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SpatialGeneFilterScriptV2beta.Rmd
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SpatialGeneFilterScriptV2beta.Rmd
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---
title: "R Notebook"
output: html_notebook
---
networkExpressionFile <- readRDS("/Volumes/castelo-branco-group/David/SingleCellData/ELISA10xanalysis/AggrGeneFocus.rds")
networkAnnotableFile <- readRDS("/Volumes/castelo-branco-group/David/SingleCellData/ELISA10xanalysis/AggrGeneFocusAnno.rds")
networkExpressionFile <- read.table("~/Downloads/ens_neurons_sten.csv", sep=",",header=TRUE,as.is=TRUE,check.names=FALSE)
row.names(networkExpressionFile) <- networkExpressionFile[,1]
networkExpressionFile <- networkExpressionFile[,-1]
networkAnnotableFile <- savenetworkanno
networkExpressionFile <- savenetworkdf[,row.names(subset(networkAnnotableFile, networkAnnotableFile$`substring(colnames(emat), 18)` %in% c("3","4")))]
library(scater, quietly = TRUE)
emat_10x.qc <- newSCESet(countData = networkExpressionFile)
emat_10x.qc <- scran::computeSumFactors(emat_10x.qc,positive=TRUE)
emat_10x.qc <- scater::normalize(emat_10x.qc)
filtercells <- apply(exprs(emat_10x.qc),2,function(x) anyNA(x))
filtercells <- exprs(emat_10x.qc)[,!filtercells]
networkExpressionFile <- filtercells
#subsetEAE OL Cntrl Gioele
networkExpressionFile <- networkExpressionFile[,networkAnnotableFile$Group %in% c("EAE")]
networkAnnotableFile <- networkAnnotableFile[colnames(networkExpressionFile),]
networkExpressionFile <- networkExpressionFile[,networkAnnotableFile$L1Clusters %in% c("1","2")]
networkAnnotableFile <- networkAnnotableFile[colnames(networkExpressionFile),]
load("~/Documents/SingleCellData/Networkclustering/OLsMS/OL_patient_121_networkAnnotableFile.Robj")
load("~/Documents/SingleCellData/Networkclustering/OLsMS/OL_patient_121_networkExpressionFile.Robj")
load("~/Documents/SingleCellData/Networkclustering/OLsMS/OL_patient_122_networkAnnotableFile.Robj")
load("~/Documents/SingleCellData/Networkclustering/OLsMS/OL_patient_122_networkExpressionFile.Robj")
load("~/Documents/SingleCellData/Networkclustering/OLsMS/OL_patient_176_networkAnnotableFile.Robj")
load("~/Documents/SingleCellData/Networkclustering/OLsMS/OL_patient_176_networkExpressionFile.Robj")
networkExpressionFile <- cbind(networkExpressionFile_121,networkExpressionFile_122,networkExpressionFile_176)
networkAnnotableFile <- as.data.frame(rbind(networkAnnotableFile_121,networkAnnotableFile_122,networkAnnotableFile_176))
rm(networkAnnotableFile_121,networkAnnotableFile_122,networkAnnotableFile_176,networkExpressionFile_121,networkExpressionFile_122,networkExpressionFile_176)
Optional, after L1 clustering remove clusters that seem to be doublets or otherwise bad quality cells
```{r}
networkExpressionFile <- networkExpressionFile[,names(subset(SCN3Elevel1,! SCN3Elevel1 %in% c("1")))]
networkAnnotableFile <- networkAnnotableFile[colnames(networkExpressionFile),]
```
Filter out NaN and Na
```{r}
#mt-
#Mt-
#MT-
#see if you grep something
table(grepl("ERCC-",rownames(networkExpressionFile)))
networkExpressionFile <- networkExpressionFile[! grepl("ERCC-",rownames(networkExpressionFile)),]
#see if you grep something
table(grepl("MT-",rownames(networkExpressionFile)))
networkExpressionFile <- networkExpressionFile[! grepl("mt-",rownames(networkExpressionFile)),]
filtercells <- apply(networkExpressionFile,2,function(x) anyNA(x))
networkExpressionFile <- as.matrix(networkExpressionFile[,!filtercells])
filtercells <- apply(networkExpressionFile,2,function(x) any(x=="NaN"))
networkExpressionFile <- as.matrix(networkExpressionFile[,!filtercells])
networkAnnotableFile <- networkAnnotableFile[colnames(networkExpressionFile),]
rm(filtercells)
networkExpressionFile <- networkExpressionFile-min(networkExpressionFile)
```
Remove genes that are only expressed in a few cells
```{r}
expr_limit <- mean(networkExpressionFile)
expressed_genes <- 1*apply(networkExpressionFile,1,function(x) (x) > expr_limit)
expressed_genes <- apply(expressed_genes,2,function(x) sum(x) >= 1)
networkExpressionFile <- networkExpressionFile[expressed_genes,]
rm(expressed_genes)
```
Feature selection
```{r}
CVsq <- function(x) {(sd(x)/mean(x))^2}
expr_limit <- mean(networkExpressionFile)
emat_expressed <- apply(networkExpressionFile,1,function(x) any ((x) > expr_limit))
emat_expressed <- networkExpressionFile[emat_expressed,]
CV2 <- apply(emat_expressed,1,CVsq)
meancounts <- apply(emat_expressed,1,mean)
CV2_matrix <- matrix(c(log(meancounts),log(CV2+1)),nrow = length(CV2),ncol = 2)
row.names(CV2_matrix) <- names(CV2)
colnames(CV2_matrix) <- c("LogExpr","CV2")
CV2_matrix <- as.data.frame(CV2_matrix)
library(e1071)
model <- svm(CV2 ~ LogExpr ,CV2_matrix)#, gamma = 0.06)
predictedY <- predict(model, CV2_matrix)
CV2_matrix <- cbind(CV2_matrix,predictedY)
CV2_matrix$RelativeVariance <-CV2_matrix$CV2 - CV2_matrix$predictedY
NormGenes <- row.names(subset(CV2_matrix, RelativeVariance >= -0))
NormGenesVar <- apply(networkExpressionFile,1,function(x) var(x))
#NormGenes <- row.names(networkExpressionFile[NormGenesVar > median(NormGenesVar),])
NormGenesFactor <- as.factor(1*(CV2_matrix$RelativeVariance >= -0))
#NormGenesFactor<- as.factor(1*(row.names(CV2_matrix) %in% NormGenes))
palette("default")
plot(CV2_matrix$LogExpr,CV2_matrix$CV2,xlab ="Mean Count Magnitude (log)",ylab="Squared Coefficient of Variation", pch=20, cex=0.3, col=NormGenesFactor)
points(CV2_matrix$LogExpr,predictedY, col="green",pch=20, cex=0.1)
length(NormGenes)
NormGenes <- subset(NormGenes,! NormGenes %in% c("Xist","Tsix"))
featureselection <- NormGenes
SCN3Egeneset <- featureselection
#SCN3Egeneset <- intersect(featureselection,L1L2markers)
#SCN3Egeneset <- L1L2markers
rm(CV2,CV2_matrix,emat_expressed,meancounts,model,NormGenes,NormGenesFactor,predictedY,SCN3Egeneset)
write(featureselection,file="featureselection.txt")
```
```{r}
library(Matrix)
savenetworkdf <- as.matrix(networkExpressionFile,"sparse=TRUE")
savenetworkanno <- networkAnnotableFile
#networkExpressionFile <- savenetworkdf
featureselection <- row.names(networkExpressionFile)
featureselection <- subset(featureselection,! featureselection %in% c("Xist","Tsix"))
```
#Spatially filter genes with auto bootstrapping
```{r, message=FALSE}
gc()
usemagic <- "FALSE"
whentobootstrap <- 10000 #cellnumber
howmanyboots <- 10+1 #bootstrap repetitions
samplesize <- 2000 #how many cells to take for sampling
#networkExpressionFile <- networkExpressionFilepremagic
threshold <- 2 #use mean spatial correlation of geneset of the featureselection (1) or after the first round of filtering (2) (more strict, which is default)
SCN3Egeneset <- featureselection
originalnetworkdf <- networkExpressionFile
if(ncol(networkExpressionFile)>whentobootstrap){
originalnetworkdf <- networkExpressionFile
bootstrap<-1
sample<-1
moransIsampled <- as.character(NULL)
}
if(ncol(networkExpressionFile)<=whentobootstrap)
{
bootstrap<-howmanyboots-1
sample<-0
}
while(bootstrap<howmanyboots){
SCN3Egeneset <- featureselection
iter=0
meanMoran<-0
if(sample==1){
networkExpressionFile <- originalnetworkdf[,sample(ncol(originalnetworkdf), samplesize) ]
print(paste("bootstrapping...","round",bootstrap))
}
OldGeneset <- as.character(seq_len(100000))
while(length(OldGeneset) != length(SCN3Egeneset) ){
iter=iter+1
library(amap)
#library(MASS)
#library(destiny)
library(dpt)
#library(Matrix)
#library(diffusionMap)
print(paste("Preparing genefiltering on", length(SCN3Egeneset), "genes."))
