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scFunctions.R
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##QC plots visualization function
seurat_qc<- function(sobject,name,folder,nFeature_RNA_min,nFeature_RNA_max,percent_mito,percent_ribo,
nCount_RNA_min, nCount_RNA_max){
sobject[["percent.mt"]] <- PercentageFeatureSet(sobject, pattern = "^MT-")
sobject[["percent.rb"]] <- PercentageFeatureSet(sobject, pattern = "^RP[SL]")
counts_df<- data.frame('Read_Counts'[email protected]['nCount_RNA'], 'Metric'='Read_Counts','Filter'='OG')
colnames(counts_df)<- c('Read_Counts','Metric','Filter')
genes_df<- data.frame('Gene_Counts'[email protected]['nFeature_RNA'],'Metric'='Gene_Counts','Filter'='OG')
colnames(genes_df)<- c('Gene_Counts','Metric','Filter')
mito<- data.frame("percent.mt"[email protected]['percent.mt'],'Metric'= 'MT%','Filter'='OG')
sobject <- subset(sobject, subset = nFeature_RNA>nFeature_RNA_min & nFeature_RNA<nFeature_RNA_max & percent.mt<percent_mito & percent.rb< percent_ribo &
nCount_RNA < nCount_RNA_max & nCount_RNA > nCount_RNA_min )
counts_df_qc<- data.frame('Read_Counts'[email protected]['nCount_RNA'], 'Metric'='Read_Counts','Filter'='QC')
colnames(counts_df_qc)<- c('Read_Counts','Metric','Filter')
genes_df_qc<- data.frame('Gene_Counts'[email protected]['nFeature_RNA'],'Metric'='Gene_Counts','Filter'='QC')
colnames(genes_df_qc)<- c('Gene_Counts','Metric','Filter')
mito_qc<- data.frame("percent.mt"[email protected]['percent.mt'],'Metric'= 'MT%','Filter'='QC')
counts_df<-rbind(counts_df,counts_df_qc)
genes_df<-rbind(genes_df,genes_df_qc)
mito<- rbind(mito,mito_qc)
p1<-ggplot(counts_df, aes(x=Metric, y=Read_Counts)) + geom_violin(fill='firebrick4') +
theme_bw() + facet_grid(cols = vars(Metric), rows = vars(Filter), scales = "free")+
geom_dotplot(binaxis='y',stackdir='center',alpha=0.2, color='gray81', dotsize=0.5)
p2<-ggplot(genes_df, aes(x=Metric, y=Gene_Counts)) + geom_violin(fill='turquoise4') +
theme_bw() + facet_grid(cols = vars(Metric),rows = vars(Filter), scales = "free")+
geom_dotplot(binaxis='y',stackdir='center',alpha=0.2, color='gray81', dotsize=0.5 )
p3<-ggplot(mito, aes(x=Metric, y=percent.mt)) + geom_violin(fill='forestgreen') +
theme_bw() + facet_grid(cols = vars(Metric),rows = vars(Filter), scales = "free")+
geom_dotplot(binaxis='y',stackdir='center',alpha=0.2, color='gray81', dotsize=0.5)
setEPS()
postscript(paste0(folder,name,".eps"))
plot(p1+p2+p3)
dev.off()
return(sobject)
}
####NORMALIZATION FUNCTION
seurat_norm<- function(sobject, method=NULL, regress.out=c()){
DefaultAssay(sobject) <- "RNA"
if(method=="SCT"){
print("SCT is applied")
sobject=SCTransform(sobject, vars.to.regress = regress.out, verbose=FALSE )
p=VariableFeaturePlot(sobject, selection.method="sct")
#p2 <- LabelPoints(plot = p, points = sobject@[email protected][0:10], repel = TRUE)
print(p)
return(sobject)
}
else if(method=="vst"){
sobject <- NormalizeData(sobject)
sobject <- FindVariableFeatures(sobject, selection.method = "vst",nfeatures = 2000, verbose=FALSE)
sobject <- ScaleData(sobject, vars.to.regress = regress.out)
