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R_functions_scATAC.R
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R_functions_scATAC.R
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### set dir -----
setDir <- function(dirIn, dirOut){
setwd(dirIn)
dirIn <- getwd()
if(!file.exists(dirOut))
dir.create(dirOut)
setwd(dirOut)
dirOut <- getwd()
cat("dirOut: ",dirOut,"\n")
return(dirOut)
}
## function - runQCscATAC.R
# parameter: dirIn-dir for .bam, dirOut-output folder, prefix
# QC parameters
# minPRK=3000 # minimum of fragments in peaks
# maxPRK=20000 # max of fragments in peaks
# PctRiP=15 # Fraction of fragments in peaks
# blr=0.05 # Ratio reads in genomic blacklist regions
# nsr=4 #
# tssE=2 # Transcriptional start site (TSS) enrichment score
# usage: objAt <- runQCscATAC(dirIn, dirOut,prefix,genomeV)
runcreateSignacObj <- function(dirIn,dirOut,prefix,genomeV){
#library(Signac);library(EnsDb.Hsapiens.v86);library(EnsDb.Mmusculus.v79)
# QC parameters
#minPRK=3000 # minimum of fragments in peaks
#maxPRK=20000 # max of fragments in peaks
#PctRiP=15 # Fraction of fragments in peaks
#blr=0.05 # Ratio reads in genomic blacklist regions
nsr=4 #
tssE=2 # Transcriptional start site (TSS) enrichment score
#genomeV="hg38" #options: "hg38","mm10","rn6" and others
# set dir
if(is.null(dirOut)){dirOut <- "out_scATAC_signac"}
dirOut <- setDir(dirIn=dirIn, dirOut = dirOut)
# input data
setwd(dirIn)
counts <- Read10X_h5(filename = "filtered_peak_bc_matrix.h5")
metadata <- read.csv(file = "singlecell.csv", header = TRUE, row.names = 1)
chrom_assay <- CreateChromatinAssay(counts = counts, sep = c(":", "-"),genome = genomeV,
fragments = 'fragments.tsv.gz', min.cells = 1, min.features = 10)
objAt <- CreateSeuratObject(counts = chrom_assay, assay = "peaks", meta.data = metadata)
objAt
objAt$orig.ident <- prefix
# annotation
# extract gene annotations from EnsDb
if(genomeV=="hg38"){
annotations <- GetGRangesFromEnsDb(ensdb = EnsDb.Hsapiens.v86)} else if(genomeV=="mm10"){
annotations <- GetGRangesFromEnsDb(ensdb = EnsDb.Mmusculus.v79)} else {
stop("check genome!")}
# change to UCSC style since the data was mapped to hg19
seqlevelsStyle(annotations) <- 'UCSC'
# add the gene information to the object
Annotation(objAt) <- annotations
# compute nucleosome signal score per cell
objAt <- NucleosomeSignal(object = objAt)
# compute TSS enrichment score per cell
objAt <- TSSEnrichment(object = objAt, fast = FALSE)
# add blacklist ratio and fraction of reads in peaks
objAt$pct_reads_in_peaks <- objAt$peak_region_fragments / objAt$passed_filters * 100
objAt$blacklist_ratio <- objAt$blacklist_region_fragments / objAt$peak_region_fragments
objAt$high.tss <- ifelse(objAt$TSS.enrichment > tssE, 'High', 'Low')
p1<-TSSPlot(objAt, group.by = 'high.tss') + NoLegend() + ggtitle(paste0("TSS enrichment: ",prefix))+labs(subtitle=prefix)
objAt$nucleosome_group <- ifelse(objAt$nucleosome_signal > nsr, paste0('NS > ',nsr), paste0('NS < ',nsr))
p2<- FragmentHistogram(object = objAt, group.by = 'nucleosome_group') +labs(subtitle=prefix)
p3<-VlnPlot(object = objAt,
features = c('pct_reads_in_peaks', 'peak_region_fragments',
'TSS.enrichment', 'blacklist_ratio', 'nucleosome_signal'),pt.size = 0, ncol = 5) +labs(subtitle=prefix)
p4 <- plot_grid(p1,p2,ncol=1)
# save RDS
setwd(dirOut)
ncells <-dim(objAt)[2]
saveRDS(objAt, paste0("obj_HAS_",prefix,"_unfilt_n",ncells,".rds"))
# plot-tss
setwd(dirOut)
#pdf(paste0("01_1_plot_tss_",prefix,".pdf"))
png(paste0("01_1_plot_tss_",prefix,".png"))
print(p4)
dev.off()
setwd(dirOut)
png(paste0("01_2_plot_QC_",prefix,"_unfilted.png"),height = 300, width = 1000)
print(p3)
dev.off()
return(objAt)
}
# function-qc summary table from scATAC Object
# usage: qcsumTable <- runACsumT(objAt,prefix)
runQCsumT<-function(objAt,prefix){
prp<- objAt$pct_reads_in_peaks
sd.prp <- sd(prp) *3 + mean(prp)
prps <- summary(prp)
pf <- objAt$peak_region_fragments
sd.pf <- sd(pf) *3 + mean(pf)
#sd.pf2 <- sd(pf) * -3 + mean(pf)
pfout <- sum(pf>sd.pf)
pfs <- summary(pf)
te <-objAt$TSS.enrichment
sd.te <- sd(te) *3 + mean(te)
tes <-summary(te)
nss <-objAt$nucleosome_signal
sd.nss <- sd(nss)*3 + mean(nss)
nsss<- summary(nss)
sumAt <-round(rbind(prps,pfs,tes,nsss),2)
rName <-c("pct_reads_in_peaks","peak_region_fragments","TSS.enrichment","nucleosome_signal")
rownames(sumAt)<-rName
sd3cutoff=round(c(sd.prp, sd.pf, sd.te, sd.nss),2)
sumAt<-cbind(sumAt,sd3cutoff)