print("Making celllandscape...Filtering possible duplicated cells in original file")
#if(length(OldGeneset)==100000)
#{
# pca <- prcomp(t(log(networkExpressionFile[featureselection,]+1)),scale. = FALSE)
# cd_diffusionplot <-pca$x[,c(1:30)]
# }
#if(length(OldGeneset)!=100000){cd_diffusionplot <- t(log(networkExpressionFile[SCN3Egeneset,]+1))}
if(length(SCN3Egeneset) > 4000){
pca <- prcomp(t(log(networkExpressionFile[SCN3Egeneset,]+1)),scale. = FALSE)
ncompGF <- 50
if(length(SCN3Egeneset) < 50) {ncompGF <- length(SCN3Egeneset)}
cd_diffusionplot <-pca$x[,c(1:which(cumsum(pca$sdev[1:ncompGF])/sum(pca$sdev[1:ncompGF]) > 0.6)[1])]
}
if(length(SCN3Egeneset) <= 4000){
cd_diffusionplot <-t(log(networkExpressionFile[SCN3Egeneset,]+1))
}
cd_diffusionplot <- cd_diffusionplot[!duplicated(cd_diffusionplot),]
print("Making celllandscape...Making diffusionmap")
gc()
ts <- Transitions(cd_diffusionplot,k=20)
#Gene filtering
print(paste("Filtering", length(SCN3Egeneset), "genes."))
OldGeneset <- SCN3Egeneset
NetworkDist <- as.matrix(ts@transitions)
#NetworkDist <- distcells*distcells2
colnames(NetworkDist) <- row.names(cd_diffusionplot)
row.names(NetworkDist) <- row.names(cd_diffusionplot)
emat_expressed <- apply(networkExpressionFile,1,function(x) any ((x) >expr_limit))
emat_expressed <- networkExpressionFile[emat_expressed,row.names(NetworkDist)]
emat_expressed <- as.matrix(emat_expressed[intersect(row.names(emat_expressed),OldGeneset),])
if(usemagic=="TRUE"){
emat_expressed <- magic(ts,emat_expressed[,row.names(cd_diffusionplot)], power = 1, k = 3, n_eigs = 20, n_local = 10)
}
library(doParallel)
library(foreach)
library(spdep)
cores <- detectCores()
cl <- makeCluster(cores)
registerDoParallel(cl)
SCN3Egenefilter <- matrix(nrow=nrow(emat_expressed),ncol = 3)
cellnames <- colnames(emat_expressed)
spatial.weights <- mat2listw(NetworkDist)
numbsim <- 1
test <- foreach(i=1:nrow(emat_expressed),.packages="spdep",.combine=cbind) %dopar%
{
x <- emat_expressed[i,]
r <- row.names(emat_expressed)[i]
m <- c(unlist(moran.mc(x,spatial.weights,nsim=numbsim,zero.policy = TRUE))[1:3],r)
}
stopCluster(cl)
SCN3Egenefilter <- t(test)
SCN3Egenefilter <- SCN3Egenefilter[! apply(SCN3Egenefilter,1,function(x) any(x=="NaN")),]
row.names(SCN3Egenefilter) <- SCN3Egenefilter[,4]
SCN3Egenefilter_clean <- as.data.frame(SCN3Egenefilter[complete.cases(SCN3Egenefilter),c(1:3)])
r <- row.names(SCN3Egenefilter_clean)
SCN3Egenefilter_clean <- apply(SCN3Egenefilter_clean,2,function(x) as.numeric(x))
row.names(SCN3Egenefilter_clean) <- r
#if(meanMoran==0){meanMoran<-mean(SCN3Egenefilter_clean[,1])}
if(iter<=threshold){meanMoran<-mean(SCN3Egenefilter_clean[,1],na.rm=TRUE)}
SCN3Egeneset <- row.names(SCN3Egenefilter_clean[SCN3Egenefilter_clean[,1] >= meanMoran,])
}
bootstrap <- bootstrap+1
if(sample==1){
moransIsampled <- c(moransIsampled,SCN3Egeneset)
}
}
networkExpressionFile <- originalnetworkdf
GeneMarkov1 <- SCN3Egeneset
if(sample==1){GeneMarkov1 <- unique(moransIsampled)}
#L1geneset <- GeneMarkov
rm(emat_expressed,NetworkDist,SCN3Egenefilter,test,cellnames,cl,cores,i,iter,meanMoran,numbsim,OldGeneset,r,SCN3Egeneset,spatial.weights)
print("Done with part one... Starting part two...")
gc()
SCN3Egeneset <- featureselection
originalnetworkdf <- networkExpressionFile
if(ncol(networkExpressionFile)>whentobootstrap){
bootstrap<-1
sample<-1
moransIsampled <- as.character(NULL)
}
if(ncol(networkExpressionFile)<=whentobootstrap)
{
bootstrap<-howmanyboots-1
sample<-0
}
while(bootstrap<howmanyboots){
SCN3Egeneset <- featureselection
iter=0
meanMoran<-0
if(sample==1){
networkExpressionFile <- originalnetworkdf[,sample(ncol(originalnetworkdf), samplesize) ]
print(paste("bootstrapping...","round",bootstrap))
}
OldGeneset <- as.character(seq_len(100000))
while(length(unique(GeneMarkov1)) < (length(SCN3Egeneset)) ){
iter=iter+1
library(amap)
#library(MASS)
#library(destiny)
library(dpt)
#library(Matrix)
#library(diffusionMap)
print(paste("Preparing genefiltering on", length(SCN3Egeneset), "genes."))
print("Making celllandscape...Filtering possible duplicated cells in original file")
#if(length(OldGeneset)==100000)
#{
# pca <- prcomp(t(log(networkExpressionFile[featureselection,]+1)),scale. = FALSE)
# cd_diffusionplot <-pca$x[,c(1:30)]
# }
#if(length(OldGeneset)!=100000){cd_diffusionplot <- t(log(networkExpressionFile[SCN3Egeneset,]+1))}
pca <- prcomp(t(log(networkExpressionFile[SCN3Egeneset,]+1)),scale. = FALSE)
ncompGF <- 50
if(length(SCN3Egeneset) < 50) {ncompGF <- length(SCN3Egeneset)}
cd_diffusionplot <-pca$x[,c(1:which(cumsum(pca$sdev[1:ncompGF])/sum(pca$sdev[1:ncompGF]) > 0.4)[1])]
#cd_diffusionplot <- t(log(networkExpressionFile[SCN3Egeneset,]+1))
cd_diffusionplot <- cd_diffusionplot[!duplicated(cd_diffusionplot),]
print("Making celllandscape...Making diffusionmap")
gc()
ts <- Transitions(cd_diffusionplot,k=20)
#Gene filtering
print(paste("Filtering", length(SCN3Egeneset), "genes."))
