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Replicate_data_analysis.Rmd
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Replicate_data_analysis.Rmd
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---
title: "Replicate data analysis"
author: "Kristof Torkenczy"
date: "11/16/2021"
output: html_document
---
external function, can modify output
```{r}
plot_combined_cross_species<-function(gene,gene_mouse=NULL,upstr=1000,downstr=1000,plot_by="handannot",motif.names=NULL,highlight_human_reg=NULL,highlight_mouse_reg=NULL,validate=F){
fname<-substring(gene, 1,1)
sname<-substring(gene, 2)
if(is.null(gene_mouse))
{
gene_mouse<-paste0(toupper(fname),tolower(sname))
}
gene_human<-toupper(gene)
all_plots<-list()
# reg_h<-FindRegion(object = human_all_atac,region = gene_human,extend.downstream = downstr,extend.upstream = upstr,assay = "cre")
# reg_m<-FindRegion(object = mouse_select_atac,region = gene_mouse,extend.downstream = downstr,extend.upstream = upstr,assay = "mm10")
reg_h <- LookupGeneCoords(object = human_all_atac,gene = gene_human,assay = "cre")
reg_h <- suppressWarnings(expr = Extend(x = reg_h, upstream = upstr,downstream = downstr))
reg_m <- LookupGeneCoords(object = mouse_select_atac,gene = gene_mouse,assay = "mm10")
reg_m <- suppressWarnings(expr = Extend(x = reg_m, upstream = upstr,downstream = downstr))
#findOverlaps(subject=reg_h,query = highlight_human_reg)
print(paste0("plotting gene: ", gene))
print(paste0("plotting human: ", gene_human," human: ",reg_h))
print(paste0("plotting mouse: ", gene_mouse," mouse: ",reg_m))
mm10_phylp="/home/torkenczyk/conservation_tracks/mm10.60way.phyloP60way.bw"
hg38_phylp="/home/torkenczyk/conservation_tracks/hg38.phyloP100way.bw"
mm10_lecif="/home/torkenczyk/conservation_tracks/mm10.hg38.LECIF.srt.median.bw"
hg38_lecif="/home/torkenczyk/conservation_tracks/hg38.mm10.LECIF.srt.median.bw"
# conservation_mm10<-BigwigTrack(region = reg_m,bigwig = mm10_phylp,type = 'heatmap')
#conservation_hg38<-BigwigTrack(region = reg_h,bigwig = hg38_phylp,type = 'heatmap')
hg38_A549_a="/home/torkenczyk/H_M_chromatin_atlas/validation_sets/A459/GSM3137776_ENCFF631HEX_signal_of_unique_reads_GRCh38.bigWig"
hg38_A549_b="/home/torkenczyk/H_M_chromatin_atlas/validation_sets/A459/GSM3137777_ENCFF103COS_signal_of_unique_reads_GRCh38.bigWig"
hg38_A549_c="/home/torkenczyk/H_M_chromatin_atlas/validation_sets/A459/GSM3137778_ENCFF957GCK_signal_of_unique_reads_GRCh38.bigWig"
if(!is.null(motif.names)){
print("Adding Motifs")
print("For Hg38")
ls<-unlist(GetMotifData(object = human_all_atac, assay = "cre", slot = "motif.names"))
motif_labels<-names(ls[match(motif.names,ls)])
M<-GetMotifData(human_all_atac, assay = "cre")[,motif_labels]
M2<-apply(M,2,function(x){which(x>0)})
M2n<-apply(M,2,function(x){which(x==0)})
Mdf<-data.frame(motifs=rep("",times=length(row.names(M))))
M<-as.matrix(M)
for (mt in names(M2)){
c_ct<-match(mt,colnames(M))
mt_n<-ls[match(mt,names(ls))]
M[M2[[mt]],c_ct]<-mt_n
M[M2n[[mt]],c_ct]<-""
}
M<-as.data.frame(M)
M <- M %>% unite(motif, colnames(M), sep = ',')
M$motif<-gsub(M$motif,perl=T,pattern = ",{2,}",replacement = "")
M$motif<-gsub(M$motif,perl=T,pattern = "^,",replacement = "")
M$motif<-gsub(M$motif,perl=T,pattern = ",$",replacement = "")
M$motif[which(M$motif=="")]<-"No motif"
human_all_atac[["cre"]]<-AddMetaData(human_all_atac[["cre"]],M)
mouse_select_atac[["cre"]]<-AddMetaData(mouse_select_atac[["cre"]],M)
print("For MM10")
ls<-unlist(GetMotifData(object = mouse_select_atac, assay = "mm10", slot = "motif.names"))
motif_labels<-names(ls[match(motif.names,ls)])
M<-GetMotifData(mouse_select_atac,assay = "mm10")[,motif_labels]
M2<-apply(M,2,function(x){which(x>0)})
M2n<-apply(M,2,function(x){which(x==0)})
Mdf<-data.frame(motifs=rep("",times=length(row.names(M))))
M<-as.matrix(M)
for (mt in names(M2)){
c_ct<-match(mt,colnames(M))
mt_n<-ls[match(mt,names(ls))]
M[M2[[mt]],c_ct]<-mt_n
M[M2n[[mt]],c_ct]<-""
}
M<-as.data.frame(M)
M <- M %>% unite(motif, colnames(M), sep = ',')
M$motif<-gsub(M$motif,perl=T,pattern = ",{2,}",replacement = "")
M$motif<-gsub(M$motif,perl=T,pattern = "^,",replacement = "")
M$motif<-gsub(M$motif,perl=T,pattern = ",$",replacement = "")
#M$motif<-gsub(M$motif,perl=T,pattern = ",No motif$",replacement = "")
M$motif[which(M$motif=="")]<-"No motif"
mouse_select_atac[["mm10"]]<-AddMetaData(mouse_select_atac[["mm10"]],M)