write.csv(sumAt,paste0("summary_QC_statistics_unFilt_",prefix,".csv"))
print(sumAt)
return(sumAt)
}
## function - run QC for scATAc data
# minPRK=3000 # minimum of fragments in peaks
# maxPRK=20000 # max of fragments in peaks
# PctRiP=15 # Fraction of fragments in peaks
# blr=0.05 # Ratio reads in genomic blacklist regions
# nsr=4 #
# tssE=2 # Transcriptional start site (TSS) enrichment score
# cut-off parameter
runQCSignacObj <- function(objAt,dirOut,prefix){
# default parameters
minPRK =1000
PctRiP=15
blr=0.05
nsr=4
tssE=2
# QC summary table
qcsumTable <- data.frame(runQCsumT(objAt,prefix))
maxPRK <-qcsumTable$sd3cutoff[2]
if(maxPRK <10000)(maxPRK=10000)
cat("maxPRK= ",maxPRK,"\n")
# filtering
objAt <- subset(
x = objAt,
subset = peak_region_fragments > minPRK &
peak_region_fragments < maxPRK &
pct_reads_in_peaks > PctRiP &
blacklist_ratio < blr &
nucleosome_signal < nsr &
TSS.enrichment > tssE
)
qcCells<-dim(objAt)[2]
p4<-VlnPlot(object = objAt,
features = c('pct_reads_in_peaks', 'peak_region_fragments',
'TSS.enrichment', 'blacklist_ratio', 'nucleosome_signal'),
pt.size = 0, ncol = 5) + labs(subtitle=prefix)
setwd(dirOut)
png(paste0("01_3_plot_QC_",prefix,"_filted_n",qcCells,".png"),height = 300, width = 1000)
print(p4)
dev.off()
print(objAt)
# save RDS
setwd(dirOut)
ncells <-dim(objAt)[2]
saveRDS(objAt, paste0("obj_HAS_",prefix,"_qc_pf",minPRK,"_",maxPRK,"_pct",PctRiP,"_br",blr,"_ns",nsr,"_te",tssE,"_n",ncells,".rds"))
# normalization, linear dimensional reduction
objAt <- RunTFIDF(objAt)
objAt <- FindTopFeatures(objAt, min.cutoff = 'q0')
objAt <- RunSVD(objAt)
p5<-DepthCor(objAt)+ labs(subtitle=prefix)
setwd(dirOut)
#pdf(paste0("02.1_plot_depth_",prefix,".pdf"),width = 6, height = 4)
png(paste0("02_1_plot_depth_",prefix,".png"),width = 600, height = 500)
print(p5)
dev.off()
# Non-linear dimension reduction and clustering => removed lsi 1 dim
objAt <- RunUMAP(object = objAt, reduction = 'lsi', dims = 2:30)
objAt <- FindNeighbors(object = objAt, reduction = 'lsi', dims = 2:30)
objAt <- FindClusters(object = objAt, verbose = FALSE, algorithm = 3)
p6 <- DimPlot(object = objAt, label = TRUE) + NoLegend()+ labs(subtitle=prefix)
#pdf(paste0("02.2_plot_umap_",prefix,".pdf"),width = 6, height = 4)
png(paste0("02_2_plot_umap_",prefix,".png"),width = 600, height = 500)
print(p6)
dev.off()
# Create a gene activity matrix
# add the gene activity matrix to the Seurat object as a new assay and normalize it
gene.activities <- GeneActivity(objAt)
objAt[['RNA']] <- CreateAssayObject(counts = gene.activities)
objAt <- NormalizeData(
object = objAt,
assay = 'RNA',
normalization.method = 'LogNormalize',
scale.factor = median(objAt$nCount_RNA)
)
objAt <- RunUMAP(object = objAt, reduction = 'lsi', dims = 2:30)
objAt <- FindNeighbors(object = objAt, reduction = 'lsi', dims = 2:30)
objAt <- FindClusters(object = objAt, verbose = FALSE, algorithm = 3)
p7 <- DimPlot(object = objAt, label = TRUE) + NoLegend()+ labs(subtitle=prefix)
#pdf(paste0("02.3_plot_umap_afterNorm_geneActivity_",prefix,".pdf"),width = 5, height = 4)
png(paste0("02_3_plot_umap_afterNorm_geneActivity_",prefix,".png"),width = 600, height = 500)
print(p7)
dev.off()
# save RDS
setwd(dirOut)
ncells <-dim(objAt)[2]
saveRDS(objAt, paste0("obj_HAS_",prefix,"_geneActivity_pf",minPRK,"_",maxPRK,
"_pct",PctRiP,"_br",blr,"_ns",nsr,"_te",tssE,"_n",ncells,".rds"))
return(objAt)
cat("Process was done!- runQCSignacObj","\n")
} # end; runQCscATAC
### cell proportion ----
cType <- "ID" #option: ID,Clust,subset
runCellProportion <- function(objectA,prefix,dirOut,cType){
# cell proportion
if(cType=="ID"){
metaTbl <- data.frame(table(Idents(objectA)))
}else if(cType=="Clust"){
metaTbl <- data.frame(table(objectA$seurat_clusters))
}else if(cType=="subset"){
metaTbl <- data.frame(table(objectA$subset))
}else {print("choose cluster type:(ID or Clust")}
prop2pr <- data.frame(proportion=round(prop.table(metaTbl$Freq)*100,1))
metaTblA <- cbind(metaTbl,prop2pr)
colnames(metaTblA) <- c('cluster','cells','Proportion')
setwd(dirOut)
write.csv(metaTblA, paste0(prefix,"_proportion_clusters.csv"),row.names= F)
# cell table
if(!(sum(names(objectA@meta.data) == "Cluster"))){
clustering.table <- table(Idents(objectA), objectA$seurat_clusters)
head(clustering.table)
setwd(dirOut)
write.csv(clustering.table, paste0(prefix,"_summary_table_clusters_celltype_",prefix,".csv"),row.names= T)
}
# plot-bar
#pdf(paste0(prefix,"_plot_Barplot_cell.proportion.pdf"),width = 20, height = 10)
