norm(D,"2")
solution<-solve.QP(D/sc,d/sc,A,bzero)$solution
names(solution)<-colnames(S)
return(solution)
}
#find a dampening constant for the weights using cross-validation
findDampeningConstant<-function(S,B,goldStandard){
solutionsSd<-NULL
#goldStandard is used to define the weights
sol<-goldStandard
ws<-as.vector((1/(S%*%sol))^2)
wsScaled<-ws/min(ws)
wsScaledMinusInf<-wsScaled
#ignore infinite weights
if(max(wsScaled)=="Inf"){
wsScaledMinusInf<-wsScaled[-which(wsScaled=="Inf")]
}
#try multiple values of the dampening constant (multiplier)
#for each, calculate the variance of the dampened weighted solution for a subset of genes
for (j in 1:ceiling(log2(max(wsScaledMinusInf)))){
multiplier<-1*2^(j-1)
wsDampened<-wsScaled
wsDampened[which(wsScaled>multiplier)]<-multiplier
solutions<-NULL
seeds<-c(1:100)
for (i in 1:100){
set.seed(seeds[i]) #make nondeterministic
subset<-sample(length(ws),size=length(ws)*0.5) #randomly select half of gene set
#solve dampened weighted least squares for subset
fit = lm (B[subset] ~ -1+S[subset,],weights=wsDampened[subset])
sol<-fit$coef*sum(goldStandard)/sum(fit$coef)
solutions<-cbind(solutions,sol)
}
solutionsSd<-cbind(solutionsSd,apply(solutions,1,sd))
}
#choose dampening constant that results in least cross-validation variance
j<-which.min(colMeans(solutionsSd^2))
return(j)
}
solveSVR<-function(S,B){
#scaling
ub=max(c(as.vector(S),B)) #upper bound
lb=min(c(as.vector(S),B)) #lower bound
Bs=((B-lb)/ub)*2-1
Ss=((S-lb)/ub)*2-1
#perform SVR
model<-svm(Ss,Bs, nu=0.5,scale = TRUE, type = "nu-regression",kernel ="linear",cost = 1)
coef <- t(model$coefs) %*% model$SV
coef[which(coef<0)]<-0
coef<-as.vector(coef)
names(coef)<-colnames(S)
print(round(coef/sum(coef),5))
return(coef/sum(coef))
}
#perform DE analysis using Seurat
DEAnalysis<-function(scdata,id,path){
exprObj<-CreateSeuratObject(raw.data=as.data.frame(scdata), project = "DE")
exprObj2<-SetIdent(exprObj,ident.use=as.vector(id))
print("Calculating differentially expressed genes:")
for (i in unique(id)){
de_group <- FindMarkers(object=exprObj2, ident.1 = i, ident.2 = NULL,
only.pos = TRUE, test.use = "bimod")
save(de_group,file=paste(path,"/de_",i,".RData",sep=""))
}
}
#build signature matrix using genes identified by DEAnalysis()
buildSignatureMatrixUsingSeurat<-function(scdata,id,path,diff.cutoff=0.5,pval.cutoff=0.01){
#perform differential expression analysis
DEAnalysis(scdata,id,path)
numberofGenes<-c()
for (i in unique(id)){
load(file=paste(path,"/de_",i,".RData",sep=""))
DEGenes<-rownames(de_group)[intersect(which(de_group$p_val_adj<pval.cutoff),which(de_group$avg_logFC>diff.cutoff))]
nonMir = grep("MIR|Mir", DEGenes, invert = T)
assign(paste("cluster_lrTest.table.",i,sep=""),de_group[which(rownames(de_group)%in%DEGenes[nonMir]),])
numberofGenes<-c(numberofGenes,length(DEGenes[nonMir]))
}
#need to reduce number of genes
#for each subset, order significant genes by decreasing fold change, choose between 50 and 200 genes
#choose matrix with lowest condition number
conditionNumbers<-c()
for(G in 50:200){
Genes<-c()
j=1
for (i in unique(id)){
if(numberofGenes[j]>0){
temp<-paste("cluster_lrTest.table.",i,sep="")
temp<-as.name(temp)
temp<-eval(parse(text = temp))
temp<-temp[order(temp$p_val_adj,decreasing=TRUE),]
Genes<-c(Genes,(rownames(temp)[1:min(G,numberofGenes[j])]))
}
j=j+1
}
Genes<-unique(Genes)
#make signature matrix
ExprSubset<-scdata[Genes,]
Sig<-NULL
for (i in unique(id)){
Sig<-cbind(Sig,(apply(ExprSubset,1,function(y) mean(y[which(id==i)]))))
}
colnames(Sig)<-unique(id)
conditionNumbers<-c(conditionNumbers,kappa(Sig))