mouse1<-CoveragePlot(
object = mouse_select_atac,links = F,assay ="mm10",
group.by=plot_by,
region = gene_mouse,
annotation=T,peaks=T,
extend.upstream = upstr,
extend.downstream = downstr,bigwig = list(mm10_phylp,mm10_lecif),bigwig.type = 'heatmap',peaks.group.by = "motif", region.highlight = highlight_mouse_reg
)
mouse2<-CoveragePlot(
object = mouse_select_atac,links = F,assay ="cre",
group.by=plot_by,
region = gene_human,
annotation=T,peaks=T,
extend.upstream = upstr,
extend.downstream = downstr,bigwig = list(hg38_phylp,hg38_lecif),bigwig.type = 'heatmap',peaks.group.by = "motif",region.highlight = highlight_human_reg
)
human<-CoveragePlot(
object = human_all_atac,
group.by=plot_by,region = gene_human,assay ="cre",
extend.upstream = upstr,
extend.downstream = downstr,annotation=T,peaks=T,bigwig = list(hg38_phylp,hg38_lecif),bigwig.type = 'heatmap',peaks.group.by = "motif", region.highlight = highlight_human_reg
)
} else {
mouse1<-CoveragePlot(
object = mouse_select_atac,links = F,assay ="mm10",
group.by=plot_by,
region = reg_m,
annotation=T,peaks=T,
extend.upstream = upstr,
extend.downstream = downstr,bigwig = list(mm10_phylp,mm10_lecif),bigwig.type = 'heatmap', region.highlight = highlight_mouse_reg
)
mouse2<-CoveragePlot(
object = mouse_select_atac,links = F,assay ="cre",
group.by=plot_by,
region = reg_h,
annotation=T,peaks=T,
extend.upstream = upstr,
extend.downstream = downstr,bigwig = list(hg38_phylp,hg38_lecif),bigwig.type = 'heatmap',region.highlight = highlight_human_reg
)
mouse2b<-CoveragePlot(
object = mouse_select_atac,links = F,assay ="cre",
group.by=plot_by,
region = reg_h,
annotation=F,peaks=F,
extend.upstream = upstr,
extend.downstream = downstr,region.highlight = highlight_human_reg)
human<-CoveragePlot(
object = human_all_atac,
group.by=plot_by,region = reg_h,assay ="cre",
extend.upstream = upstr,
extend.downstream = downstr,annotation=T,peaks=T,bigwig = list(hg38_phylp,hg38_lecif),bigwig.type = 'heatmap',region.highlight = highlight_human_reg
)
humanb<-CoveragePlot(
object = human_all_atac,
group.by=plot_by,region = reg_h,assay ="cre",
extend.upstream = upstr,
extend.downstream = downstr,annotation=T,peaks=T,bigwig = list(hg38_A549_a,hg38_A549_b,hg38_A549_c),bigwig.type = "coverage",region.highlight = highlight_human_reg
)
}
plots2<-wrap_plots(list(mouse2b,human), ncol = 1, heights = c(8,10))
print(paste0("plotting gene: ", gene))
png(filename = paste0("/brahms/torkenczyk/H_M_storage/analysis/plots/",gene,"_combined.wcons_yaxe2.png"),width = 20,height=18,units = "cm",res=300)
print(plots2)
dev.off()
ggsave(plot = plots2,filename = paste0("/brahms/torkenczyk/H_M_storage/analysis/plots/paper_pdfs/",gene,"_combined.wcons_yaxe.pdf"),width = 10,height=18,units = "cm")
print("THIS")
plots<-wrap_plots(list(mouse1,mouse2,human), ncol = 3, widths = c(10,10,10))
png(filename = paste0("/brahms/torkenczyk/H_M_storage/analysis/plots/",gene,"_combined.wcons2.png"),width = 30,height=25,units = "cm",res=300)
print(wrap_plots(list(mouse1,mouse2,human), ncol = 3, widths = c(10,10,10)))
dev.off()
all_plots[["mouse_orig"]]<-mouse1
all_plots[["mouse_cre"]]<-mouse2b
all_plots[["human_cre"]]<-human
if (validate==T){
#with mouse
print("Plotting validation of mouse")
DefaultAssay(M_subset)<-"peaks"
mouse2c<-CoveragePlot(
object = M_subset,links = F,assay ="peaks",
group.by="cell_type",
region = reg_m,
annotation=F,peaks=F,
extend.upstream = upstr,
extend.downstream = downstr,region.highlight = highlight_mouse_reg)
plots3<-wrap_plots(list(mouse2c,mouse1), ncol = 1, heights = c(8,10))
png(filename = paste0("/brahms/torkenczyk/H_M_storage/analysis/plots/",gene,"_combined.wcons_yaxe_mouse_VAL.png"),width = 20,height=18,units = "cm",res=300)
print(plots3)
dev.off()
all_plots[["mouse_val"]]<-mouse2c
#with human
print("Plotting validation of human")
DefaultAssay(integrated)<-"bins"
human_v<-CoveragePlot(object = integrated,
group.by="cluster_name",region = reg_h,assay ="bins",
extend.upstream = upstr,
extend.downstream = downstr,annotation=F,peaks=F,region.highlight = highlight_human_reg)
all_plots[["human_val"]]<-human_v
plots4<-wrap_plots(list(human_v,humanb), ncol = 1, heights = c(8,14))
print("THIS")
png(filename = paste0("/home/torkenczyk/H_M_chromatin_atlas/validation_sets/A459/plots/",gene,"_combined.wcons_yaxe_human_VAL.png"),width = 20,height=28,units = "cm",res=300)