OldGeneset <- SCN3Egeneset
NetworkDist <- as.matrix(ts@transitions)
#NetworkDist <- distcells*distcells2
colnames(NetworkDist) <- row.names(cd_diffusionplot)
row.names(NetworkDist) <- row.names(cd_diffusionplot)
emat_expressed <- apply(networkExpressionFile,1,function(x) any ((x) >expr_limit))
emat_expressed <- networkExpressionFile[emat_expressed,row.names(NetworkDist)]
emat_expressed <- as.matrix(emat_expressed[intersect(row.names(emat_expressed),OldGeneset),])
if(usemagic=="TRUE"){
emat_expressed <- magic(ts,emat_expressed[,row.names(cd_diffusionplot)], power = 1, k = 3, n_eigs = 20, n_local = 10)
}
library(doParallel)
library(foreach)
library(spdep)
cores <- detectCores()
cl <- makeCluster(cores)
registerDoParallel(cl)
SCN3Egenefilter <- matrix(nrow=nrow(emat_expressed),ncol = 3)
cellnames <- colnames(emat_expressed)
spatial.weights <- mat2listw(NetworkDist)
numbsim <- 1
networkdist <- 1-NetworkDist
test <- foreach(i=1:nrow(emat_expressed),.packages="spdep",.combine=cbind) %dopar%
{
x <- emat_expressed[i,]
x <- x[x>expr_limit]
if(length(x)<2){}
else{
r <- row.names(emat_expressed)[i]
y <- networkdist[names(x),names(x)]
a <- mean(y)
y <- nrow(y)
m <- c(a,y,r)
}
}
stopCluster(cl)
SCN3Egenefilter <- t(test)
SCN3Egenefilter <- SCN3Egenefilter[! apply(SCN3Egenefilter,1,function(x) any(x=="NaN")),]
row.names(SCN3Egenefilter) <- SCN3Egenefilter[,3]
SCN3Egenefilter_clean <- as.data.frame(SCN3Egenefilter[complete.cases(SCN3Egenefilter),c(1:2)])
r <- row.names(SCN3Egenefilter_clean)
SCN3Egenefilter_clean <- apply(SCN3Egenefilter_clean,2,function(x) as.numeric(x))
row.names(SCN3Egenefilter_clean) <- r
SCN3Egenefilter_clean2 <- SCN3Egenefilter_clean
SCN3Egenefilter_clean2[,1] <- log(SCN3Egenefilter_clean2[,1]+1)
library(e1071)
model <- svm(V1 ~ V2 ,SCN3Egenefilter_clean2,gamma = 10)
predictedY <- predict(model, SCN3Egenefilter_clean2)
SCN3Egenefilter_clean2 <- SCN3Egenefilter_clean2[names(predictedY),]
CV2_matrix <- as.data.frame(cbind(SCN3Egenefilter_clean2,predictedY))
CV2_matrix$RelativeVariance <-CV2_matrix[,1] - CV2_matrix[,3]
SCN3Egeneset <- row.names(subset(CV2_matrix, RelativeVariance < 0))
}
bootstrap <- bootstrap+1
if(sample==1){
moransIsampled <- c(moransIsampled,SCN3Egeneset)
}
}
networkExpressionFile <- originalnetworkdf
GeneMarkov <- unique(c(SCN3Egeneset,GeneMarkov1))
if(sample==1){GeneMarkov <- unique(c(moransIsampled,GeneMarkov1))}
L1geneset <- GeneMarkov
rm(emat_expressed,NetworkDist,SCN3Egenefilter,test,cellnames,cl,cores,i,iter,meanMoran,numbsim,OldGeneset,r,SCN3Egeneset,spatial.weights)
print("Making final celllandscape...Filtering possible duplicated cells in original file")
#cd_diffusionplot <- t(log(networkExpressionFile[GeneMarkov,]+1))
if(length(GeneMarkov) > 4000){
pca <- prcomp(t(log(networkExpressionFile[GeneMarkov,]+1)),scale. = FALSE)
ncompGF <- 50
if(length(GeneMarkov) < 50) {ncompGF <- length(GeneMarkov)}
cd_diffusionplot <-pca$x[,c(1:which(cumsum(pca$sdev[1:ncompGF])/sum(pca$sdev[1:ncompGF]) > 0.6)[1])]
}
if(length(GeneMarkov) <= 4000){
cd_diffusionplot <-t(log(networkExpressionFile[GeneMarkov,]+1))
}
cd_diffusionplot <- cd_diffusionplot[!duplicated(cd_diffusionplot),]
#library(Matrix)
#cd_diffusionplot <- Matrix(cd_diffusionplot, sparse = TRUE)
print("Making final celllandscape...Making diffusionmap. This can take a while...")
ts <- Transitions(cd_diffusionplot,k=20)
if(usemagic=="TRUE"){
networkExpressionFilepremagic <- networkExpressionFile
networkExpressionFile <- magic(ts,networkExpressionFile[,row.names(cd_diffusionplot)], power = 1, k = 3, n_eigs = 20, n_local = 10)
}
print("Done.")