png(paste0(prefix,"_plot_Barplot_cell.proportion.png"),width = 1200, height = 800)
print(ggplot(metaTblA, aes(x=cluster, y=Proportion, fill=cluster)) + ylab("Proportion (%)") +xlab("Cluster")+
geom_bar(stat="identity") + geom_text(aes(label=cells), vjust=-0.3, size=15) +
scale_x_discrete(limits = rev(levels(metaTblA$cluster)))+ coord_flip()+
theme(text = element_text(size=20)))
dev.off()
cat("Process was done! \n")
}
# test run: cell proportion
# runCellProportion(obj.WTp14,"test",dirOut = dirOut,cType = "ID")
### function : generare report
#render_report = function(dirIn, prefix, callRmd,dirOut) {
render_report = function(dirIn,prefix,dirOut,callRmd) {
rmarkdown::render(
callRmd, params = list(
# dirIn = dirIn,
prefix=prefix,
# finsum = finsum,
dirOut=dirOut),
output_file = paste0(dirOut,"/Report_QC_", prefix, ".html"),"html_document")
}
# Function: runCoembedscRnaAtac - do co-embedding for celltype prediction
# require: library(EnsDb.Hsapiens.v86)
# rnaCellType="celltype" # celltype info column name in scRNA
# parameters: fragPath-frgment file path
runCoembedscRnaAtacHg38 <- function(scRNA,scAtac,dirOut,prefix,rnaCellType="celltype",fragPath){
print(scRNA); print(scAtac)
scRNA$seqType <- "scRNA"
scAtac$seqType <- "scATAC"
# update fragment file path
frags <- Fragments(scAtac) # get list of fragment objects
Fragments(scAtac) <- NULL # remove fragment information from assay
newpath <- paste0(fragPath,"/fragments.tsv.gz")
frags[[1]] <- UpdatePath(frags[[1]], new.path = newpath) # update path. Do this for any/all fragment objects in the list
Fragments(scAtac) <- frags
# Perform standard analysis of each modality independently RNA analysis
DefaultAssay(object = scRNA) <- "RNA"
scRNA <- NormalizeData(scRNA)
scRNA <- FindVariableFeatures(scRNA)
scRNA <- ScaleData(scRNA)
scRNA <- RunPCA(scRNA)
scRNA <- RunUMAP(scRNA, dims = 1:30)
# ATAC analysis add gene annotation information
annotations <- GetGRangesFromEnsDb(ensdb = EnsDb.Hsapiens.v86)
seqlevelsStyle(annotations) <- "UCSC"
genome(annotations) <- "hg38"
Annotation(scAtac) <- annotations
# We exclude the first dimension as this is typically correlated with sequencing depth
scAtac <- RunTFIDF(scAtac)
scAtac <- FindTopFeatures(scAtac, min.cutoff = "q0")
scAtac <- RunSVD(scAtac)
scAtac <- RunUMAP(scAtac, reduction = "lsi", dims = 2:30, reduction.name = "umap.atac", reduction.key = "atacUMAP_")
# plot
p1 <- DimPlot(scRNA, group.by = rnaCellType, label = TRUE) + NoLegend() + ggtitle("RNA")
p2 <- DimPlot(scAtac, group.by = "seqType", label = FALSE) + NoLegend() + ggtitle("ATAC")
setwd(dirOut)
png(paste0("1_plot_umap_RNA_ATAC_",prefix,".png"),width=800, height=300)
print(p1 + p2)
dev.off()
# Identifying anchors between scRNA-seq and scATAC-seq datasets
# quantify gene activity
# setwd(fragPath)
gene.activities <- GeneActivity(scAtac, features = VariableFeatures(scRNA))
# add gene activities as a new assay
scAtac[["ACTIVITY"]] <- CreateAssayObject(counts = gene.activities)
# normalize gene activities
DefaultAssay(scAtac) <- "ACTIVITY"
scAtac <- NormalizeData(scAtac)
scAtac <- ScaleData(scAtac, features = rownames(scAtac))
# Identify anchors
transfer.anchors <- FindTransferAnchors(reference = scRNA, query = scAtac, features = VariableFeatures(object = scRNA),
reference.assay = "RNA", query.assay = "ACTIVITY", reduction = "cca")
# Annotate scATAC-seq cells via label transfer
celltype.predictions <- TransferData(anchorset = transfer.anchors, refdata = scRNA$celltype,
weight.reduction = scAtac[["lsi"]], dims = 2:30)
scAtac <- AddMetaData(scAtac, metadata = celltype.predictions)
# save RDS
setwd(dirOut)
saveRDS(scAtac, paste0("obj_scATAC_wcelltypeTransfer_",prefix,".rds"))
# plot
#scAtac$annotation_correct <- scAtac$predicted.id == scAtac$seurat_annotations
p3 <- DimPlot(scAtac, group.by = "predicted.id", label = TRUE) + NoLegend() + ggtitle("Predicted annotation")
#p3.2 <- DimPlot(scAtac, group.by = rnaCellType, label = TRUE) + NoLegend() + ggtitle("Ground-truth annotation")
setwd(dirOut)
png(paste0("2_plot_umap_RNA_ATAC_labeTransfer_",prefix,".png"))
print(p3)
dev.off()
# Co-embedding scRNA-seq and scATAC-seq datasets
genes.use <- VariableFeatures(scRNA)
refdata <- GetAssayData(scRNA, assay = "RNA", slot = "data")[genes.use, ]
# imputation
imputation <- TransferData(anchorset = transfer.anchors, refdata = refdata, weight.reduction = scAtac[["lsi"]],dims = 2:30)
scAtac[["RNA"]] <- imputation