return(sobject)
}
}
####INTERGRATION FUNCTION
integration<- function(sobject_list,method=NULL, normalization=NULL, resolution=NULL){
if(length(resolution)>1){
resolutions=paste(resolution, collapse = '_')
}
else{resolutions=resolution}
if(method=='Seurat' && normalization=='vst'){
print("Plot unintegrated data")
norm_list <- lapply(X = sobject_list, FUN = function(x) {
x<-NormalizeData(x)
x<- FindVariableFeatures(x, selection.method = normalization, nfeatures = 2000)
})
features <- SelectIntegrationFeatures(object.list = norm_list, nfeatures = 2000)
unintegrated_data<- merge(norm_list[[1]], y=c(norm_list[-1]))
unintegrated_data <- ScaleData(unintegrated_data, verbose = FALSE)
unintegrated_data <- RunPCA(unintegrated_data, npcs = 30, verbose = FALSE, features=features)
p1<-ElbowPlot(unintegrated_data)
unintegrated_data <- RunUMAP(unintegrated_data, reduction = "pca", dims = 1:15,verbose = FALSE)
unintegrated_data <- FindNeighbors(unintegrated_data, reduction = "pca", dims = 1:15,verbose = FALSE)
unintegrated_data <- FindClusters(unintegrated_data, resolution = resolution,verbose = FALSE)
p2 <- DimPlot(unintegrated_data, reduction = "umap", group.by = "Group")
print(p1+p2)
print("Perform integration with Seurat and vst")
features <- SelectIntegrationFeatures(object.list = norm_list)
anchors <- FindIntegrationAnchors(object.list = norm_list, anchor.features = features)
integrated <- IntegrateData(anchorset = anchors)
DefaultAssay(integrated) <- "integrated"
integrated <- ScaleData(integrated, verbose = FALSE)
integrated <- RunPCA(integrated, npcs = 30, verbose = FALSE)
integrated <- RunUMAP(integrated, reduction = "pca", dims = 1:15,verbose = FALSE)
integrated <- FindNeighbors(integrated, reduction = "pca", dims = 1:15,verbose = FALSE)
integrated <- FindClusters(integrated, resolution = resolution,verbose = FALSE)
umap_values<-integrated@[email protected]
group_values<[email protected]$Group
write.table(group_values, paste(method,normalization,resolutions,'group.txt',sep='_'))
write.table(umap_values, paste(method,normalization,resolutions,'umap.txt',sep='_'))
#p3 <- DimPlot(integrated, reduction = "umap", group.by = "Group")
#p4 <- DimPlot(integrated, reduction = "umap", label = TRUE, repel = TRUE)
#print(p3 + p4)
return(integrated)
}
else if(method=='Seurat' && normalization=='SCT'){
print("Perform integration with Seurat and SCT")
norm_list <- lapply(X = sobject_list, FUN = SCTransform)
features <- SelectIntegrationFeatures(object.list = norm_list, nfeatures = 3000)
norm_list <- PrepSCTIntegration(object.list = norm_list, anchor.features = features)
anchors <- FindIntegrationAnchors(object.list = norm_list, normalization.method = "SCT",anchor.features = features)
integrated <- IntegrateData(anchorset = anchors, normalization.method = "SCT")
integrated <- RunPCA(integrated, verbose = FALSE)
integrated <- RunUMAP(integrated, reduction = "pca", dims = 1:30,verbose = FALSE)
integrated <- FindNeighbors(integrated, reduction = "pca", dims = 1:30,verbose = FALSE)
integrated <- FindClusters(integrated, resolution = resolution)