print(plots4)
dev.off()
print("THIS2")
png(filename = paste0("/brahms/torkenczyk/H_M_storage/analysis/plots/",gene,"_combined.wcons_yaxe_human_VAL.png"),width = 20,height=28,units = "cm",res=300)
print(plots4)
dev.off()
}
return(all_plots)
}
```
Validation in a human lung dataset
```{r}
all_data<- read.table(
file = "/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultGSE161381_lung_snATAC.UMAP.cluster_labels.txt.gz",
header = TRUE,
row.names = 1
)
counts_D122 <- read.table("/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultGSM4906342_D122.mtx.gz",skip = 2)
rows_D122 <- read.table("/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultGSM4906342_D122.regions.tsv.gz")
cols_D122<-read.table("/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultGSM4906342_D122.barcodes.tsv.gz")
resize<-StringToGRanges(rows_D122$V1,sep = c(":", "-"))
end(resize)<-end(resize)-1
start(resize)<-start(resize)+1
D122_H<-sparseMatrix(i=counts_D122[,2],j=counts_D122[,1],x=counts_D122[,3])
colnames(D122_H)<-cols_D122$V1
# I have to shrink ranges
row.names(D122_H)<-paste0("chr",GRangesToString(resize))
human_D122_atac_assay <- CreateChromatinAssay(
counts = D122_H,
sep = c(":", "-"),
genome = "hg38",
fragments = NULL,
)
human_D1221_atac <- CreateSeuratObject(
counts = human_D122_atac_assay,
assay = 'bins',
project = 'ATAC',
meta.data = all_data
)
saveRDS(human_D1221_atac,file="/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultGSM4906342_D122.seurat.rds")
human_D1221_atac <- RunTFIDF(human_D1221_atac)
human_D1221_atac <- FindTopFeatures(human_D1221_atac, min.cutoff = 'q50')
human_D1221_atac <- RunSVD(human_D1221_atac)
human_D1221_atac <- RunUMAP(object = human_D1221_atac, reduction = 'lsi', dims = 2:30)
DimPlot(object = human_D1221_atac, label = TRUE,group.by = "cluster_name") + NoLegend()
saveRDS(human_D1221_atac,file="/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultGSM4906342_D122.seurat.rds")
counts_D175 <- read.table("/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultGSM4906343_D175.mtx.gz",skip = 2)
rows_D175 <- read.table("/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultGSM4906343_D175.regions.tsv.gz")
cols_D175<-read.table("/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultGSM4906343_D175.barcodes.tsv.gz")
resize<-StringToGRanges(rows_D175$V1,sep = c(":", "-"))
end(resize)<-end(resize)-1
start(resize)<-start(resize)+1
D175_H<-sparseMatrix(i=counts_D175[,2],j=counts_D175[,1],x=counts_D175[,3])
colnames(D175_H)<-cols_D175$V1
# I have to shrink ranges
row.names(D175_H)<-paste0("chr",GRangesToString(resize))
human_D175_atac_assay <- CreateChromatinAssay(
counts = D175_H,
sep = c(":", "-"),
genome = "hg38",
fragments = NULL,
)
human_D1751_atac <- CreateSeuratObject(
counts = human_D175_atac_assay,
assay = 'bins',
project = 'ATAC',
meta.data = all_data
)
saveRDS(human_D1751_atac,file="/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultGSM4906342_D175.seurat.rds")
human_D1751_atac <- RunTFIDF(human_D1751_atac)
human_D1751_atac <- FindTopFeatures(human_D1751_atac, min.cutoff = 'q50')
human_D1751_atac <- RunSVD(human_D1751_atac)
human_D1751_atac <- RunUMAP(object = human_D1751_atac, reduction = 'lsi', dims = 2:30)
DimPlot(object = human_D1751_atac, label = TRUE,group.by = "cluster_name") + NoLegend()
saveRDS(human_D1751_atac,file="/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultGSM4906342_D175.seurat.rds")
counts_D231 <- read.table("/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultGSM4906344_D231.mtx.gz",skip = 2)
rows_D231 <- read.table("/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultGSM4906344_D231.regions.tsv.gz")
cols_D231<-read.table("/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultGSM4906344_D231.barcodes.tsv.gz")
resize<-StringToGRanges(rows_D231$V1,sep = c(":", "-"))
end(resize)<-end(resize)-1
start(resize)<-start(resize)+1
D231_H<-sparseMatrix(i=counts_D231[,2],j=counts_D231[,1],x=counts_D231[,3])
colnames(D231_H)<-cols_D231$V1
# I have to shrink ranges
row.names(D231_H)<-paste0("chr",GRangesToString(resize))
human_D231_atac_assay <- CreateChromatinAssay(
counts = D231_H,
sep = c(":", "-"),
genome = "hg38",
fragments = NULL,
)
human_D2311_atac <- CreateSeuratObject(
counts = human_D231_atac_assay,
assay = 'bins',
project = 'ATAC',
meta.data = all_data
)