#save(networkExpressionFile,networkAnnotableFile,expr_limit,featureselection,GeneMarkov,GeneMarkov1,L1geneset,ts,file="~/Documents/SingleCellData/Networkclustering/L1saved.rds")
write(GeneMarkov,file="FilteredGenesEAE.txt")
```
Estimate the number of Level1 classes, using cluster estimation and tSNE, try diffent numbers of PCA components
```{r}
#load("~/Documents/SingleCellData/Networkclustering/L1saved.rds")
gc()
SCN3Egeneset <- GeneMarkov
library(factoextra)
pca <- prcomp(as.matrix(ts@transitions),scale. = FALSE)
plot(pca$sdev[1:50],pch = 20,
xlab = 'Diffusion component (DC)', ylab = 'sdev')
ncompL1 <- which(cumsum(pca$sdev[1:50])/sum(pca$sdev[1:50]) > 0.6)[1]
genemodulePCA <- cor(t(log(networkExpressionFile+1)),pca$x[,1:20],method="pearson")
genemodulePCAz <- scale(genemodulePCA)
Networkdist <- as.matrix(ts@transitions)
#Networkdist <- pca$x[,c(1:ncompL1)]
#row.names(Networkdist) <- row.names(cd_diffusionplot)
#Networkdist <- Networkdist[colnames(clusterdata),]
#Networkdist <- as.matrix(dist(Networkdist,method="manhattan"))
#Networkdist <- as.matrix(Dist(pca$x[,c(1:ncompL1)],method = "manhattan",nbproc = 8))
colnames(Networkdist) <- row.names(cd_diffusionplot)
row.names(Networkdist) <- row.names(cd_diffusionplot)
#colnames(Networkdist) <- colnames(networkExpressionFile)
#row.names(Networkdist) <- colnames(networkExpressionFile)
library(WGCNA)
#range01 <- function(x){(x-min(x))/(max(x)-min(x))}
#Networkdist <- range01(Networkdist)
#adjacency <- as_adj(testMNE, sparse = FALSE, attr = "Pearson")
#TOM = TOMsimilarity(adjacency)
#Networkdist <- as.dist(1-adjacency)
TOM = TOMsimilarity(Networkdist)
library(igraph)
L1graph <- graph_from_adjacency_matrix(Networkdist,weighted = TRUE,mode="undirected")
L1graphSupercells <- cluster_louvain(L1graph,weights=E(L1graph)$weight)
table(L1graphSupercells$membership)
SCN3Elevel1 <- as.factor(L1graphSupercells$membership)
names(SCN3Elevel1) <- L1graphSupercells$names
SCN3Elevel1 <- SCN3Elevel1[intersect(row.names(cd_diffusionplot),names(SCN3Elevel1))]
SCN3Ecluster <- SCN3Elevel1
Networkdist <- as.dist(1-as.matrix(ts@transitions))
#fviz_nbclust(pca$x[,c(1:ncompL1)],diss=dist(pca$x[,c(1:ncompL1)],method="manhattan"), hcut, method = "silhouette",hc_method = "ward.D2",k.max = 40)
```
```{r}
#View(as.matrix(ts@transitions))
#Networkdist <- 1*(as.matrix(ts@transitions) > 0)
L1graphSupercells <- cluster_walktrap(L1graph,weights=E(L1graph)$weight,step=50,merges=TRUE)
table(L1graphSupercells$membership)
#L1graphSupercells <- cluster_fast_greedy(L1graph,weights=E(L1graph)$weight)
#table(L1graphSupercells$membership)
SCN3Elevel1 <- as.factor(L1graphSupercells$membership)
names(SCN3Elevel1) <- L1graphSupercells$names
SCN3Ecluster <- SCN3Elevel1[colnames(networkExpressionFile)]
Networkdist <- as.dist(1-Networkdist)
```
Generate tSNE coordinates
```{r, message=FALSE, warning=FALSE, include=FALSE}
pcatsne <- prcomp(t(log(networkExpressionFile[GeneMarkov,]+1)),scale. = FALSE)
ncompLtsne <- which(cumsum(pcatsne$sdev[1:50])/sum(pcatsne$sdev[1:50]) > 0.8)[1]
tsne <- t(pcatsne$x[,c(1:ncompLtsne)])
#tsne <- t(pca$x[which(colnames(networkExpressionFile) %in% names(SCN3Ecluster)),c(1:ncompL1)])
#tsne <- Networkdist
#tsne <- 1-TOM
#tsne <- log(networkExpressionFile[L1L2markers,]+1)
#tsne <- z$H[-c(29,31:33),]
library(Rtsne)
set.seed(123456789)
tsne_out <- Rtsne(t(tsne), initial_dims=100, perplexity=round(7+(0.01*ncol(networkExpressionFile)),0),max_iter = 1000,check_duplicates = FALSE,
verbose = TRUE, theta = 0.5, pca = FALSE)
#rownames(tsne_out$Y) <- colnames(tsne)
```
Do Level1 clustering and split the dataset for downstream clustering
```{r, fig.height=10}
library(heatmap3)
library(viridis)
clusterdata <- as.data.frame(t((1*(apply(networkExpressionFile[GeneMarkov,names(SCN3Ecluster)],1,function(x) (x) >expr_limit)))))
clusterdata <- clusterdata[apply(clusterdata,1,function(x) var(x) > 0),apply(clusterdata,2,function(x) var(x) > 0)]
library(cluster)
distpca <- pca$x[,c(1:ncompL1)]
row.names(distpca) <- row.names(cd_diffusionplot)
distpca <- distpca[colnames(clusterdata),]
#SCN3E_hc <- hclust(dist(distpca,method="manhattan"), method = "ward.D2")
SCN3E_hc <- hclust(as.dist(1-TOM), method = "average")
#SCN3E_hc <- hclust(as.dist(networkdist[colnames(clusterdata),colnames(clusterdata)]), method = "ward.D2")
#SCN3Ecluster <- rbind(groups = cutree(SCN3E_hc, k=20))
#names(SCN3Ecluster) <- row.names(distpca)
pcagene <- prcomp(log(networkExpressionFile[row.names(clusterdata),]+1),scale. = FALSE)
plot(pcagene$sdev[1:50],pch = 20,
xlab = 'Diffusion component (DC)', ylab = 'sdev')
pcageneexpression <- pcagene$x[,c(1:which(cumsum(pcagene$sdev[1:100])/sum(pcagene$sdev[1:100]) > 0.2)[1])]
SCN3E_hc_gene <- hclust(dist(pcageneexpression,method="manhattan"), method = "ward.D2")
SCN3Egenecluster <- rbind(groups = cutree(SCN3E_hc_gene, k=15))
names(SCN3Egenecluster) <- row.names(pcageneexpression)
#View(t(as.data.frame(SCN3Egenecluster)))
#GeneMarkovUnfiltered <- GeneMarkov
#GeneMarkov <- GeneMarkovUnfiltered
#GeneMarkov <- subset(GeneMarkov, GeneMarkov %in% setdiff(GeneMarkov,names(SCN3Egenecluster[SCN3Egenecluster == 8])))
cd_diffusionplotgenes <-t(log(networkExpressionFile[row.names(clusterdata),]+1))
cd_diffusionplotgenes <- cd_diffusionplotgenes[!duplicated(cd_diffusionplotgenes),]
tsgenes <- Transitions(t(cd_diffusionplotgenes),k=20)
library(igraph)
Networkdistgenes <- as.matrix(tsgenes@transitions)
colnames(Networkdistgenes) <- colnames(cd_diffusionplotgenes)
row.names(Networkdistgenes) <- colnames(cd_diffusionplotgenes)
genegraph <- graph_from_adjacency_matrix(Networkdistgenes,weighted = TRUE,mode="undirected")
genegraphSupercells <- cluster_louvain(genegraph,weights=E(genegraph)$weight)
table(genegraphSupercells$membership)
SCN3Egenecluster <- as.factor(genegraphSupercells$membership)
names(SCN3Egenecluster) <- genegraphSupercells$names
library(WGCNA)
TOMgenes = TOMsimilarity(Networkdistgenes)
SCN3E_hc_gene <- hclust(as.dist(1-TOMgenes), method = "average")
#
# library("dendsort")
#
# SCN3E_hc <- dendsort(SCN3E_hc, type="min")
# SCN3E_hc_gene <- dendsort(SCN3E_hc_gene, type="min")
library(cba)
SCN3E_hc$merge <- order.optimal(dist(pca$x[,c(1:ncompL1)],method="manhattan"), SCN3E_hc$merge)$merge
SCN3E_hc_gene$merge <- order.optimal(dist(pcageneexpression,method="manhattan"), SCN3E_hc_gene$merge)$merge
#library(ReorderCluster)
#SCN3E_hc <- RearrangeJoseph(SCN3E_hc, as.dist(1-TOM),SCN3Ecluster,cpp=1)