coembed <- merge(x = scRNA, y = scAtac)
# add celltype
#coembed$celltype2 <-c(as.character(scAtac$predicted.id),as.character(scRNA$celltype))
# PCA and UMAP on this combined object to visualize the co-embedding
coembed <- ScaleData(coembed, features = genes.use, do.scale = FALSE)
coembed <- RunPCA(coembed, features = genes.use, verbose = FALSE)
coembed <- RunUMAP(coembed, dims = 1:30)
p4a <-DimPlot(coembed, group.by = "seqType")+ ggtitle("Co-embeded data")
p4b <-DimPlot(coembed, group.by = "celltype")+ ggtitle("scRNA:celltype")
p4c <-DimPlot(coembed, group.by = "predicted.id")+ ggtitle("scATAC:predicted celltype")
#plot_annotation(title ='Co-embeded data, scRNA:celltype, scATAC:predicted celltype')
png(paste0("3_plot_umap_RNA_ATAC_coembed_",prefix,".png"),width=1200,height=300)
print(p4a+p4b+p4c)
dev.off()
# save RDS
setwd(dirOut)
saveRDS(coembed, paste0("obj_coembed_",prefix,".rds"))
# cellproportion
Idents(scAtac)<-scAtac$predicted.id
figTitle=paste0("scATAC_",prefix)
runCellProportion(objectA=scAtac,prefix,dirOut,cType = "ID")
return(coembed)
}
## scRNA analysis-scPred
### Function : createscPredReference
# parameters: objectA(require 'celltype' info), dirOut,clssID(default:celltype)
# usage: reference <- createscPredReference(objectA,dirOut,clssID, prefix)
createscPredReference <- function(objectA,dirOut,clssID,prefix){
print(head(objectA@meta.data))
#if(is.null(clssID)) {clssID <- "celltype"; print(clssID)}
# umap
setwd(dirOut)
p1<-DimPlot(object = objectA, reduction = "umap", group.by = clssID,label = T,label.size = 5)
pdf(paste0("1_plot_umap_ref_",prefix,".pdf"),width = 8)
print(p1)
dev.off()
# cell proportion
cID <-which(names(objectA@meta.data)==clssID)
head(objectA@meta.data[,cID])
Idents(objectA) <- objectA@meta.data[,cID]
cType <- "ID" #option: ID,Clust,subset,subset.all
runCellProportion(objectA=objectA,prefix=prefix,dirOut=dirOut,cType=cType)
# train classifier
objectA <- getFeatureSpace(objectA, pvar=clssID)
objectA <- trainModel(objectA)
get_probabilities(objectA) %>% head()
probTable <- data.frame(get_probabilities(objectA))
write.csv(probTable,paste0("summary_probTable_ref_",prefix,".csv"),row.names=T)
# save RDS
print(objectA)
saveRDS(objectA, paste0("obj_reference_scPredTable_",prefix,".rds"))
sink(paste0("summary_scPred_Table_ref_",prefix,".out"))
print(get_scpred(objectA)) #option: recompute_alignment = FALSE
sink()
p3<-plot_probabilities(objectA)
png(paste0("2_plot_ref_probability_",prefix,".png"),width = 800,height = 800)
print(p3)
dev.off()
return(objectA)
}
if(F){
objectA<- readRDS("/storage/chen/data_share_folder/22_10x_hs_AnteriorSegment_data/scAtacQC/data/test/out_scPred_ref_testSacilia/obj_reference_scPredTable_testSacilia.rds")
prefix <- "test_cilia"
dirOut<-"/storage/chen/data_share_folder/22_10x_hs_AnteriorSegment_data/scAtacQC/data/test/out_scPred_ref_testSacilia/"
list.file
}
### Function run scPred with ref and train data
# parameters: reference,query,prefix,dirOut, rsN=0.1,dn=20
# usage: query <- runscPred(objref=reference,objtrain=query,prefix,dirOut)
runscPred<-function(reference,query,prefix,dirOut){
rsN=0.1
dn=20
# Training data- prediction =====
setwd(dirOut)
subDir <- paste0("out_cellType_prediction_",prefix)
subDir <- setDir(dirIn=dirOut,dirOut=subDir)
#lssvmRadial
#query <- NormalizeData(query)
query <- scPredict(query, reference)
# save RDS
setwd(subDir)
saveRDS(query, paste0("obj_query_predicted_",prefix,".rds"))
# umap- outputs
query <- RunUMAP(query, reduction = "scpred", dims = 1:dn)
# cell clustering
query <- FindNeighbors(object = query, reduction = "scpred", dims = 1:dn)
query <- FindClusters(query, resolution = rsN)
# cell proportion
Idents(query) <- query$seurat_clusters
cType <- "ID" #option: ID,Clust,subset,subset.all
runCellProportion(objectA=query,prefix=paste0(prefix,"_bycluster"),dirOut=subDir,cType=cType)
p4 <-DimPlot(query, group.by = "scpred_prediction", label = TRUE, repel = TRUE)
p5 <-DimPlot(query, group.by = "seurat_clusters", label = TRUE, repel = TRUE)
# plot-umap
setwd(subDir)
pdf( paste0("01_1_plot_umap_classf_scpred_",prefix,".pdf"),width = 10)
print(p4) #| p2
dev.off()
pdf(paste0("01_2_plot_umap_classf2_compare_ref.vs.train_",prefix,".pdf"),width = 14)
print(p5 |p4) #| p2
dev.off()
# save RDS
setwd(subDir)
saveRDS(query, paste0("obj_query_predicted_umap_",prefix,".rds"))
#compare raw vs prediction
outCross <- crossTab(query, "seurat_clusters", "scpred_prediction")