saveRDS(human_D2311_atac,file="/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultGSM4906342_D231.seurat.rds")
human_D2311_atac <- RunTFIDF(human_D2311_atac)
human_D2311_atac <- FindTopFeatures(human_D2311_atac, min.cutoff = 'q50')
human_D2311_atac <- RunSVD(human_D2311_atac)
human_D2311_atac <- RunUMAP(object = human_D2311_atac, reduction = 'lsi', dims = 2:30)
DimPlot(object = human_D2311_atac, label = TRUE,group.by = "cluster_name") + NoLegend()
saveRDS(human_D2311_atac,file="/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultGSM4906342_D231.seurat.rds")
human_D2311_atac$dataset <- "D231"
human_D1221_atac$dataset <- "D122"
human_D1751_atac$dataset <- "D175"
combined <- merge(
x = human_D2311_atac,
y = list(human_D1221_atac, human_D1751_atac))
# process the combined dataset
combined <- FindTopFeatures(combined, min.cutoff = 10)
combined <- RunTFIDF(combined)
combined <- RunSVD(combined)
combined <- RunUMAP(combined, reduction = "lsi", dims = 2:30)
p1 <- DimPlot(combined, group.by = "dataset")
# find integration anchors
integration.anchors <- FindIntegrationAnchors(
object.list = list(human_D2311_atac, human_D1221_atac,human_D1751_atac),
anchor.features = rownames(human_D2311_atac),
reduction = "rlsi",
dims = 2:30
)
# integrate LSI embeddings
integrated <- IntegrateEmbeddings(
anchorset = integration.anchors,
reductions = combined[["lsi"]],
new.reduction.name = "integrated_lsi",
dims.to.integrate = 1:30
)
# create a new UMAP using the integrated embeddings
integrated <- RunUMAP(integrated, reduction = "integrated_lsi", dims = 2:30)
p2 <- DimPlot(integrated, group.by = "dataset")
# extract gene annotations from EnsDb
annotations_human <- GetGRangesFromEnsDb(ensdb = EnsDb.Hsapiens.v86)
# change to UCSC style
seqlevelsStyle(annotations_human) <- 'UCSC'
genome(annotations_human) <- "hg38"
# add the gene information to the object
Annotation(integrated)<- annotations_human
saveRDS(integrated,file="/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultAll.seurat.rds")
integrated<-readRDS("/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultAll.seurat.rds")
integrated=subset(integrated,cells=colnames(integrated)[!is.na(integrated$cluster_name)])
DefaultAssay(integrated)<-"bins"
#add in hg38, which I regenerated
frags_add_fragments <- CreateFragmentObject(
path = "/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultfragments_all_human.verify.frags.bed.final.final.srt.bed.hg38.srt.gz",
cells = colnames(integrated),
validate.fragments = TRUE
)
Fragments(integrated)<-NULL
Fragments(integrated)<-frags_add_fragments
#add to validation hg19 assay
all_cre_hg19<-read.table(/brahms/torkenczyk/H_M_storage/analysis/bedfiles/ALL.bg.hg19.bed",header = F)
all_cre_hg19<-paste(all_cre_hg19$V1,all_cre_hg19$V2,all_cre_hg19$V3,sep = "-")
all_cre_hg19_gr<-reduce(StringToGRanges(all_cre_hg19,sep = c("-","-")))
fragment<-"/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultfragments_all_human.verify.frags.bed.final.final.srt.bed.hg19.gz"
fragments_hg19 <- CreateFragmentObject(
path = fragment,cells = colnames(integrated)
)
cRE_peaks_human<- FeatureMatrix(
fragments = fragments_hg19,
features = all_cre_hg19_gr,
cells = rownames(integrated[[]])
)
integrated[["hg19_cre"]] <- CreateChromatinAssay(
counts = cRE_peaks_human,
sep = c(":", "-"),
genome = "hg19",
fragments = fragments_hg19
)
saveRDS(integrated,file="/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultAll.seurat.rds")
DefaultAssay(integrated)<-"hg19_cre"
integrated <- FindTopFeatures(integrated, min.cutoff = 10)
integrated <- RunTFIDF(integrated)
integrated <- RunSVD(integrated)
integrated <- RunUMAP(integrated, reduction = "lsi", dims = 2:30)
p1 <- DimPlot(integrated, group.by = "cluster_name",label = T,pt.size = 1)
MO<-read.table(/brahms/torkenczyk/H_M_storage/analysis/bedfiles/Uniq2celline.m.Pneumocytes.hg19.bed")
HO<-read.table(/brahms/torkenczyk/H_M_storage/analysis/bedfiles/Uniq2celline.h.Pneumocytes.hg19.bed")
S<-read.table(/brahms/torkenczyk/H_M_storage/analysis/bedfiles/Shared2celline.Pneumocytes.hg19.bed")
M_granges<-StringToGRanges(paste(MO$V1,MO$V2,MO$V3,sep = "-"))
H_granges<-StringToGRanges(paste(HO$V1,HO$V2,HO$V3,sep="-"))
I_granges<-StringToGRanges(paste(S$V1,S$V2,S$V3,sep="-"))
Mat_granges<- StringToGRanges(row.names(integrated))
MO_Ol<-row.names(integrated)[unique(subjectHits(findOverlaps(M_granges,subject = Mat_granges)))]