}
G<-which.min(conditionNumbers)+min(49,numberofGenes-1) #G is optimal gene number
#
Genes<-c()
j=1
for (i in unique(id)){
if(numberofGenes[j]>0){
temp<-paste("cluster_lrTest.table.",i,sep="")
temp<-as.name(temp)
temp<-eval(parse(text = temp))
temp<-temp[order(temp$p_val_adj,decreasing=TRUE),]
Genes<-c(Genes,(rownames(temp)[1:min(G,numberofGenes[j])]))
}
j=j+1
}
Genes<-unique(Genes)
ExprSubset<-scdata[Genes,]
Sig<-NULL
for (i in unique(id)){
Sig<-cbind(Sig,(apply(ExprSubset,1,function(y) mean(y[which(id==i)]))))
}
colnames(Sig)<-unique(id)
save(Sig,file=paste(path,"/Sig.RData",sep=""))
return(Sig)
}
##alternative differential expression method using MAST
#functions for DE
Mean.in.log2space=function(x,pseudo.count) {
return(log2(mean(2^(x)-pseudo.count)+pseudo.count))
}
stat.log2=function(data.m, group.v, pseudo.count){
#data.m=data.used.log2
log2.mean.r <- aggregate(t(data.m), list(as.character(group.v)), function(x) Mean.in.log2space(x,pseudo.count))
log2.mean.r <- t(log2.mean.r)
colnames(log2.mean.r) <- paste("mean.group",log2.mean.r[1,], sep="")
log2.mean.r = log2.mean.r[-1,]
log2.mean.r = as.data.frame(log2.mean.r)
log2.mean.r = varhandle::unfactor(log2.mean.r) #from varhandle
log2.mean.r[,1] = as.numeric(log2.mean.r[,1])
log2.mean.r[,2] = as.numeric(log2.mean.r[,2])
log2_foldchange = log2.mean.r$mean.group1-log2.mean.r$mean.group0
results = data.frame(cbind(log2.mean.r$mean.group0,log2.mean.r$mean.group1,log2_foldchange))
colnames(results) = c("log2.mean.group0","log2.mean.group1","log2_fc")
rownames(results) = rownames(log2.mean.r)
return(results)
}
v.auc = function(data.v,group.v) {
prediction.use=prediction(data.v, group.v, 0:1)
perf.use=performance(prediction.use,"auc")
auc.use=round(perf.use@y.values[[1]],3)
return(auc.use)
}
m.auc=function(data.m,group.v) {
AUC=apply(data.m, 1, function(x) v.auc(x,group.v))
AUC[is.na(AUC)]=0.5
return(AUC)
}
#perform DE analysis using MAST
DEAnalysisMAST<-function(scdata,id,path){
pseudo.count = 0.1
data.used.log2 <- log2(scdata+pseudo.count)
colnames(data.used.log2)<-make.unique(colnames(data.used.log2))
diff.cutoff=0.5
for (i in unique(id)){
cells.symbol.list2 = colnames(data.used.log2)[which(id==i)]
cells.coord.list2 = match(cells.symbol.list2, colnames(data.used.log2))
cells.symbol.list1 = colnames(data.used.log2)[which(id != i)]
cells.coord.list1 = match(cells.symbol.list1, colnames(data.used.log2))
data.used.log2.ordered = cbind(data.used.log2[,cells.coord.list1], data.used.log2[,cells.coord.list2])
group.v <- c(rep(0,length(cells.coord.list1)), rep(1, length(cells.coord.list2)))
#ouput
log2.stat.result <- stat.log2(data.used.log2.ordered, group.v, pseudo.count)
Auc <- m.auc(data.used.log2.ordered, group.v)
bigtable <- data.frame(cbind(log2.stat.result, Auc))
DE <- bigtable[bigtable$log2_fc >diff.cutoff,]
dim(DE)
if(dim(DE)[1]>1){
data.1 = data.used.log2[,cells.coord.list1]
data.2 = data.used.log2[,cells.coord.list2]
genes.list = rownames(DE)
log2fold_change = cbind(genes.list, DE$log2_fc)
colnames(log2fold_change) = c("gene.name", "log2fold_change")
counts = as.data.frame(cbind( data.1[genes.list,], data.2[genes.list,] ))
groups = c(rep("Cluster_Other", length(cells.coord.list1) ), rep(i, length(cells.coord.list2) ) )
groups = as.character(groups)
data_for_MIST <- as.data.frame(cbind(rep(rownames(counts), dim(counts)[2]), melt(counts),rep(groups, each = dim(counts)[1]), rep(1, dim(counts)[1] * dim(counts)[2]) ))
colnames(data_for_MIST) = c("Gene", "Subject.ID", "Et", "Population", "Number.of.Cells")
vbeta = data_for_MIST