write.csv(outCross,paste0("summary_coross_seuratcluster.vs.prediction_",prefix,".csv"))
sink(file=paste0("summary_classifiers_",prefix,".out"))
get_classifiers(reference)
sink()
return(query)
}
### Function: draw plots(dot,feature,vlnplot) fro marker set list
# parameters: objectA-Robj, markerSet-markers list format, prefix, dirOut
# usage:checkMarkersHsHas(objectA,markerSet,prefix,dirOut)
checkMarkersHsHas <- function(objectA,markerSet,prefix,dirOut){
#markerSet #Mlist1
markerSetNames <- names(markerSet)
print(names(markerSet))
# set dir
subDir <- paste0("out_check_markerGeneExpression_",prefix)
setwd(dirOut)
dir.create(subDir)
setwd(subDir)
allGenes <- rownames(objectA)
for (i in 1:length(markerSetNames)) {
cat ("process_marker_set: ",i,"\n")
mgene1 <- markerSet[[i]]
mgeneIS1 <- toupper(mgene1)
# set plot size
iwid <- 8+length(mgeneIS1)*1/3
if(length(mgeneIS1)/4 <=1){iwid2<-300*length(mgeneIS1)} else{iwid2= 1200}
ihig2=300*ceiling((length(mgeneIS1)/4))
ihig3=3*ceiling((length(mgeneIS1)/4))
# dot plot
pdf(paste0(i,"_1_plot_dot_markers_",markerSetNames[i],"_",prefix,".pdf"),width = iwid)
print(DotPlot(object = objectA,features = unique(mgeneIS1), dot.scale = 6, assay = "RNA",col.min = 0, scale.max = 80,
cols = c("gray","magenta1")) + RotatedAxis() + theme(axis.text.x = element_text(angle = 45,hjust=1)) +
theme(panel.grid.major = element_line(colour = "grey95", size = (.1)))+ggtitle(paste0("Marker gene expression:",markerSetNames[i],"- ",prefix))) #+ coord_flip
dev.off()
# feature plot
#pdf(paste0(prefix, "_2.2_featureplot_celltype.markers.pdf"),width = 9, height =15 )
png(paste0(i,"_2_featureplot_celltype_markers_",markerSetNames[i],"_",prefix,"2.png"),width = iwid2, height = ihig2)
print(FeaturePlot(object = objectA, features = mgeneIS1, pt.size=.1, min.cutoff = 0, ncol = 3)+
labs(caption = paste0("TissueMarker: ",markerSetNames[i],"- Sample: ",prefix)))
dev.off()
# vlnplot
#png(paste0(prefix,"_2_4_vlnplot_celltype.markers_",markerSetNames[i],"_",prefix,".png"),width = iwid2*1.5, height = ihig2))
pdf(paste0(i,"_3_vlnplot_celltype_markers_",markerSetNames[i],"_",prefix,".pdf"),width = 12, height =ihig3)
print(VlnPlot(object = objectA,features = mgeneIS1,pt.size = 0, ncol = 3)+
labs(caption = paste0("TissueMarker: ",markerSetNames[i],"- Sample: ",prefix)))
dev.off()
}
print(markerSet)
print("Process was done!")
}
# function - getSubTissueMarker.R
# parameters: mkers-Robj with HAS list, tType-tissue or celltype e.g. TM
# usage: chkMkList2<- getSubTissueMarker(mkers,tType)
#tType='TM'
getSubTissueMarker<- function(mkers,tType){
submkers<- dplyr::filter(mkers,Tissue %in% tType)[,c(2,3)]
tNames2 <- names(table(submkers$celltype))
chkMkList2 <- list()
for (i in 1:length(tNames2)) {
print(tNames2[i])
eachtissue2 <- tNames2[i]
submker2 <- dplyr::filter(submkers,celltype %in% eachtissue2)$marker
chkMkList2[[i]] <- submker2
}
names(chkMkList2)<- tNames2
print(head(chkMkList2))
return(chkMkList2)
}
### function - run_dotplot4HAScelltypeMkers: check HAS markers showing celltype info on dot plot
# parameters: objectA-Robj with fullpath, prefix- sample_ID
# requirement: getSubTissueMarker function
run_dotplot4HAScelltypeMkers<- function(objectA,prefix){
# load HAS marker object
mkers <- readRDS("/storage/chen/data_share_folder/22_10x_hs_AnteriorSegment_data/scAtacQC/data/obj_mkers_HAS_mkerSets.rds")
chkMkList<- readRDS("/storage/chen/data_share_folder/22_10x_hs_AnteriorSegment_data/scAtacQC/data/obj_marker_HAS_list_6T.rds")
# check tissue types
tNames2 <- names(chkMkList)
#setwd(dirOut)
for (i in 1:length(tNames2))
{
tType <- tNames2[i]
chkMkList2 <- getSubTissueMarker(mkers,tType)
mgeneIS1 <- unique(unlist(chkMkList2))
# dot plot for each celltype
iwid <- (8+length(mgeneIS1)*1/3)*2
pdf(paste0(i,"_1_plot_dot_markers_",tType,"_",prefix,".pdf"),width = iwid)
print(DotPlot(object = objectA,features = chkMkList2, dot.scale = 6, assay = "RNA",col.min = 0, scale.max = 80,
cols = c("gray","magenta1")) + RotatedAxis() + theme(axis.text.x = element_text(angle = 45,hjust=1)) +
labs(caption=paste0("-Data: ",prefix,", Tissue: ",tType)))#+
#theme(panel.gratrid.major = element_line(colour = "grey95", size = (.1)))+ggtitle(paste0("Marker gene expression:",markerSetNames[i],"- ",prefix))) #+ coord_flip
dev.off()
#return(chkMkList)
}
}
### run data integration ---------
runIntegrateObjects <- function(objList,dirOut,prefix,rsN,ds){