umap_values<-integrated@[email protected]
group_values<[email protected]$Group
write.table(group_values, paste(method,normalization,resolutions,'group.txt',sep='_'))
write.table(umap_values, paste(method,normalization,resolutions,'umap.txt',sep='_'))
#p1 <- DimPlot(integrated, reduction = "umap", group.by = "Group")
#p2 <- DimPlot(integrated, reduction = "umap", label = TRUE, repel = TRUE)
#print(p1 + p2)
return(integrated)
}
else if(method=='Harmony'&& normalization=='SCT'){
library(harmony)
library(SeuratData)
norm_list <- lapply(X = sobject_list, FUN = SCTransform,do.scale=T,do.center=T)
features <- SelectIntegrationFeatures(object.list = norm_list, nfeatures = 3000)
merged_object<- merge(norm_list[[1]], y=c(norm_list[-1]))
merged_object <- RunPCA(merged_object, npcs = 30, features=features, assay='SCT')
integrated <- merged_object %>% RunHarmony("Group", plot_convergence = FALSE, assay.use='SCT')
integrated <- integrated%>% RunUMAP(reduction = "harmony", dims = 1:10) %>% FindNeighbors(reduction = "harmony", dims = 1:10) %>%
FindClusters(resolution = resolution) %>%
identity()
umap_values<-integrated@[email protected]
group_values<[email protected]$Group
write.table(group_values, paste(method,normalization,resolutions,'group.txt',sep='_'))
write.table(umap_values, paste(method,normalization,resolutions,'umap.txt',sep='_'))
#p1=DimPlot(integrated, reduction = "umap", pt.size = .1,group.by = "Group")
#p2=DimPlot(integrated, reduction = "umap", label = TRUE, repel = TRUE)
#print(p1+p2)
return(integrated)
}
else if(method=='Harmony'&& normalization=='vst'){
library(harmony)
library(SeuratData)
merged_object<- merge(sobject_list[[1]], y=c(sobject_list[-1]))
merged_norm_object<- seurat_norm(merged_object, method="vst",)
merged_norm_object <- RunPCA(merged_norm_object, npcs = 30)
integrated <- merged_norm_object %>% RunHarmony("Group", plot_convergence = FALSE)
integrated <- integrated%>% RunUMAP(reduction = "harmony", dims = 1:10) %>%FindNeighbors(reduction = "harmony", dims = 1:10) %>%
FindClusters(resolution = resolution,graph.name=) %>%
identity()
umap_values<-integrated@[email protected]
group_values<[email protected]$Group
write.table(group_values, paste(method,normalization,resolutions,'group.txt',sep='_'))
write.table(umap_values, paste(method,normalization,resolutions,'umap.txt',sep='_'))
#p1=DimPlot(integrated, reduction = "umap", pt.size = .1,group.by = "Group")
#p2=DimPlot(integrated, reduction = "umap", label = TRUE, repel = TRUE)
#print(p1+p2)
return(integrated)
}
}
###MULTIOME QC
seurat_qc_multiome<- function(sobject,nCount_ATAC_min,nCount_ATAC_max,
nCount_RNA_min, nCount_RNA_max, nFeature_RNA_min,nFeature_RNA_max,percent_mito,percent_ribo, TSS, nucl_signal){
DefaultAssay(sobject) <- "RNA"
sobject[["percent.mt"]] <- PercentageFeatureSet(sobject, pattern = "^MT-")
sobject[["percent.rb"]] <- PercentageFeatureSet(sobject, pattern = "^RP[SL]")
counts_df<- data.frame('Read_Counts'[email protected]['nCount_RNA'], 'Metric'='Read_Counts','Filter'='OG')
colnames(counts_df)<- c('Read_Counts','Metric','Filter')