HO_Ol<-row.names(integrated)[unique(subjectHits(findOverlaps(H_granges,subject = Mat_granges)))]
I_Ol<-row.names(integrated)[unique(subjectHits(findOverlaps(I_granges,subject = Mat_granges)))]
have_reads_h<-integrated[["hg19_cre"]][[]][integrated[["hg19_cre"]][[]]$count>0,]
matched_m<-row.names(have_reads_h) %in% MO_Ol
matched_h<-row.names(have_reads_h) %in% HO_Ol
matched_i <- row.names(have_reads_h) %in% I_Ol
m_matched_df<- data.frame(mouse=matched_m,human=matched_h,int=matched_i)
library(BSgenome.Hsapiens.UCSC.hg19)
library(chromVAR)
library(motifmatchr)
library(Matrix)
library(SummarizedExperiment)
#getChromInfoFromUCSC("hg38")
have_reads_h<-have_reads_h[-which(seqnames(rowRanges) == "chr17")[1],]
rowRanges<-StringToGRanges(row.names(have_reads_h))
anno_m <- getAnnotations(annotations = m_matched_df, rowRanges = rowRanges)
#fragments_m <- SummarizedExperiment(assays = list(counts = GetAssayData(integrated, assay = "peaks", slot = "counts")[GRangesToString(rowRanges),], rowRanges = rowRanges))
fragments_m <- SummarizedExperiment(assays = list(counts = GetAssayData(integrated, assay = "hg19_cre", slot = "counts")[row.names(have_reads_h),]), rowRanges = rowRanges)
fragments_m <- filterPeaks(fragments_m, non_overlapping = TRUE)
#fragments_m <- addGCBias(fragments_m, genome = BSgenome.Mmusculus.UCSC.mm9)
fragments_m <- addGCBias(fragments_m, genome=BSgenome.Hsapiens.UCSC.hg19)
dev_m_i <- computeDeviations(object = fragments_m, annotations = anno_m)
chromvar.hz <- SummarizedExperiment::assays(dev_m_i)[[2]]
integrated[["Deviation_DA"]] <- CreateAssayObject(data = chromvar.hz)
DefaultAssay(integrated)<-"Deviation_DA"
Idents(integrated)<-integrated$cluster_name
p1c<-RidgePlot(integrated, features = row.names(integrated), ncol = 3)
p1<-FeaturePlot(integrated, features = "mouse",min.cutoff = 0,raster = F)
p2<-FeaturePlot(integrated, features = "human",min.cutoff = 0,raster = F)
p3<-FeaturePlot(integrated, features = "int",min.cutoff = 0,raster = F)
DimPlot(integrated,label=T)
```
#There is also RNA data here so lets integrate
```{r}
integrated<-readRDS(file="/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultAll.seurat.rds")
#get RNA D122 D175 D231
mtx <- "/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultscRNA/GSE161382_counts.mtx.gz"
cells <- "/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultscRNA/GSE161382_barcodes.tsv.gz"
features <- "/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultscRNA/GSE161382_features.tsv.gz"
cell.barcodes <- read.table(file =cells, header = T, row.names = 1)
feature.names <- read.table(file =features, header = T, row.names = 1)
data <- readMM(file = mtx)
#data <- t(x = data)
colnames(x = data) <- cell.barcodes$x
rownames(x = data) <- feature.names$x
data <- as(data, Class = "dgCMatrix")
lung_RNA_all<-CreateSeuratObject(counts=data)
metadata=read.table(file = "/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultscRNA/GSE161382_metadata.txt.gz")
lung_RNA_all<-AddMetaData(lung_RNA_all,metadata = metadata)
Idents(lung_RNA_all)<-"donor"
lung_RNA_adult<-subset(lung_RNA_all,idents=c("D122","D175","D231"))
lung_RNA_adult <- NormalizeData(lung_RNA_adult)
all.genes <- rownames(lung_RNA_adult)
lung_RNA_adult <- ScaleData(lung_RNA_adult, features = all.genes)
lung_RNA_adult<-FindVariableFeatures(lung_RNA_adult)
lung_RNA_adult <- RunPCA(lung_RNA_adult, features = VariableFeatures(object = lung_RNA_adult))
ElbowPlot(lung_RNA_adult)
lung_RNA_adult <- RunUMAP(lung_RNA_adult, dims = 1:20)
DimPlot(lung_RNA_adult,group.by = "celltype",label = T)+NoLegend()
DefaultAssay(integrated)<-"hg19_cre"
integrated <- RunTFIDF(integrated)
integrated <- FindTopFeatures(integrated, min.cutoff = "q0")
integrated <- RunSVD(integrated,reduction.key = "lsi19",reduction.name = "lsi19")
integrated <- RunUMAP(integrated, reduction = "lsi19", dims = 2:30, reduction.name = "umap.atac", reduction.key = "atacUMAP_")
library("EnsDb.Hsapiens.v75")
# ATAC analysis add gene annotation information
annotations <- GetGRangesFromEnsDb(ensdb = EnsDb.Hsapiens.v75)
seqlevelsStyle(annotations) <- "UCSC"
genome(annotations) <- "hg19"
Annotation(integrated) <- annotations
gene.activities <- GeneActivity(integrated, features = VariableFeatures(lung_RNA_adult))
# add gene activities as a new assay
integrated[["ACTIVITY"]] <- CreateAssayObject(counts = gene.activities)