# select features that are repeatedly variable across datasets for integration
features <- SelectIntegrationFeatures(object.list = objList)
# Perform integration
integ.anchors <- FindIntegrationAnchors(object.list = objList, anchor.features=features, dims = 1:ds)
integ.combined <- IntegrateData(anchorset = integ.anchors, dims = 1:ds)
integ.combined
DefaultAssay(integ.combined) <- "integrated"
# Run the standard workflow for visualization and clustering
integ.combined <- ScaleData(integ.combined, verbose = FALSE)
integ.combined <- RunPCA(integ.combined, npcs = 30, verbose = FALSE)
integ.combined <- RunUMAP(integ.combined, reduction = "pca", dims = 1:ds)
integ.combined <- FindNeighbors(integ.combined, reduction = "pca", dims = 1:ds)
integ.combined <- FindClusters(integ.combined, resolution = rsN)
#integ.combined$subset <- gsub("MK.org.D173","D173",integ.combined$subset)
integ.combined <- FindClusters(integ.combined, resolution = rsN)
print(head(integ.combined@meta.data))
# save rds
setwd(dirOut)
saveRDS(integ.combined,paste0("obj_integ_",prefix,"_rs_",rsN,"_ds_",ds,"_",prefix,".rds"))
pdf(paste0("1_plot_umap_rs_",rsN,"_ds_",ds,"_",prefix,".pdf"))
print(DimPlot(object = integ.combined, reduction = "umap",label=T,label.size = 8))
print(DimPlot(object = integ.combined, reduction = "umap",group.by ="subset" ,label=F,label.size = 8))
#print(DimPlot(object = integ.combined, reduction = "umap",group.by ="celltype" ,label=T,label.size = 6))
#print(DimPlot(object = integ.combined, reduction = "umap",group.by ="celltype" ,label=F,label.size = 8))
dev.off()
nd <-dim(summary(objList))[1]
nwid <- 300*nd
png(paste0("2_plot_umap_rs_",rsN,"_ds_",ds,"_",prefix,".png"),width = nwid, height = 300)
print(DimPlot(object = integ.combined, reduction = "umap",split.by ="subset" ,label=F,label.size = 5))
dev.off()
cat("Process was done!\n")
return(integ.combined)
}
### run data integration ---------
runIntegrateObjectsATAC <- function(objList,dirOut,prefix,rsN,ds){
}
# Function - draw venndiagram upto 7 groups
# require: library(ggVennDiagram);library(ggplot2);library(nVennR)
#install.packages('/storage/singlecell/sangbaek/software/nVennR_0.2.3.tar.gz')
# user parameters: gList-id list per group,prefix, catNames-category name(group names)
# usage: venPlot <- runVennDgramG7(gList=x,prefix)
runVennDgramG7 <-function(gList,prefix,catNames=NULL){
if(is.null(catNames)){catNames <- names(gList)}else{catNames} # replace with new names}
grnumb <- length(catNames)
# plot
p <- ggVennDiagram(gList,category.names = catNames,label_color = "black", label_size = 4)# + scale_fill_gradient(low="blue",high = "red")
p2<- p + scale_fill_distiller(palette = "RdBu")
pdf(paste0("plot_venn_g",grnumb,"_",prefix,".pdf"))
print(p2)
dev.off()
# get classified list
#overlap <- calculate.overlap(xx.1)
overlap <- calculate.overlap(gList)
#save
saveRDS(overlap, paste0('obj_overlap_summary_venn_table_groups_',grnumb,'_',prefix,'.rds'))
sink(paste0('summary_venn_table_groups_',grnumb,'_',prefix,'.txt'))
print(str(overlap))
print(overlap)
sink()
#library(nVennR)
myV <- plotVenn(gList, showPlot = F, outFile = paste0("plot_venn_g",grnumb,"_",prefix,"_bynVennR.svg"))
sink(paste0('summary_venn_table_groups_',grnumb,'_',prefix,'_bynVenn.txt'))
print(str(myV))
print(listVennRegions(myV))
sink()
return(p2)
}
# function: get GRange object
getGRanges<-function(dirIn, finbed){
#setwd(dirIn)
peaksA <- read.table(file = finbed,col.names = c("chr", "start", "end"))
gr.A <- makeGRangesFromDataFrame(peaksA)
return(gr.A)
}
# Function-get ATAC signac obj
# parameters: finObj-sigobj_qc,finfrag-fragmentFile,annotations=annotObject,prefix,combined.peaks
# usage: aAtac <- getEachAtacObjwcomFeatures(finObj,finfrag,annotations,prefix,combined.peaks)
getEachAtacObjwcomFeatures <-function(finObj,finfrag,annotations,prefix,combined.peaks){
atac1<- readRDS(finObj)
md.A <- atac1@meta.data
DefaultAssay(object = atac1) <- "peaks"
finfrag <- paste0(dirRawList[i],'/fragments.tsv.gz')
frags.A <- CreateFragmentObject(path=finfrag, cells=colnames(atac1))
atac1.counts <- FeatureMatrix(fragments = frags.A, features = combined.peaks,
cells = colnames(atac1))
atac1_assay <- CreateChromatinAssay(atac1.counts, fragments = frags.A)
aAtac1 <- CreateSeuratObject(atac1_assay, assay = "ATAC", meta.data=md.A)
Annotation(aAtac1) <- annotations
aAtac1$dataset <- prefix
print(head(colnames(aAtac1)))
# save RDS
saveRDS(aAtac1, paste0("obj_annotAtac_",prefix,"_wcompeaks.rds"))
return(aAtac1)
}