genes_df<- data.frame('Gene_Counts'[email protected]['nFeature_RNA'],'Metric'='Gene_Counts','Filter'='OG')
colnames(genes_df)<- c('Gene_Counts','Metric','Filter')
mito<- data.frame("percent.mt"[email protected]['percent.mt'],'Metric'= 'MT%','Filter'='OG')
sobject <- subset(sobject, subset = nFeature_RNA>nFeature_RNA_min & nFeature_RNA<nFeature_RNA_max & percent.mt<percent_mito & percent.rb< percent_ribo &
nCount_RNA < nCount_RNA_max & nCount_RNA > nCount_RNA_min )
counts_df_qc<- data.frame('Read_Counts'[email protected]['nCount_RNA'], 'Metric'='Read_Counts','Filter'='QC')
colnames(counts_df_qc)<- c('Read_Counts','Metric','Filter')
genes_df_qc<- data.frame('Gene_Counts'[email protected]['nFeature_RNA'],'Metric'='Gene_Counts','Filter'='QC')
colnames(genes_df_qc)<- c('Gene_Counts','Metric','Filter')
mito_qc<- data.frame("percent.mt"[email protected]['percent.mt'],'Metric'= 'MT%','Filter'='QC')
counts_df<-rbind(counts_df,counts_df_qc)
genes_df<-rbind(genes_df,genes_df_qc)
mito<- rbind(mito,mito_qc)
DefaultAssay(sobject) <- "ATAC"
sobject <- TSSEnrichment(sobject)
sobject <- NucleosomeSignal(sobject)
atac_df<- data.frame("ATAC_Counts"[email protected]['nCount_ATAC'],'Metric'= 'ATAC_Counts','Filter'='OG')
colnames(atac_df)<- c('ATAC_Counts','Metric','Filter')
tss.enrich<- data.frame("TSS.enrichment"[email protected]["TSS.enrichment"], 'Metric'= "TSS.enrichment",'Filter'='OG' )
nucl.sign<- data.frame("nucleosome_signal"[email protected]["nucleosome_signal"], 'Metric'= "nucleosome_signal",'Filter'='OG' )
sobject <- subset(sobject, subset = nCount_ATAC>nCount_ATAC_min & nCount_ATAC<nCount_ATAC_max &
TSS.enrichment > TSS & nucleosome_signal<nucl_signal)
atac_df_qc<- data.frame("ATAC_Counts"[email protected]['nCount_ATAC'],'Metric'= 'ATAC_Counts','Filter'='QC')
colnames(atac_df_qc)<- c('ATAC_Counts','Metric','Filter')
tss.enrich_qc<- data.frame("TSS.enrichment"[email protected]["TSS.enrichment"], 'Metric'= "TSS.enrichment",'Filter'='QC' )
nucl.sign_qc<- data.frame("nucleosome_signal"[email protected]["nucleosome_signal"], 'Metric'= "nucleosome_signal",'Filter'='QC' )
atac_df<-rbind(atac_df,atac_df_qc)
tss.enrich_df<- rbind(tss.enrich,tss.enrich_qc)
nucl.sign_df<- rbind(nucl.sign,nucl.sign_qc)
p1<-ggplot(counts_df, aes(x=Metric, y=Read_Counts)) + geom_violin(fill='firebrick4') +
theme_bw() + facet_grid(cols = vars(Metric), rows = vars(Filter), scales = "free")+
geom_dotplot(binaxis='y',stackdir='center',alpha=0.2, color='gray81', dotsize=0.5)
p2<-ggplot(genes_df, aes(x=Metric, y=Gene_Counts)) + geom_violin(fill='turquoise4') +
theme_bw() + facet_grid(cols = vars(Metric),rows = vars(Filter), scales = "free")+
geom_dotplot(binaxis='y',stackdir='center',alpha=0.2, color='gray81', dotsize=0.5 )
p3<-ggplot(mito, aes(x=Metric, y=percent.mt)) + geom_violin(fill='forestgreen') +
theme_bw() + facet_grid(cols = vars(Metric),rows = vars(Filter), scales = "free")+
geom_dotplot(binaxis='y',stackdir='center',alpha=0.2, color='gray81', dotsize=0.5)
p4<-ggplot(atac_df, aes(x=Metric, y=ATAC_Counts)) + geom_violin(fill='turquoise4') +