# normalize gene activities
DefaultAssay(integrated) <- "ACTIVITY"
integrated <- NormalizeData(integrated)
integrated <- ScaleData(integrated, features = rownames(integrated))
transfer.anchors <- FindTransferAnchors(reference = lung_RNA_adult, query = integrated, features = VariableFeatures(object = lung_RNA_adult),
reference.assay = "RNA", query.assay = "ACTIVITY", reduction = "cca")
genes.use <- VariableFeatures(lung_RNA_adult)
refdata <- GetAssayData(lung_RNA_adult, assay = "RNA", slot = "data")[genes.use, ]
# refdata (input) contains a scRNA-seq expression matrix for the scRNA-seq cells. imputation
# (output) will contain an imputed scRNA-seq matrix for each of the ATAC cells
imputation <- TransferData(anchorset = transfer.anchors, refdata = refdata, weight.reduction = integrated[["lsi19"]],
dims = 2:30)
integrated[["RNA"]] <- imputation
lung_RNA_adult$data<-"RNA"
integrated$data<-"ATAC"
coembed <- merge(x = lung_RNA_adult, y = integrated)
#create RNA imp assay
integrated[["RNA"]]<-imputation
# Finally, we run PCA and UMAP on this combined object, to visualize the co-embedding of both
# datasets
coembed <- ScaleData(coembed, features = genes.use, do.scale = FALSE)
coembed <- RunPCA(coembed, features = genes.use, verbose = FALSE)
coembed <- RunUMAP(coembed, dims = 1:30)
DimPlot(coembed,group.by = "patient")
DimPlot(coembed,group.by = "celltype",label=T)+NoLegend()
DimPlot(coembed,group.by = "data",label=T)+NoLegend()
#now link peaks together in each of the batches
DefaultAssay(integrated) <- "hg19_cre"
Idents(integrated)<-"patient"
coembed_pat1<-subset(integrated,idents="D231")
DimPlot(coembed_pat1,group.by = "patient")
DimPlot(coembed_pat1,group.by = "celltype",label=T)+NoLegend()
DimPlot(coembed_pat1,group.by = "data",label=T)+NoLegend()
rowRanges<-StringToGRanges(row.names(coembed_pat1))
rowRanges[which(seqnames(rowRanges) == "chr17")[1],]<-StringToGRanges("chr17-1-488",sep = c("-","-"))
rowRanges[which(seqnames(rowRanges) == "chr17")[1],]
feature.metadata<-RegionStats(rowRanges, genome = BSgenome.Hsapiens.UCSC.hg19,verbose = TRUE)
rownames(x = feature.metadata) <- rownames(x = coembed_pat1)
coembed_pat1[["hg19_cre"]]<-AddMetaData(coembed_pat1[["hg19_cre"]],metadata = as.data.frame(feature.metadata))
# first computed the GC content for each peak
# link peaks to genes
coembed_pat1 <- LinkPeaks(
object = coembed_pat1 ,
peak.assay = "hg19_cre",
expression.assay = "RNA",
genes.use = genes.use
)
coembed_pat2<-subset(integrated,idents="D122")
DimPlot(coembed_pat2,group.by = "patient")
DimPlot(coembed_pat2,group.by = "celltype",label=T)+NoLegend()
DimPlot(coembed_pat2,group.by = "data",label=T)+NoLegend()
rowRanges<-StringToGRanges(row.names(coembed_pat2))
rowRanges[which(seqnames(rowRanges) == "chr17")[1],]<-StringToGRanges("chr17-1-488",sep = c("-","-"))
rowRanges[which(seqnames(rowRanges) == "chr17")[1],]
feature.metadata<-RegionStats(rowRanges, genome = BSgenome.Hsapiens.UCSC.hg19,verbose = TRUE)
rownames(x = feature.metadata) <- rownames(x = coembed_pat2)
coembed_pat2[["hg19_cre"]]<-AddMetaData(coembed_pat2[["hg19_cre"]],metadata = as.data.frame(feature.metadata))
# first computed the GC content for each peak
# link peaks to genes
coembed_pat2 <- LinkPeaks(
object = coembed_pat2 ,
peak.assay = "hg19_cre",
expression.assay = "RNA",
genes.use = genes.use
)
coembed_pat3<-subset(integrated,idents="D231")
DimPlot(coembed_pat3,group.by = "patient")
DimPlot(coembed_pat3,group.by = "celltype",label=T)+NoLegend()
DimPlot(coembed_pat3,group.by = "data",label=T)+NoLegend()
rowRanges<-StringToGRanges(row.names(coembed_pat3))
rowRanges[which(seqnames(rowRanges) == "chr17")[1],]<-StringToGRanges("chr17-1-488",sep = c("-","-"))
rowRanges[which(seqnames(rowRanges) == "chr17")[1],]
feature.metadata<-RegionStats(rowRanges, genome = BSgenome.Hsapiens.UCSC.hg19,verbose = TRUE)
rownames(x = feature.metadata) <- rownames(x = coembed_pat3)
coembed_pat3[["hg19_cre"]]<-AddMetaData(coembed_pat3[["hg19_cre"]],metadata = as.data.frame(feature.metadata))
# first computed the GC content for each peak
# link peaks to genes
coembed_pat3 <- LinkPeaks(
object = coembed_pat3 ,
peak.assay = "hg19_cre",
expression.assay = "RNA",
genes.use = genes.use
)
saveRDS(integrated,"/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultscRNA/int_all.rds")