#SCN3E_hc_gene <- RearrangeJoseph(SCN3E_hc_gene, as.dist(1-TOMgenes),SCN3Egenecluster,cpp=1)
#library(cba)
#SCN3E_hc <- order.optimal(as.dist(1-TOM), SCN3E_hc$merge)
paletteGenes <- rainbow(length(levels(as.factor(SCN3Egenecluster))))
colsGenes <- paletteGenes[as.factor(SCN3Egenecluster)]
palette <- rainbow(length(levels(as.factor(SCN3Ecluster))))
cols <- palette[as.factor(SCN3Ecluster)]
palette <- rainbow(length(levels(droplevels(as.factor(networkAnnotableFile$Renamed_clusternames)))))
colsDTA <- palette[as.factor(networkAnnotableFile$Renamed_clusternames)]
cols <- cbind(cols,colsDTA)
#cols <- palette[as.factor(networkAnnotableFile$Experiment)]
#pdf("~/Documents/SingleCellData/IdoDAM/L1clusteringheatmapNew.pdf",width = 20,height = 30,useDingbats=FALSE)
heatmap3(log(clusterdata+1),RowSideColors=colsGenes,ColSideColors=cols, Rowv = as.dendrogram(SCN3E_hc_gene) , Colv = as.dendrogram(SCN3E_hc) ,scale = "none",symm = F, method = "ward.D2",col=viridis(1024),balanceColor =F,cexRow = 0.4)
legend("topright", legend=levels(as.factor(SCN3Egenecluster)),
col=paletteGenes,
pch=19)
#pdf("~/Documents/SingleCellData/IdoDAM/L1clusteringtSNE.pdf",width = 10,height = 6,useDingbats=FALSE)
palette <- rainbow(length(levels(as.factor(SCN3Ecluster))))
cols <- palette[as.factor(SCN3Ecluster)]
plot(tsne_out$Y,col=cols,pch=20,cex=3)
legend("topright", legend=levels(as.factor(SCN3Ecluster)),
col=palette,
pch=19,cex=2)
palette <- rainbow(length(levels(droplevels(as.factor(networkAnnotableFile[colnames(networkExpressionFile),]$Renamed_clusternames)))))
cols <- palette[droplevels(as.factor(networkAnnotableFile[colnames(networkExpressionFile),]$Renamed_clusternames))]
plot(tsne_out$Y,col=cols,pch=20,cex=5)
legend("topright", legend=levels(droplevels(as.factor(networkAnnotableFile[colnames(networkExpressionFile),]$Renamed_clusternames))),
col=palette,
pch=19)
palette <- rainbow(length(levels(droplevels(as.factor(networkAnnotableFile[names(SCN3Ecluster),]$Group)))))
cols <- palette[droplevels(as.factor(networkAnnotableFile[names(SCN3Ecluster),]$Group))]
plot(tsne_out$Y,col=cols,pch=20,cex=5)
legend("topright", legend=levels(droplevels(as.factor(networkAnnotableFile[names(SCN3Ecluster),]$Group))),
col=palette,
pch=19)
palette <- rainbow(length(levels(as.factor(networkAnnotableFile$LineageTrace))))
cols <- palette[as.factor(networkAnnotableFile$LineageTrace)]
plot(tsne_out$Y,col=cols,pch=20,cex=2)
legend("topright", legend=levels(as.factor(networkAnnotableFile$LineageTrace)),
col=palette,
pch=19)
SCN3Elevel1 <- as.factor(SCN3Ecluster)
table(SCN3Elevel1)
```
Visualize expression
```{r, fig.width=5}
library(ggplot2)
Expression <- as.data.frame(t(log(networkExpressionFile+1)))
#Expression <- as.data.frame(t(z$H))
ggplot(as.data.frame(tsne_out$Y), aes(x=V1, y=V2, colour=Expression$Mog)) +
geom_point(size=3) + geom_density2d(alpha=0.0,size=0.5, color="black", bins=10) + scale_colour_gradient(low = "blue", high = "yellow") +
theme(strip.background = element_blank(), strip.text.x = element_blank(),axis.text.x = element_blank(),axis.text.y= element_blank(),axis.ticks= element_blank(),axis.line = element_blank(),panel.border = element_blank(),legend.position = "right") +
xlab("tSNE_1") + ylab("tSNE_2")+theme_bw() +
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
```
First we generate markers for each of the clusters
```{r, fig.width=30}
library(MAST)
library(NMF)
networkExpressionFilesub <- networkExpressionFile[GeneMarkov,names(SCN3Ecluster)]
networkAnnotableFilesub <- networkAnnotableFile[names(SCN3Ecluster),]
networkAnnotableFilesub$Clustering <- SCN3Elevel1[colnames(networkExpressionFilesub)]
networkExpressionFilesub <- networkExpressionFilesub[,networkAnnotableFilesub$Clustering %in% c("8","5")]
networkAnnotableFilesub <- networkAnnotableFilesub[colnames(networkExpressionFilesub),]
diffclusters <- droplevels(as.factor(SCN3Elevel1[colnames(networkExpressionFilesub)]))
names(diffclusters) <- colnames(networkExpressionFilesub)
table(diffclusters)
NetworkMASTobj <- FromMatrix(networkExpressionFilesub, as.data.frame(as.character(diffclusters)))
thres <- thresholdSCRNACountMatrix(assay(NetworkMASTobj), nbins = 10, min_per_bin = 10,bin_by = "median",data_log = FALSE)#,adj = 1)
assays(NetworkMASTobj) <- list(thresh=thres$counts_threshold, tpm=assay(NetworkMASTobj))
cdr2 <-colSums(assay(NetworkMASTobj)>mean(networkExpressionFile))
colData(NetworkMASTobj)$cngeneson <- scale(cdr2)
options(mc.cores=1)
zlmCond <- zlm(~diffclusters+cngeneson, NetworkMASTobj)
show(zlmCond)
zlm.lr <- lrTest(zlmCond, 'diffclusters')
FDR <- p.adjust(zlm.lr[,,'Pr(>Chisq)'][,3],"fdr")
diffgenes <- names(FDR[FDR < 0.01])
sigtest <- diffgenes
summaryDt <- summary(zlmCond)
test <- print(summaryDt,n=nrow(NetworkMASTobj))
row.names(test) <- test[,1]
rown <- row.names(test)
test <- test[,-c(1,2)]
diffclusters1 <- 0
test <- cbind(diffclusters1,test)
test <- t(apply(test,1,function (x)gsub("*", "", x, fixed = TRUE)))
test <- apply(test,2,as.numeric)
row.names(test) <- rown
DifferentialGenestest <- t(apply(test,1,function(x) x-mean(x,na.rm=TRUE)))
#DifferentialGenestest <- DifferentialGenestest[sigtest,-2]
#DifferentialGenestest <- DifferentialGenestest
markersMAST <- scale(DifferentialGenestest)
marker <- t(scale(t(markersMAST)))
marker <- marker[sigtest,]
markermarkov <- marker[intersect(GeneMarkov,row.names(marker)),]
MarkerGeneList <- subset(row.names(markermarkov),! row.names(markermarkov) %in% c("Xist","Tsix","marker-GFP"))
clusterexpression <- t(rowsum(t(networkExpressionFile[GeneMarkov,]),SCN3Elevel1[colnames(networkExpressionFile)],reorder=TRUE))
clusterexpression <- networkExpressionFile#[GeneMarkov,]
Expression <- networkExpressionFile
diffclusters <- SCN3Elevel1[colnames(networkExpressionFile)]
diffclusters <- droplevels(as.factor(networkAnnotableFile$FinalClusters))
i=1
for(i in 1:length(levels(diffclusters)))
{
x <- Expression[,as.character(diffclusters) %in% levels(droplevels(diffclusters))[i]]
x <- rowMeans(x)
clusterexpression <- cbind(x,clusterexpression)
}
clusterexpression <- clusterexpression[,ncol(clusterexpression)-ncol(Expression):ncol(clusterexpression)]
colnames(clusterexpression) <- levels(droplevels(diffclusters))
minexpr <- mean(clusterexpression)
clusterexpression <- clusterexpression[apply(clusterexpression,1,function(x) any(x>minexpr)),]
clusterexpression <- clusterexpression[apply(t(scale(t(clusterexpression))),1,function(x) any(abs(x)>1.96)),]
clusterexpression <- log(clusterexpression+1)