theme_bw() + facet_grid(cols = vars(Metric),rows = vars(Filter), scales = "free")+
geom_dotplot(binaxis='y',stackdir='center',alpha=0.2, color='gray81', dotsize=0.5 )
p5<-ggplot(tss.enrich_df, aes(x=Metric, y=TSS.enrichment)) + geom_violin(fill='firebrick4') +
theme_bw() + facet_grid(cols = vars(Metric),rows = vars(Filter), scales = "free")+
geom_dotplot(binaxis='y',stackdir='center',alpha=0.2, color='gray81', dotsize=0.5)
p6<-ggplot(nucl.sign_df, aes(x=Metric, y=nucleosome_signal)) + geom_violin(fill='forestgreen') +
theme_bw() + facet_grid(cols = vars(Metric),rows = vars(Filter), scales = "free")+
geom_dotplot(binaxis='y',stackdir='center',alpha=0.2, color='gray81', dotsize=0.5)
print(p1+p2+p3+p4+p5+p6)
return(sobject)
}
####ATAC QC
seurat_qc_atac<- function(sobject,nCount_ATAC_min,nCount_ATAC_max,pct.peaks, TSS, nucl_signal){
sobject$pct_reads_in_peaks <- sobject$peak_region_fragments / sobject$passed_filters * 100
sobject$blacklist_ratio <- sobject$blacklist_region_fragments / sobject$peak_region_fragments
atac_df<- data.frame("ATAC_Counts"[email protected]['nCount_ATAC'],'Metric'= 'ATAC_Counts','Filter'='OG')
colnames(atac_df)<- c('ATAC_Counts','Metric','Filter')
pct_reads<- data.frame('Reads_in_Peaks'[email protected]['pct_reads_in_peaks'], 'Metric'= '%Reads_in_Peaks','Filter'='OG')
colnames(pct_reads)<- c('Reads_in_Peaks','Metric','Filter')
tss.enrich<- data.frame("TSS.enrichment"[email protected]["TSS.enrichment"], 'Metric'= "TSS.enrichment",'Filter'='OG' )
nucl.sign<- data.frame("nucleosome_signal"[email protected]["nucleosome_signal"], 'Metric'= "nucleosome_signal",'Filter'='OG' )
sobject <- subset(sobject, subset = nCount_ATAC>nCount_ATAC_min & nCount_ATAC<nCount_ATAC_max & pct_reads_in_peaks >pct.peaks &
TSS.enrichment > TSS & nucleosome_signal<nucl_signal)
atac_df_qc<- data.frame("ATAC_Counts"[email protected]['nCount_ATAC'],'Metric'= 'ATAC_Counts','Filter'='QC')
colnames(atac_df_qc)<- c('ATAC_Counts','Metric','Filter')
pct_reads_qc<- data.frame('Reads_in_Peaks'[email protected]['pct_reads_in_peaks'], 'Metric'= '%Reads_in_Peaks','Filter'='QC')
colnames(pct_reads_qc)<- c('Reads_in_Peaks','Metric','Filter')
tss.enrich_qc<- data.frame("TSS.enrichment"[email protected]["TSS.enrichment"], 'Metric'= "TSS.enrichment",'Filter'='QC' )
nucl.sign_qc<- data.frame("nucleosome_signal"[email protected]["nucleosome_signal"], 'Metric'= "nucleosome_signal",'Filter'='QC' )
atac_df<-rbind(atac_df,atac_df_qc)
pct_reads_df<- rbind(pct_reads,pct_reads_qc)
tss.enrich_df<- rbind(tss.enrich,tss.enrich_qc)
nucl.sign_df<- rbind(nucl.sign,nucl.sign_qc)
p1<-ggplot(atac_df, aes(x=Metric, y=ATAC_Counts)) + geom_violin(fill='turquoise4') +
theme_bw() + facet_grid(cols = vars(Metric),rows = vars(Filter), scales = "free")+
geom_dotplot(binaxis='y',stackdir='center',alpha=0.2, color='gray81', dotsize=0.5 )
p2<-ggplot(pct_reads_df, aes(x=Metric, y=Reads_in_Peaks)) + geom_violin(fill='dodgerblue3') +
theme_bw() + facet_grid(cols = vars(Metric),rows = vars(Filter), scales = "free")+
geom_dotplot(binaxis='y',stackdir='center',alpha=0.2, color='gray81', dotsize=0.5 )