saveRDS(coembed_pat1,"/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultscRNA/pat1_int_all.rds")
saveRDS(coembed_pat2,"/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultscRNA/pat2_int_all.rds")
saveRDS(coembed_pat3,"/brahms/torkenczyk/H_M_storage/analysis/pneumocyte_val/adultscRNA/pat3_int_all.rds")
Idents(lung_RNA_adult)<-"celltype"
lung_RNA_adult <- FindNeighbors(lung_RNA_adult, dims = 1:20)
lung_RNA_adult <- FindClusters(lung_RNA_adult, resolution = 0.1)
p1<-DimPlot(lung_RNA_adult,label=T)+NoLegend()
p2<-DimPlot(lung_RNA_adult,group.by = "celltype",label=T)+NoLegend()
lung_RNA_adult$ct<-as.factor(Idents(lung_RNA_adult))
levels(lung_RNA_adult$ct)<-c("Macrophages/Mono","Pneumocytes","Pneumocytes","Pneumocytes","Fibroblasts","T-cells","Macrophages/Mono","Cap","Goblet","Cilliated","Lymph","B-cells")
Idents(lung_RNA_adult)<-"ct"
select_markers<-FindMarkers(lung_RNA_adult,ident.1 = c("Pneumocytes"),only.pos = T)
DimPlot(lung_RNA_adult)
L1<-Links(coembed_pat1)
L2<-Links(coembed_pat2)
L3<-Links(coembed_pat3)
L1_df<-as.data.frame(L1)
row.names(L1_df)<-paste(L1_df$peak,L1_df$gene,sep = "_")
L2_df<-as.data.frame(L2)
row.names(L2_df)<-paste(L2_df$peak,L2_df$gene,sep = "_")
L3_df<-as.data.frame(L3)
row.names(L3_df)<-paste(L3_df$peak,L3_df$gene,sep = "_")
union_set<-merge(L1_df,L2_df,by="row.names",all=T)
row.names(union_set)<-union_set$Row.names
union_set$Row.names<-NULL
union_set[is.na(union_set)]<-0
union_set<-merge(union_set,L3_df,by="row.names",all=T)
row.names(union_set)<-union_set$Row.names
union_set$Row.names<-NULL
union_set[is.na(union_set)]<-0
union_short_set<-data.frame(peak=str_split_fixed(row.names(union_set),pattern = "_",2)[,1],gene=str_split_fixed(row.names(union_set),pattern = "_",2)[,2],avg_score=(union_set$zscore.x+union_set$zscore.y+union_set$zscore)/3)
union_short_set_gr<-StringToGRanges(union_short_set$peak)
union_short_set_gr$gene=union_short_set$gene
union_short_set_gr$avg_score=union_short_set$avg_score
MO<-read.table(/brahms/torkenczyk/H_M_storage/analysis/bedfiles/Uniq2celline.m.Pneumocytes.hg19.bed")
HO<-read.table(/brahms/torkenczyk/H_M_storage/analysis/bedfiles/Uniq2celline.h.Pneumocytes.hg19.bed")
S<-read.table(/brahms/torkenczyk/H_M_storage/analysis/bedfiles/Shared2celline.Pneumocytes.hg19.bed")
S2<-read.table(/brahms/torkenczyk/H_M_storage/analysis/bedfiles/Shared2celline.Pneumocyte.hg19.orig.incl.bed")
M_granges<-StringToGRanges(paste(MO$V1,MO$V2,MO$V3,sep = "-"))
H_granges<-StringToGRanges(paste(HO$V1,HO$V2,HO$V3,sep="-"))
I_granges<-StringToGRanges(paste(S$V1,S$V2,S$V3,sep="-"))
MO_Ol<-union_short_set_gr[subjectHits(findOverlaps(M_granges,subject = union_short_set_gr))]
MO_Ol$orig_peak<-GRangesToString(M_granges[queryHits(findOverlaps(M_granges,subject = union_short_set_gr))])
MO$orig_peak<-paste(MO$V1,MO$V2,MO$V3,sep = "-")
MO_Ol_df<-as.data.frame(MO_Ol)
MO_fin<-merge(x=MO,y=MO_Ol_df,all=T,by="orig_peak")
MO_plot<-data.frame(peak=MO_fin$orig_peak,avg_score=MO_fin$avg_score)
MO_plot$type="mouse"
MO_plot2<-na.omit(MO_plot)
MO_plot[is.na(MO_plot)]<-0
ggplot(MO_plot,aes(x=type,y=avg_score))+geom_violin()+theme_classic()
HO_Ol<-union_short_set_gr[unique(subjectHits(findOverlaps(H_granges,subject = union_short_set_gr)))]
HO_Ol<-union_short_set_gr[subjectHits(findOverlaps(H_granges,subject = union_short_set_gr))]
HO_Ol$orig_peak<-GRangesToString(H_granges[queryHits(findOverlaps(H_granges,subject = union_short_set_gr))])
HO$orig_peak<-paste(HO$V1,HO$V2,HO$V3,sep = "-")
HO_Ol_df<-as.data.frame(HO_Ol)
HO_fin<-merge(x=HO,y=HO_Ol_df,all=T,by="orig_peak")
HO_plot<-data.frame(peak=HO_fin$orig_peak,avg_score=HO_fin$avg_score)
HO_plot$type="human"
HO_plot2<-na.omit(HO_plot)
HO_plot[is.na(HO_plot)]<-0
ggplot(HO_plot,aes(x=type,y=avg_score))+geom_violin()+geom_boxplot()+theme_classic()
I_Ol<-union_short_set_gr[unique(subjectHits(findOverlaps(I_granges,subject = union_short_set_gr)))]
I_Ol$orig_peak<-GRangesToString(I_granges[queryHits(findOverlaps(I_granges,subject = union_short_set_gr))])
S$orig_peak<-paste(S$V1,S$V2,S$V3,sep = "-")
I_Ol_df<-as.data.frame(I_Ol)
I_fin<-merge(x=S,y=I_Ol_df,all=T,by="orig_peak")
I_plot<-data.frame(peak=I_fin$orig_peak,avg_score=I_fin$avg_score)
I_plot$type="shared"
I_plot2<-na.omit(I_plot)
I_plot[is.na(I_plot)]<-0
ggplot(I_plot,aes(x=type,y=avg_score))+geom_violin()+geom_boxplot()+theme_classic()
all_comb<-rbind(MO_plot,HO_plot,I_plot)