colnames(clusterexpression) <- levels(droplevels(diffclusters))
markerscl <- scale(t(clusterexpression))
markerscl <- as.data.frame(t(scale(markerscl)))
markers <- as.character(NULL)
for(i in 1:ncol(clusterexpression))
{
sorted <- markerscl[,i]
names(sorted) <- row.names(markerscl)
#sorted <- sorted[GeneMarkov]
sorted <- sort(sorted,decreasing = TRUE)
markers <- c(markers,intersect(names(head(sorted,30)),row.names(markerscl[abs(markerscl[,i])>1,])))
#markers <- c(markers,row.names(sorted[abs(markerscl[,i])>2,]))
}
markers <- unique(markers[!is.na(markers)])
#markers <- markers[!is.na(markers)]
geneselect <- markers
pcacluster <- prcomp(t(markerscl[geneselect,]),scale. = FALSE)
pcaclusterexpression <- pcacluster$x#[,c(1:which(cumsum(pcacluster$sdev[1:ncol(pcacluster$x)])/sum(pcacluster$sdev[1:ncol(pcacluster$x)]) > 0.8)[1])]
SCN3E_hc_cluster <- hclust(dist(pcaclusterexpression,method="manhattan"), method = "ward.D2")
pcagene <- prcomp(markerscl[geneselect,],scale. = FALSE)
pcageneexpression <- pcagene$x[,c(1:which(cumsum(pcagene$sdev[1:ncol(pcagene$x)])/sum(pcagene$sdev[1:ncol(pcagene$x)]) > 0.8)[1])]
SCN3E_hc_gene <- hclust(dist(pcageneexpression,method="manhattan"), method = "ward.D2")
library(cba)
SCN3E_hc_cluster$merge <- order.optimal(dist(pcaclusterexpression,method="manhattan"), SCN3E_hc_cluster$merge)$merge
#SCN3E_hc_gene$merge <- order.optimal(dist(pcageneexpression,method="manhattan"), SCN3E_hc_gene$merge)$merge
# markers <- as.character(NULL)
# for(i in 1:ncol(clusterexpression[,]))
# {
# sorted <- markerscl[,SCN3E_hc_cluster$order[i]]
# names(sorted) <- row.names(markerscl)
# #sorted <- sorted[GeneMarkov]
# sorted <- sort(sorted,decreasing = TRUE)
# markers <- c(markers,intersect(names(head(sorted,50)),row.names(markerscl[abs(markerscl[,SCN3E_hc_cluster$order[i]])>2,])))
# #markers <- c(markers,row.names(sorted[abs(markerscl[,i])>2,]))
# }
# markers <- unique(markers[!is.na(markers)])
# #markers <- markers[!is.na(markers)]
# geneselect <- markers
library(heatmap3)
library(viridis)
heatmap3(clusterexpression[geneselect,], Rowv = NA , Colv = NA ,scale = "row",symm = F, method = "ward.D2",col=viridis(1024),balanceColor =F,cexRow = 3,cexCol = 4)
testwilcox <-pairwise.wilcox.test(t(clusterexpression), as.factor(colnames(clusterexpression)), p.adj = "bonf")
View(testwilcox$p.value)
L1names <- levels(SCN3Elevel1)
####Optional
#Name the clusters
#L1names <-paste(c("OL","NPROG","EVLMC","VLMC","NBLAST"),L1names,sep=".cluster")
#####
L1Markerslist <- MarkerGeneList
save(networkExpressionFile,networkAnnotableFile,GeneMarkov, SCN3Elevel1, tsne_out, markermarkov, DifferentialGenestest, file = "~/Documents/SingleCellData/ELISA10xanalysis/DifferentialExpression/Week1/3VS4.Rdata")
```
Save the files
```{r}
write.table(DifferentialGenestest,file = paste("~/Documents/SingleCellData/IdoDAM/ClustersZ_Lvl1Elisa.txt",sep=""), sep="\t",row.names = TRUE, col.names = TRUE, quote = FALSE)
write.table(test,file = paste("~/Documents/SingleCellData/IdoDAM/ClustersLvl1Elisa.txt",sep=""), sep="\t",row.names = TRUE, col.names = TRUE, quote = FALSE)
```
L2 clustering, Now we split the data and repeat the clustering
Only do this once because it wipes the previous clusterinformation
```{r, fig.height=5}
#Reset genelist
expr_limit <- 0
NetworkClusters <- as.character(NULL)
L2markerlist <- as.character(NULL)
L2markovlist <- as.character(NULL)
namesNetworkClusters <- as.character(NULL)
##########Indicate the clusternumber by setting i
```
Set i to the sub-cluster
```{r}
#i=c("1","2","4","5","6","7","8","9","10","11","12","13","15")
i=c("1")
```
```{r}
threshold <- 3 #use mean spatial correlation of geneset of the featureselection (1) or after the first round of filtering (2) (more strict, which is default)
usemagic <- "FALSE"
boolean <- "FALSE"
networksubset <- networkExpressionFile#[,names(subset(SCN3Elevel1, SCN3Elevel1 %in% i))]
dim(networksubset)
emat_expressed <- apply(networksubset,1,function(x) any ((x) > expr_limit))
clusternumber <- as.numeric(i)
print(paste("Working on cluster",clusternumber, nrow(networksubset),"Genes",ncol(networksubset),"Cells"))
CVsq <- function(x) {(sd(x)/mean(x))^2}
expr_limit <- mean(networkExpressionFile)
emat_expressed <- apply(networksubset,1,function(x) any ((x) > expr_limit))
emat_expressed <- networksubset[emat_expressed,]
CV2 <- apply(emat_expressed,1,CVsq)
meancounts <- apply(emat_expressed,1,mean)
CV2_matrix <- matrix(c(log(meancounts),log(CV2)),nrow = length(CV2),ncol = 2)
row.names(CV2_matrix) <- names(CV2)
colnames(CV2_matrix) <- c("LogExpr","CV2")
CV2_matrix <- as.data.frame(CV2_matrix)
library(e1071)
model <- svm(CV2 ~ LogExpr ,CV2_matrix)#, gamma = 0.06)
predictedY <- predict(model, CV2_matrix)
CV2_matrix <- cbind(CV2_matrix,predictedY)
CV2_matrix$RelativeVariance <-CV2_matrix$CV2 - CV2_matrix$predictedY
NormGenes <- row.names(subset(CV2_matrix, RelativeVariance >= 0))
NormGenesFactor <- as.factor(1*(CV2_matrix$RelativeVariance >= 0))
palette("default")
plot(CV2_matrix$LogExpr,CV2_matrix$CV2,xlab ="Mean Count Magnitude (log)",ylab="Squared Coefficient of Variation", pch=20, cex=0.3, col=NormGenesFactor)
points(CV2_matrix$LogExpr,predictedY, col="green",pch=20, cex=0.1)
length(NormGenes)
NormGenes <- subset(NormGenes,! NormGenes %in% c("Xist","Tsix","eGFP"))
featureselection <- NormGenes
featureselection <- NormGenes
#featureselection <- row.names(networkExpressionFile)
#featureselection <- subset(featureselection,! featureselection %in% c("Xist","Tsix"))
SCN3Egeneset <- featureselection
SCN3Egeneset2 <- featureselection
if(boolean=="TRUE"){networksubset <- 1*as.matrix(networksubset > expr_limit)}
iter=0
meanMoran<-0
OldGeneset <- as.character(seq_len(100000))
while(length(OldGeneset) != length(SCN3Egeneset) ){
iter=iter+1
library(dpt)
if(length(SCN3Egeneset) > 4000){
pca <- prcomp(t(log(networksubset[SCN3Egeneset,]+1)),scale. = FALSE)
ncompGF <- 50
if(length(SCN3Egeneset) < 50) {ncompGF <- length(SCN3Egeneset)}
cd_diffusionplot <-pca$x[,c(1:which(cumsum(pca$sdev[1:ncompGF])/sum(pca$sdev[1:ncompGF]) > 0.6)[1])]
}
if(length(SCN3Egeneset) <= 4000){
cd_diffusionplot <-t(log(networksubset[SCN3Egeneset,]+1))
}
cd_diffusionplot <- cd_diffusionplot[!duplicated(cd_diffusionplot),]
#cd_diffusionplot <- t(log(networksubset[SCN3Egeneset,]+1))
cd_diffusionplot <- cd_diffusionplot[!duplicated(cd_diffusionplot),]
ts <- Transitions(cd_diffusionplot,k=10)
#Gene filtering
print(paste("Filtering", length(SCN3Egeneset), "genes."))