p3<-ggplot(tss.enrich_df, aes(x=Metric, y=TSS.enrichment)) + geom_violin(fill='firebrick4') +
theme_bw() + facet_grid(cols = vars(Metric),rows = vars(Filter), scales = "free")+
geom_dotplot(binaxis='y',stackdir='center',alpha=0.2, color='gray81', dotsize=0.5)
p4<-ggplot(nucl.sign_df, aes(x=Metric, y=nucleosome_signal)) + geom_violin(fill='forestgreen') +
theme_bw() + facet_grid(cols = vars(Metric),rows = vars(Filter), scales = "free")+
geom_dotplot(binaxis='y',stackdir='center',alpha=0.2, color='gray81', dotsize=0.5)
print(p1+p2+p3+p4)
return(sobject)
}
##PEAK CALLING UNSING MACS2
callpeaks<- function(sobject,fragmentpath){
DefaultAssay(sobject) <- "ATAC"
peaks <- CallPeaks(sobject,macs2.path = "/Users/u0149445/opt/anaconda3/bin/macs2")
# remove peaks on nonstandard chromosomes and in genomic blacklist regions
peaks <- keepStandardChromosomes(peaks, pruning.mode = "coarse")
peaks <- subsetByOverlaps(x = peaks, ranges = blacklist_hg38_unified, invert = TRUE)
macs2_counts <- FeatureMatrix(
fragments = Fragments(sobject),
features = peaks,
cells = colnames(sobject)
)
sobject[["peaks"]] <- CreateChromatinAssay(
counts = macs2_counts,
fragments = fragmentpath,
annotation = annotation
)
return(sobject)
}
###READ ATAC OBJECT
make_atac_object<- function(h5_path,fragment_path,metadata_path, metadata, annotation, combined_peaks){
atac_counts <- Read10X_h5(h5_path)
# grange.counts <- StringToGRanges(rownames(atac_counts), sep = c(":", "-"))
# grange.use <- seqnames(grange.counts) %in% standardChromosomes(grange.counts)
# atac_counts <- atac_counts[as.vector(grange.use), ]
#barcodes<- read.table(barcode_path, sep = ',', header = TRUE)
#barcodes<- barcodes[barcodes$atac_barcode %in% colnames(atac_counts),]
#barcodes <- barcodes[order(barcodes$atac_barcode),]
# metadata <- read.csv(file = metadata_path,header = TRUE, row.names = 1)
# metadata<- metadata[,c("peak_region_fragments","blacklist_region_fragments","passed_filters")]
# metadata <-metadata[rownames(metadata) %in% colnames(atac_counts),]
# fragpth <-fragment_path
# atac_assay <- CreateChromatinAssay( counts = atac_counts, sep = c(":", "-"), fragments=fragpth,
#annotation = annotation,min.cells = 1)
# atac_object <- CreateSeuratObject(counts = atac_assay, assay = "ATAC",meta.data = metadata)
#atac_object<- RenameCells(atac_object, new.names=barcodes$gex_barcode)
# return(atac_object)
atac_counts <- FeatureMatrix(
fragments <- CreateFragmentObject(path = fragment_path, cells = colnames(atac_counts)),
features = combined_peaks,
cells = colnames(atac_counts)
)
grange.counts <- StringToGRanges(rownames(atac_counts), sep = c(":", "-"))
grange.use <- seqnames(grange.counts) %in% standardChromosomes(grange.counts)
atac_counts <- atac_counts[as.vector(grange.use), ]
fragpth <-fragment_path
atac <- CreateChromatinAssay( counts =atac_counts, sep = c(":", "-"), fragments=fragpth, annotation = annotation)
atac_object <- CreateSeuratObject(counts = atac, assay = "ATAC")
}
##Make combined peaks
combine_peaks<- function(bed_list){
grList=list()
for(i in bed_list){
bed<-read.table(i, col.names = c("chr", "start", "end"))