# function-merge atac signac objects
# parameters: aAtacList-ATAC obj list,prefixList
# merge all datasets, adding a cell ID to make sure cell names are unique
# usage: combined <- getCombinedSignacObjects(aAtacList,prefixList)
getCombinedSignacObjects<- function(aAtacList,prefixList){
combined <- merge(
x = aAtacList[[1]],
y = aAtacList[2:length(aAtacList)],
add.cell.ids = prefixList )
#combined[["ATAC"]]
print(combined)
# clustering
combined <- RunTFIDF(combined)
combined <- FindTopFeatures(combined, min.cutoff = 20)
combined <- RunSVD(combined)
combined <- RunUMAP(combined, dims = c(2:30), reduction = 'lsi')
combined <- FindNeighbors(object = combined, reduction = 'lsi', dims = c(2:30))
combined <- FindClusters(object = combined, verbose = FALSE, algorithm = 3)
return(combined)
}
# function-Assign cell types
# parameters: objectA, prefix,dirOut,newCellTypes-new celltype list
# usage: objA <- runAssignCelltype(objectA, prefix, dirOut,newCellTypes)
### Assign cell types ------
runAssignCelltype <- function(objectA, prefix, dirOut,newCellTypes){
names(newCellTypes) <- levels(objectA)
objectA <- RenameIdents(objectA, newCellTypes)
objectA$celltype <- Idents(objectA)
# umap
# if(F){
pdf(paste0("plot_umap_d20_wsubset_wCelltypes_",prefix,".pdf"))
p1 <- DimPlot(object = objectA, reduction = "umap",group.by="celltype",label=T,label.size = 8)
p2 <- DimPlot(objectA,reduction = "umap",group.by="subset",label=F,label.size = 5)
p3 <- DimPlot(objectA,reduction = "umap",group.by="subset",split.by = "subset",label=F,label.size = 5)
p4 <- DimPlot(objectA,reduction = "umap",group.by="celltype",split.by="subset",label=F,label.size = 5)
print(p1)
print(p2)
print(p3/p4)
dev.off()
# }
return(objectA)
}
### Reclustering for Seurat object of scRNA data ----
runReclust <- function(objectA,prefix, dirOut,rsN,dn){
# parameters to tiflter
#qtmin <- 0.03 # lower quantile for cut-off
#qtmax <- 0.99 # higher quantile for cut-off
if(F){
if(is.null(qtmin)) {qtmin <- 0.03} else {qtmin <- qtmin}
if(is.null(qtmax)) {qtmax <- 0.99} else {qtmax <- qtmax}
if(is.null(rsN)) {rsN <- 0.3} else {rsN <- rsN}
if(is.null(cutMT)) {cutMT <- 20} else {cutMT <- cutMT}
if(is.null(normOpt)) {normOpt <- "SCT"} else {normOpt <- normOpt}
if(is.null(dn)) {dn <- 20} else {dn <- dn}
}
# set dir
subDir <- paste0("out_reclustering_",prefix)
setwd(dirOut)
dir.create(subDir)
setwd(subDir)
# plot format: p02 +p02 + plot_layout(ncol = 2, width = c(1, 2))
pdf(paste0("1.1.plot_QC_box_filter_bycluster_scRNA_after.filted_",prefix,".pdf"), width = 12,height = 6)
p1 <- VlnPlot(object = objectA,pt.size = 0, features = c("nFeature_RNA","nCount_RNA","percent.mito"),group.by = "subset", ncol = 3)
p2 <- VlnPlot(object = objectA,pt.size = 0, features = c("nFeature_RNA","nCount_RNA","percent.mito"), ncol = 3)
print(p1/p2)
dev.off()
objectA <- RunPCA(object = objectA, features = VariableFeatures(object = objectA))
#Run non-linear dimensional reduction (UMAP/tSNE)
objectA <- RunUMAP(object = objectA, reduction = "pca", dims = 1:dn)
# cell clustering
objectA <- FindNeighbors(object = objectA, reduction = "pca", dims = 1:dn)
objectA <- FindClusters(objectA, resolution = rsN)
# umap
pdf(paste0("1.2_plot_umap_d20_rs_",rsN,"_",prefix,".pdf"))
print(DimPlot(object = objectA, reduction = "umap",label=T,label.size = 8))
dev.off()
cat("Process was done!\n")
return(objectA)
}
### DEGs ----
runDEGspng <- function(objectA, prefix, dirOut){
# set dir
subDir <- paste0("out_DEGs_",prefix)
setwd(dirOut)
dir.create(subDir)
setwd(subDir)
# DEGs
#Identify conserved cell type markers: for two groups
DefaultAssay(object = objectA) <- "RNA"
# scale data
objectA <- ScaleData(object = objectA)
#find differentially expressed gene markers for every cluster compared to all remaining cells
objA.markers <- FindAllMarkers(object = objectA, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
head(objA.markers)
# select top 20, 50 genes of each cluster
aTop2 <- objA.markers %>% group_by(cluster) %>% top_n(2, avg_log2FC)
aTop5 <- objA.markers %>% group_by(cluster) %>% top_n(5, avg_log2FC)
aTop10 <- objA.markers %>% group_by(cluster) %>% top_n(10, avg_log2FC)
aTop50 <- objA.markers %>% group_by(cluster) %>% top_n(50, avg_log2FC)
aTop100 <- objA.markers %>% group_by(cluster) %>% top_n(100, avg_log2FC)
# write table - top50 markers
write.csv(aTop50, paste0("out_top50markers_eachCluster_",prefix,".csv"),row.names= F)
write.csv(aTop100, paste0("out_top100markers_eachCluster_",prefix,".csv"), row.names= F)