OldGeneset <- SCN3Egeneset
NetworkDist <- as.matrix(ts@transitions)
colnames(NetworkDist) <- row.names(cd_diffusionplot)
row.names(NetworkDist) <- row.names(cd_diffusionplot)
emat_expressed <- apply(networksubset,1,function(x) any ((x) >expr_limit))
emat_expressed <- networksubset[emat_expressed,row.names(NetworkDist)]
emat_expressed <- as.matrix(emat_expressed[intersect(row.names(emat_expressed),OldGeneset),])
if(usemagic=="TRUE"){
emat_expressed <- magic(ts,emat_expressed[,row.names(cd_diffusionplot)], power = 1, k = 3, n_eigs = 20, n_local = 10)
}
library(doParallel)
library(foreach)
library(spdep)
cores <- detectCores()
cl <- makeCluster(cores)
registerDoParallel(cl)
i<-1
SCN3Egenefilter <- matrix(nrow=nrow(emat_expressed),ncol = 3)
cellnames <- colnames(emat_expressed)
spatial.weights <- mat2listw(NetworkDist)
numbsim <- 1
networkdist <- 1-NetworkDist
library(ape)
library(spdep)
test <- foreach(i=1:nrow(emat_expressed),.packages="spdep",.combine=cbind) %dopar%
{
x <- emat_expressed[i,]
r <- row.names(emat_expressed)[i]
m <- c(unlist(moran.mc(x,spatial.weights,nsim=numbsim,zero.policy = TRUE))[1:3],r)
}
stopCluster(cl)
SCN3Egenefilter <- t(test)
SCN3Egenefilter <- SCN3Egenefilter[! apply(SCN3Egenefilter,1,function(x) any(x=="NaN")),]
row.names(SCN3Egenefilter) <- SCN3Egenefilter[,4]
SCN3Egenefilter_clean <- as.data.frame(SCN3Egenefilter[complete.cases(SCN3Egenefilter),c(1:3)])
r <- row.names(SCN3Egenefilter_clean)
SCN3Egenefilter_clean <- apply(SCN3Egenefilter_clean,2,function(x) as.numeric(x))
row.names(SCN3Egenefilter_clean) <- r
#if(meanMoran==0){meanMoran<-mean(SCN3Egenefilter_clean[,1])}
if(iter<=threshold){meanMoran<-mean(SCN3Egenefilter_clean[,1],na.rm=TRUE)}
SCN3Egeneset2 <- row.names(SCN3Egenefilter_clean[SCN3Egenefilter_clean[,1] >= meanMoran,])
SCN3Egeneset <- SCN3Egeneset2
}
expr_limit <- mean(networksubset)
expressed_genes <- 1*apply(networksubset[SCN3Egeneset,],1,function(x) (x) > expr_limit)
expressed_genes <- apply(expressed_genes,2,function(x) sum(x) < ncol(networksubset))
SCN3Egeneset <- intersect(row.names(networkExpressionFile[expressed_genes,]),SCN3Egeneset)
rm(expressed_genes)
GeneMarkov1 <- SCN3Egeneset
rm(cd_diffusionplot,emat_expressed,NetworkDist,SCN3Egenefilter,test,cellnames,cl,cores,i,iter,meanMoran,numbsim,OldGeneset,r,SCN3Egeneset,spatial.weights)
i<- clusternumber
threshold <- 4 #use mean spatial correlation of geneset of the featureselection (1) or after the first round of filtering (2) (more strict, which is default)
networksubset <- networkExpressionFile#[,names(subset(SCN3Elevel1, SCN3Elevel1 %in% i))]
dim(networksubset)
clusternumber <- as.numeric(i)
print(paste("Working on cluster",clusternumber, nrow(networksubset),"Genes",ncol(networksubset),"Cells"))
CVsq <- function(x) {(sd(x)/mean(x))^2}
expr_limit <- mean(networkExpressionFile)
emat_expressed <- apply(networksubset,1,function(x) any ((x) > expr_limit))
emat_expressed <- networksubset[emat_expressed,]
CV2 <- apply(emat_expressed,1,CVsq)
meancounts <- apply(emat_expressed,1,mean)
CV2_matrix <- matrix(c(log(meancounts),log(CV2)),nrow = length(CV2),ncol = 2)
row.names(CV2_matrix) <- names(CV2)
colnames(CV2_matrix) <- c("LogExpr","CV2")
CV2_matrix <- as.data.frame(CV2_matrix)
library(e1071)
model <- svm(CV2 ~ LogExpr ,CV2_matrix)#, gamma = 0.06)
predictedY <- predict(model, CV2_matrix)
CV2_matrix <- cbind(CV2_matrix,predictedY)
CV2_matrix$RelativeVariance <-CV2_matrix$CV2 - CV2_matrix$predictedY
NormGenes <- row.names(subset(CV2_matrix, RelativeVariance >= 0))
NormGenesFactor <- as.factor(1*(CV2_matrix$RelativeVariance >= 0))
palette("default")
plot(CV2_matrix$LogExpr,CV2_matrix$CV2,xlab ="Mean Count Magnitude (log)",ylab="Squared Coefficient of Variation", pch=20, cex=0.3, col=NormGenesFactor)
points(CV2_matrix$LogExpr,predictedY, col="green",pch=20, cex=0.1)
length(NormGenes)
NormGenes <- subset(NormGenes,! NormGenes %in% c("Xist","Tsix","eGFP"))
featureselection <- NormGenes
featureselection <- NormGenes
#featureselection <- row.names(networkExpressionFile)
#featureselection <- subset(featureselection,! featureselection %in% c("Xist","Tsix"))
SCN3Egeneset <- featureselection
SCN3Egeneset2 <- featureselection
if(boolean == "TRUE"){networksubset <- 1*as.matrix(networksubset > expr_limit)}
iter=0
meanMoran<-0
OldGeneset <- as.character(seq_len(100000))
while(0.5*length(GeneMarkov1) < (length(SCN3Egeneset)) ){
iter=iter+1
library(dpt)
pca <- prcomp(t(log(networksubset[SCN3Egeneset,]+1)),scale. = FALSE)
ncompGF <- 50
if(length(SCN3Egeneset) > 4000){
pca <- prcomp(t(log(networksubset[SCN3Egeneset,]+1)),scale. = FALSE)
ncompGF <- 50
if(length(SCN3Egeneset) < 50) {ncompGF <- length(SCN3Egeneset)}
cd_diffusionplot <-pca$x[,c(1:which(cumsum(pca$sdev[1:ncompGF])/sum(pca$sdev[1:ncompGF]) > 0.6)[1])]
}
if(length(SCN3Egeneset) <= 4000){
cd_diffusionplot <-t(log(networksubset[SCN3Egeneset,]+1))
}
cd_diffusionplot <- cd_diffusionplot[!duplicated(cd_diffusionplot),]
ts <- Transitions(cd_diffusionplot,k=10)
#Gene filtering
print(paste("Filtering", length(SCN3Egeneset), "genes."))