grList[[i]]<-makeGRangesFromDataFrame(bed)
}
print(grList)
combined.peaks <- GenomicRanges::reduce(x = c( grList[[1]], grList[[2]] ))
peakwidths <- width(combined.peaks)
combined.peaks <- combined.peaks[peakwidths < 10000 & peakwidths > 20]
chrs <-c("chr1","chr2","chr3","chr4","chr5","chr6","chr7","chr8","chr9","chr10","chr11","chr12","chr13","chr14","chr15","chr16","chr17","chr18","chr19","chr20","chr21","chr22","chrX","chrY")
combined.peaks<-combined.peaks[which(combined.peaks@seqnames %in% chrs),]
return(combined.peaks)
}
###READ Multiome OBJECT
make_multiome_object<- function(h5_path,fragment_path,annotation,combined_peaks){
counts <- Read10X_h5(h5_path)
rna_counts <- counts$`Gene Expression`
object <- CreateSeuratObject(counts = rna_counts,assay = "RNA", min.cells=3)
#atac_counts <- counts$Peaks
#grange.counts <- StringToGRanges(rownames(atac_counts), sep = c(":", "-"))
#grange.use <- seqnames(grange.counts) %in% standardChromosomes(grange.counts)
#atac_counts <- atac_counts[as.vector(grange.use), ]
atac_counts <- FeatureMatrix(
fragments <- CreateFragmentObject(path = fragment_path, cells = colnames(object)),
features = combined_peaks,
cells = colnames(object)
)
grange.counts <- StringToGRanges(rownames(atac_counts), sep = c(":", "-"))
grange.use <- seqnames(grange.counts) %in% standardChromosomes(grange.counts)
atac_counts <- atac_counts[as.vector(grange.use), ]
fragpth <-fragment_path
object[['ATAC']] <- CreateChromatinAssay( counts =atac_counts, sep = c(":", "-"), fragments=fragpth, annotation = annotation)
#new_counts <- FeatureMatrix(
## fragments = Fragments(object),
# features = combined_peaks,
# cells = colnames(object)
# )
return(object)
}
###NORMALIZE ATAC DATA
normalize_atac<- function(sobject){
DefaultAssay(sobject) <- "peaks"
sobject <- FindTopFeatures(sobject, min.cutoff = 5)
sobject <- RunTFIDF(sobject)
sobject <- RunSVD(sobject)
return(sobject)
}
make_UMAP<- function(sobject,name, folder, dims, resolution, res_to_plot){
sobject<- RunPCA(sobject,verbose = FALSE)
p<-ElbowPlot(sobject)
sobject<-FindNeighbors(sobject, dims = 1:dims,verbose = FALSE)
sobject <- FindClusters(sobject, verbose = FALSE, resolution = res_to_plot, algorithm =1)
sobject <- RunUMAP(sobject, dims = 1:dims,verbose = FALSE)
c=DiscretePalette(25,"alphabet")
df1=data.frame(sobject@[email protected])
df1['plot']="UMAP"
df1['cluster'][email protected]$seurat_clusters
p1=ggplot(df1, aes(x=UMAP_1, y=UMAP_2,color=cluster)) + geom_point() + scale_color_manual(values=c) + theme_bw() +
facet_grid(cols = vars(plot), scales = "free")
df<- [email protected][,c('nCount_RNA','seurat_clusters')]
df['plot']="Read_Counts"
p2=ggplot(df, aes(x=seurat_clusters, y=nCount_RNA, fill=as.factor(seurat_clusters))) + geom_bar(stat = "identity") + scale_fill_manual(values=c) +
facet_grid(cols = vars(plot), scales = "free")
all<-data.frame()
for(i in order(as.numeric(levels(df$seurat_clusters)))-1){
cluster_df <- as.data.frame(df %>% subset(seurat_clusters == i)) #%>% .$Group %>% table() #/ table(df$seurat_clusters))
cluster_df<- sum(cluster_df$nCount_RNA)/nrow(cluster_df)