# save - DEG.obj
saveRDS(objA.markers,paste0("objA.marker_",prefix,".rds"))
# Heatmap
p1<-DoHeatmap(object = objectA, features = aTop10$gene) + NoLegend()
#pdf(paste0("1_1_plot_heatmap_top10_",prefix,".pdf"), height = 20)
png(paste0("1_1_plot_heatmap_top10_",prefix,".png"))
print(p1)
dev.off()
p2<-DoHeatmap(object = subset(objectA,downsample=500), features = aTop10$gene) + NoLegend()
#pdf(paste0("1.2_plot_heatmap_top10_by100cells_",prefix,".pdf"), height = 20)
png(paste0("1_2_plot_heatmap_top10_by100cells_",prefix,".png"))
print(p2)
dev.off()
# plot_dot_top5
p3<- DotPlot(object = objectA, features = rev(x = unique(aTop5$gene)), dot.scale = 8, assay = "RNA",
col.min = 0) + RotatedAxis()
#pdf(paste0("1.3.plot_dot_markers_top5_",prefix,"_1.pdf"), height=4,width = 15)
png(paste0("1_3_plot_dot_markers_top5_",prefix,".png"), height=400,width = 1200)
print(p3)
dev.off()
p4<- DotPlot(object = objectA, features = rev(x = unique(aTop10$gene)), dot.scale = 8, assay = "RNA",
col.min = 0) + RotatedAxis()
#pdf(paste0("1.4.plot_dot_markers_top10_",prefix,".pdf"), height=4,width = 25)
png(paste0("1_4_plot_dot_markers_top10_",prefix,".png"), height=400,width = 2000)
print(p4)
dev.off()
return(objA.markers)
}
# test DEGs
# runDEGs(objectA,prefix = "test",dirOut = dirOut
checkMarkersHsHasATAC <- function(objectA,markerSet,prefix,dirOut){
#markerSet #Mlist1
markerSetNames <- names(markerSet)
print(names(markerSet))
# set dir
subDir <- paste0("out_check_markerGeneExpression_",prefix)
setwd(dirOut)
dir.create(subDir)
setwd(subDir)
allGenes <- rownames(objectA)
for (i in 1:length(markerSetNames)) {
cat ("process_marker_set: ",i,"\n")
mgene1 <- markerSet[[i]]
mgeneIS1 <- toupper(mgene1)
# set plot size
iwid <- 8+length(mgeneIS1)*1/3
if(length(mgeneIS1)/4 <=1){iwid2<-300*length(mgeneIS1)} else{iwid2= 1200}
ihig2=300*ceiling((length(mgeneIS1)/4))
ihig3=3*ceiling((length(mgeneIS1)/4))
# dot plot
pdf(paste0(i,"_1_plot_dot_markers_",markerSetNames[i],"_",prefix,".pdf"),width = iwid)
print(DotPlot(object = objectA,features = unique(mgeneIS1), dot.scale = 6, assay = "ATAC",col.min = 0, scale.max = 80,
cols = c("gray","magenta1")) + RotatedAxis() + theme(axis.text.x = element_text(angle = 45,hjust=1)) +
theme(panel.grid.major = element_line(colour = "grey95", size = (.1)))+ggtitle(paste0("Marker gene expression:",markerSetNames[i],"- ",prefix))) #+ coord_flip
dev.off()
# feature plot
#pdf(paste0(prefix, "_2.2_featureplot_celltype.markers.pdf"),width = 9, height =15 )
png(paste0(i,"_2_featureplot_celltype_markers_",markerSetNames[i],"_",prefix,"2.png"),width = iwid2, height = ihig2)
print(FeaturePlot(object = objectA, features = mgeneIS1, pt.size=.1, min.cutoff = 0, ncol = 3)+
labs(caption = paste0("TissueMarker: ",markerSetNames[i],"- Sample: ",prefix)))
dev.off()
# vlnplot
#png(paste0(prefix,"_2_4_vlnplot_celltype.markers_",markerSetNames[i],"_",prefix,".png"),width = iwid2*1.5, height = ihig2))
pdf(paste0(i,"_3_vlnplot_celltype_markers_",markerSetNames[i],"_",prefix,".pdf"),width = 12, height =ihig3)
print(VlnPlot(object = objectA,features = mgeneIS1,pt.size = 0, ncol = 3)+
labs(caption = paste0("TissueMarker: ",markerSetNames[i],"- Sample: ",prefix)))
dev.off()
}
#print(markerSet)
print("Process was done!")
tg <-c("Rho","Arr3","Opn1mw","Glul","Grm6","Prkca","Vstm2b","Grik1","Sox6","Slitrk5","Gad1","Calb2","Thy1","Lhx1","C1qa","Rpe65")
tg <- toupper(tg)
#"Grm6(BC.on)","Prkca(BC)","Vstm2b(RBC)","Grik1(BC.off)","Sox6(BC.5A)","Slitrk5(BC.5C)","Gad1","Calb2(AC.sac)","Thy1(RGC)","Lhx1(HC)","C1qa(Mic)"
### check marker-feature/Vlnplot
pdf(paste0(prefix,"_2_1_dotplot_celltype.marker.pdf"),width = 12, height =9 )
print(DotPlot(object = objectA,features = tg, dot.scale = 6, assay = "RNA",col.min = 0, scale.max = 80,
cols = c("gray","magenta1")) + RotatedAxis() + theme(axis.text.x = element_text(angle = 45,hjust=1)) +
theme(panel.grid.major = element_line(colour = "grey95", size = (.1)))) #+ coord_flip
dev.off()
#pdf(paste0(prefix, "_2.2_featureplot_celltype.markers.pdf"),width = 9, height =15 )
png(paste0(prefix,"_2_2_featureplot_celltype.markers.png"), width=800, height = 1200, units="px")
print(FeaturePlot(object = objectA, features = tg, pt.size=.1, min.cutoff = 0, ncol = 3))
dev.off()
pdf(paste0(prefix,"_2_3_featureplot_celltype.markers.pdf"), width=9, height = 3)
#png(paste0(prefix,"_2.3_featureplot_celltype.markers.png"), width=600, height = 200, units="px")