vbeta.fa <-FromFlatDF(vbeta, idvars=c("Subject.ID"),
primerid='Gene', measurement='Et', ncells='Number.of.Cells',
geneid="Gene", cellvars=c('Number.of.Cells', 'Population'),
phenovars=c('Population'), id='vbeta all')
vbeta.1 <- subset(vbeta.fa,Number.of.Cells==1)
# .3 MAST
head(colData(vbeta.1))
zlm.output <- zlm(~ Population, vbeta.1, method='bayesglm', ebayes=TRUE)
show(zlm.output)
coefAndCI <- summary(zlm.output, logFC=TRUE)
zlm.lr <- lrTest(zlm.output, 'Population')
zlm.lr_pvalue <- melt(zlm.lr[,,'Pr(>Chisq)'])
zlm.lr_pvalue <- zlm.lr_pvalue[which(zlm.lr_pvalue$test.type == 'hurdle'),]
lrTest.table <- merge(zlm.lr_pvalue, DE, by.x = "primerid", by.y = "row.names")
colnames(lrTest.table) <- c("Gene", "test.type", "p_value", paste("log2.mean.", "Cluster_Other", sep=""), paste("log2.mean.",i,sep=""), "log2fold_change", "Auc")
cluster_lrTest.table <- lrTest.table[rev(order(lrTest.table$Auc)),]
#. 4 save results
write.csv(cluster_lrTest.table, file=paste(path,"/",i,"_lrTest.csv", sep=""))
save(cluster_lrTest.table, file=paste(path,"/",i,"_MIST.RData", sep=""))
}
}
}
#build signature matrix using genes identified by DEAnalysisMAST()
buildSignatureMatrixMAST<-function(scdata,id,path,diff.cutoff=0.5,pval.cutoff=0.01){
#compute differentially expressed genes for each cell type
DEAnalysisMAST(scdata,id,path)
#for each cell type, choose genes in which FDR adjusted p-value is less than 0.01 and the estimated fold-change
#is greater than 0.5
numberofGenes<-c()
for (i in unique(id)){
if(file.exists(paste(path,"/",i,"_MIST.RData", sep=""))){
load(file=paste(path,"/",i,"_MIST.RData", sep=""))
pvalue_adjusted<-p.adjust(cluster_lrTest.table[,3], method = "fdr", n = length(cluster_lrTest.table[,3]))
cluster_lrTest.table<-cbind(cluster_lrTest.table,pvalue_adjusted)
DEGenes<-cluster_lrTest.table$Gene[intersect(which(pvalue_adjusted<pval.cutoff),which(cluster_lrTest.table$log2fold_change>diff.cutoff))]
nonMir = grep("MIR|Mir", DEGenes, invert = T) # because Mir gene is usually not accurate
assign(paste("cluster_lrTest.table.",i,sep=""),cluster_lrTest.table[which(cluster_lrTest.table$Gene%in%DEGenes[nonMir]),])
numberofGenes<-c(numberofGenes,length(DEGenes[nonMir]))
}
}
#need to reduce number of genes
#for each subset, order significant genes by decreasing fold change, choose between 50 and 200 genes
#for each, iterate and choose matrix with lowest condition number
conditionNumbers<-c()
for(G in 50:200){
Genes<-c()
j=1
for (i in unique(id)){
if(numberofGenes[j]>0){
temp<-paste("cluster_lrTest.table.",i,sep="")
temp<-as.name(temp)
temp<-eval(parse(text = temp))
temp<-temp[order(temp$log2fold_change,decreasing=TRUE),]
Genes<-c(Genes,varhandle::unfactor(temp$Gene[1:min(G,numberofGenes[j])]))
}
j=j+1
}
Genes<-unique(Genes)
#make signature matrix
ExprSubset<-scdata[Genes,]
Sig<-NULL
for (i in unique(id)){
Sig<-cbind(Sig,(apply(ExprSubset,1,function(y) mean(y[which(id==i)]))))
}
colnames(Sig)<-unique(id)
conditionNumbers<-c(conditionNumbers,kappa(Sig))
}
G<-which.min(conditionNumbers)+min(49,numberofGenes-1)
Genes<-c()
j=1
for (i in unique(id)){
if(numberofGenes[j]>0){
temp<-paste("cluster_lrTest.table.",i,sep="")
temp<-as.name(temp)
temp<-eval(parse(text = temp))
temp<-temp[order(temp$log2fold_change,decreasing=TRUE),]
Genes<-c(Genes,varhandle::unfactor(temp$Gene[1:min(G,numberofGenes[j])]))
}
j=j+1
}
Genes<-unique(Genes)
ExprSubset<-scdata[Genes,]
Sig<-NULL
for (i in unique(id)){
Sig<-cbind(Sig,(apply(ExprSubset,1,function(y) mean(y[which(id==i)]))))
}
colnames(Sig)<-unique(id)
save(Sig,file=paste(path,"/Sig.RData",sep=""))
return(Sig)
}