OldGeneset <- SCN3Egeneset
NetworkDist <- as.matrix(ts@transitions)
colnames(NetworkDist) <- row.names(cd_diffusionplot)
row.names(NetworkDist) <- row.names(cd_diffusionplot)
emat_expressed <- apply(networksubset,1,function(x) any ((x) >expr_limit))
emat_expressed <- networksubset[emat_expressed,row.names(NetworkDist)]
emat_expressed <- as.matrix(emat_expressed[intersect(row.names(emat_expressed),OldGeneset),])
if(usemagic=="TRUE"){
emat_expressed <- magic(ts,emat_expressed[,row.names(cd_diffusionplot)], power = 1, k = 20, n_eigs = 20, n_local = 10)
}
library(doParallel)
library(foreach)
library(spdep)
cores <- detectCores()
cl <- makeCluster(cores)
registerDoParallel(cl)
i<-1
SCN3Egenefilter <- matrix(nrow=nrow(emat_expressed),ncol = 3)
cellnames <- colnames(emat_expressed)
spatial.weights <- mat2listw(NetworkDist)
numbsim <- 1
networkdist <- 1-NetworkDist
library(ape)
library(spdep)
test <- foreach(i=1:nrow(emat_expressed),.packages="spdep",.combine=cbind) %dopar%
{
x <- emat_expressed[i,]
x <- x[x>expr_limit]
if(length(x)<2){}
else{
r <- row.names(emat_expressed)[i]
y <- networkdist[names(x),names(x)]
a <- mean(y)
y <- nrow(y)
m <- c(a,y,r)
}
}
stopCluster(cl)
SCN3Egenefilter <- t(test)
SCN3Egenefilter <- SCN3Egenefilter[! apply(SCN3Egenefilter,1,function(x) any(x=="NaN")),]
row.names(SCN3Egenefilter) <- SCN3Egenefilter[,3]
SCN3Egenefilter_clean <- as.data.frame(SCN3Egenefilter[complete.cases(SCN3Egenefilter),c(1:2)])
r <- row.names(SCN3Egenefilter_clean)
SCN3Egenefilter_clean <- apply(SCN3Egenefilter_clean,2,function(x) as.numeric(x))
row.names(SCN3Egenefilter_clean) <- r
SCN3Egenefilter_clean2 <- SCN3Egenefilter_clean
SCN3Egenefilter_clean2[,1] <- log(SCN3Egenefilter_clean2[,1]+1)
library(e1071)
model <- svm(V1 ~ V2 ,SCN3Egenefilter_clean2,gamma = 10)
predictedY <- predict(model, SCN3Egenefilter_clean2)
SCN3Egenefilter_clean2 <- SCN3Egenefilter_clean2[names(predictedY),]
CV2_matrix <- as.data.frame(cbind(SCN3Egenefilter_clean2,predictedY))
CV2_matrix$RelativeVariance <-CV2_matrix[,1] - CV2_matrix[,3]
SCN3E_genes2 <- row.names(subset(CV2_matrix, RelativeVariance < 0))
#NormGenesFactor <- as.factor(1*(CV2_matrix$RelativeVariance < 0))
#palette("default")
#plot(CV2_matrix$V5,CV2_matrix$V4,xlab ="Mean Cell Count Magnitude (log)",ylab="Mean Distance in Network (log)", pch=20, cex=0.1, col=NormGenesFactor)
#points(CV2_matrix$V5,CV2_matrix$predictedY, col="green",pch=20, cex=0.3)
#length(SCN3E_genes2)
SCN3Egeneset <- SCN3E_genes2
#SCN3Egeneset <- SCN3Egeneset2
}
expr_limit <- mean(networksubset)
expressed_genes <- 1*apply(networksubset[SCN3Egeneset,],1,function(x) (x) > expr_limit)
expressed_genes <- apply(expressed_genes,2,function(x) sum(x) < ncol(networksubset))
SCN3Egeneset <- intersect(row.names(networkExpressionFile[expressed_genes,]),SCN3Egeneset)
rm(expressed_genes)
GeneMarkov <- unique(c(GeneMarkov1,SCN3Egeneset))
rm(cd_diffusionplot,emat_expressed,NetworkDist,SCN3Egenefilter,test,cellnames,cl,cores,i,iter,meanMoran,numbsim,OldGeneset,r,SCN3Egeneset,spatial.weights)
```
Estimate the number of subclusters, try 5, 10, or 20 pca components = nCOMP
```{r, fig.width=15}
gc()
library(factoextra)
pca <- prcomp(t(log(networksubset[GeneMarkov,]+1)),scale. = FALSE)
plot(pca$sdev[1:50],pch = 20,
xlab = 'Diffusion component (DC)', ylab = 'sdev')
genemodulePCA <- cor(t(log(networkExpressionFile+1)),pca$x[,1:20],method="pearson")
#which(cumsum(pca$sdev[1:50])/sum(pca$sdev[1:50]) > 0.4)[1]
cd_diffusionplot <-pca$x[,c(1:which(cumsum(pca$sdev[1:20])/sum(pca$sdev[1:20]) > 0.6)[1])]
#cd_diffusionplot <- cd_diffusionplot[!duplicated(cd_diffusionplot),]
#cd_diffusionplot <-t(log(networksubset[GeneMarkov,]+1))
cd_diffusionplot <- cd_diffusionplot[!duplicated(cd_diffusionplot),]
ts <- Transitions(cd_diffusionplot)#k=20)
if(usemagic=="TRUE"){
networksubset <- magic(ts,networksubset[,row.names(cd_diffusionplot)], power = 1, k = 20, n_eigs = 20, n_local = 10)
}
pca <- prcomp(as.matrix(ts@transitions),scale. = FALSE)
plot(pca$sdev[1:50],pch = 20,