all<-rbind(all,cluster_df)
}
colnames(all)<-'average_read'
all['cluster']<-order(as.numeric(levels(df$seurat_clusters)))-1
all['plot']="Average_reads"
p3<- ggplot(all, aes(x=cluster, y=average_read, fill=as.factor(cluster))) + geom_bar(stat = "identity") + scale_fill_manual(values=c) +
facet_grid(cols = vars(plot), scales = "free")
print(p+p1+p2+p3)
setEPS()
postscript(paste0(folder,name,".eps"), width=12)
plot(p+p1+p2+p3)
dev.off()
sobject <- FindClusters(sobject, verbose = FALSE,resolution = resolutions, algorithm =1)
return(sobject)
}
###MAKE LOOM FILE
make_loom<- function(name,sobject,marker_file_list){
build_loom(
file.name=name,
dgem=sobject@assays$RNA@counts,
title="ASAP_integrated", # A name for your data
genome="Human", # Just for user information, not used internally
default.embedding = sobject@[email protected],
default.embedding.name = "umap_gex")
loom <- open_loom(name, mode = "r+")
add_seurat_clustering(loom = loom,
seurat = sobject,
seurat.assay = "RNA",
seurat.clustering.prefix = "integrated_snn_res.",
default.clustering.resolution = "res.0.5",
seurat.markers.file.path.list = marker_file_list,
seurat.marker.metric.accessors = c("avg_log2FC", "p_val_adj"),
seurat.marker.metric.names = c("Avg.log2FC", "adjusted P-value"),
seurat.marker.metric.description =
c("Average log fold change", "Adjusted p-value (BF)")
)
close_loom(loom= loom)
}
runSoupx<- function(filt, rawh5, resolution, name){
filt.matrix=Read10X(filt)
raw.matrix=Read10X_h5(rawh5,use.names = T)
soup.channel <- SoupChannel(raw.matrix, filt.matrix)
srat=CreateSeuratObject(counts = filt.matrix)
srat <- SCTransform(srat, verbose = F)
srat <- RunPCA(srat, verbose = F)
srat <- RunUMAP(srat, dims = 1:30, verbose = F)
srat <- FindNeighbors(srat, dims = 1:30, verbose = F)
srat <- FindClusters(srat, verbose = T, resolution = 0.5)
soup.channel <- setClusters(soup.channel, setNames([email protected]$seurat_clusters, rownames([email protected])))
soup.channel <- setDR(soup.channel, srat@[email protected])
soup.channel <- autoEstCont(soup.channel,forceAccept=TRUE)
adj.matrix <- adjustCounts(soup.channel, roundToInt = T)
return(adj.matrix)
# DropletUtils:::write10xCounts(paste0("soupX_",name,"_filt"), adj.matrix)
}
runSoupx_h5<- function(filt, rawh5, resolution, name){
filt.matrix=Read10X_h5(filt)
raw.matrix=Read10X_h5(rawh5,use.names = T)
soup.channel <- SoupChannel(raw.matrix, filt.matrix)
srat=CreateSeuratObject(counts = filt.matrix)
srat <- SCTransform(srat, verbose = F)
srat <- RunPCA(srat, verbose = F)
srat <- RunUMAP(srat, dims = 1:30, verbose = F)
srat <- FindNeighbors(srat, dims = 1:30, verbose = F)
srat <- FindClusters(srat, verbose = T, resolution = 0.5)
soup.channel <- setClusters(soup.channel, setNames([email protected]$seurat_clusters, rownames([email protected])))
soup.channel <- setDR(soup.channel, srat@[email protected])
soup.channel <- autoEstCont(soup.channel,forceAccept=TRUE)
adj.matrix <- adjustCounts(soup.channel, roundToInt = T)
return(adj.matrix)
# DropletUtils:::write10xCounts(paste0("soupX_",name,"_filt"), adj.matrix)
}