all_comb2<-rbind(MO_plot2,HO_plot2,I_plot2)
ggplot(all_comb,aes(x=type,fill=type,y=avg_score))+geom_violin()+geom_boxplot()+theme_classic()
ggplot(all_comb2,aes(x=type,fill=type,y=avg_score))+geom_violin()+geom_boxplot()+theme_classic()
#JUST DE GENES now
union_short_set_de<-union_short_set[union_short_set$gene %in% row.names(select_markers)[select_markers$p_val_adj<0.05],]
union_short_set_gr<-StringToGRanges(union_short_set_de$peak)
union_short_set_gr$gene=union_short_set_de$gene
union_short_set_gr$avg_score=union_short_set_de$avg_score
#HO_Ol<-union_short_set_gr[unique(subjectHits(findOverlaps(H_granges,subject = union_short_set_gr)))]
HO_Ol<-union_short_set_gr[subjectHits(findOverlaps(H_granges,subject = union_short_set_gr))]
HO_Ol$orig_peak<-GRangesToString(H_granges[queryHits(findOverlaps(H_granges,subject = union_short_set_gr))])
HO$orig_peak<-paste(HO$V1,HO$V2,HO$V3,sep = "-")
HO_Ol_df<-as.data.frame(HO_Ol)
HO_fin<-merge(x=HO,y=HO_Ol_df,all=T,by="orig_peak")
HO_plot<-data.frame(peak=HO_fin$orig_peak,avg_score=HO_fin$avg_score,gene=HO_fin$gene)
HO_plot$type="human"
HO_plot2<-na.omit(HO_plot)
HO_plot[is.na(HO_plot)]<-0
MO_Ol<-union_short_set_gr[subjectHits(findOverlaps(M_granges,subject = union_short_set_gr))]
MO_Ol$orig_peak<-GRangesToString(M_granges[queryHits(findOverlaps(M_granges,subject = union_short_set_gr))])
MO$orig_peak<-paste(MO$V1,MO$V2,MO$V3,sep = "-")
MO_Ol_df<-as.data.frame(MO_Ol)
MO_fin<-merge(x=MO,y=MO_Ol_df,all=T,by="orig_peak")
MO_plot<-data.frame(peak=MO_fin$orig_peak,avg_score=MO_fin$avg_score,gene=MO_fin$gene)
MO_plot$type="mouse"
MO_plot2<-na.omit(MO_plot)
MO_plot[is.na(MO_plot)]<-0
I_Ol<-union_short_set_gr[unique(subjectHits(findOverlaps(I_granges,subject = union_short_set_gr)))]
I_Ol$orig_peak<-GRangesToString(I_granges[queryHits(findOverlaps(I_granges,subject = union_short_set_gr))])
S$orig_peak<-paste(S$V1,S$V2,S$V3,sep = "-")
I_Ol_df<-as.data.frame(I_Ol)
I_fin<-merge(x=S,y=I_Ol_df,all=T,by="orig_peak")
I_plot<-data.frame(peak=I_fin$orig_peak,avg_score=I_fin$avg_score,gene=I_fin$gene)
I_plot$type="shared"
I_plot2<-na.omit(I_plot)
I_plot[is.na(I_plot)]<-0
all_comb_de<-rbind(MO_plot,HO_plot,I_plot)
all_comb2_de<-rbind(MO_plot2,HO_plot2,I_plot2)
ggplot(all_comb_de,aes(x=type,fill=type,y=avg_score))+geom_violin()+theme_classic()+theme_classic()+ylab("Average linkage score")+xlab("")
ggplot(all_comb2_de,aes(x=type,fill=type,y=avg_score))+geom_violin()+geom_boxplot()+theme_classic()+ylab("Average linkage score")+xlab("")
CoveragePlot(coembed_pat1,region = "WWC1",extend.upstream = 5000,extend.downstream = 5000,region.highlight = StringToGRanges("chr5-167718376-167718876"),group.by = "celltype",links = T,expression.assay = "RNA")
CoveragePlot(coembed_pat1,region = "FOXA2",extend.upstream = 5000,extend.downstream = 5000,region.highlight = StringToGRanges("chr20-22564833-22565333"),group.by = "celltype",links = T,expression.assay = "RNA")
write.csv(all_comb_de,"/home/torkenczyk/H_M_chromatin_atlas/human_data/Validation_linkage_res.csv")
write.csv(all_comb2_de,"/home/torkenczyk/H_M_chromatin_atlas/human_data/Validation_linkage_res_only_int.csv")
length(unique(MO_plot2$peak))/length(unique(MO_plot$peak))
#"CTSH" "chr15-78993828-78994328"
#ATF7IP2 chr16-10695288-10695788
coembed_pat2$ct<-as.factor(coembed_pat2$celltype)
levels(coembed_pat2$ct)<-c("Macrophages/Mono","AT1","AT2","T-cells","B-cells","T-cells","Fibroblasts","Endothelial","Endothelial","Myofibroblast","Erythrocyte","Endothelial","Fibroblasts","Pericyte","Club","Endothelial","Ciliated","Basal","Neurondocrine")
Idents(coembed_pat2)<-coembed_pat2$ct
lung_RNA_adult$ct2<-as.factor(lung_RNA_adult$RNA_snn_res.0.1)
levels(lung_RNA_adult$ct2)<-c("Macrophages/Mono","AT2","AT2","AT1","Fibroblasts","T-cells","Macrophages/Mono","Cap","Goblet","Cilliated","Lymph","B-cells")
Idents(lung_RNA_adult)<-"ct2"
levs<-levels(lung_RNA_adult$ct2)
levs[2]<-"Pneumocytes"
levs[3]<-"Pneumocytes"
levels(lung_RNA_adult$ct2)<-levs
CoveragePlot(coembed_pat2,region = "CTSH",extend.upstream = 5000,extend.downstream = 50000,region.highlight = StringToGRanges("chr15-79286170-79286670"),group.by = "ct",links = T,expression.assay = "RNA")
p1<-VlnPlot(coembed_pat2,features = "CTSH",assay = "RNA",group.by = "ct")
p1bb<-VlnPlot(lung_RNA_adult,features = "CTSH",assay = "RNA",group.by = "ct2")+NoLegend()
p1b<-VlnPlot(lung_RNA_adult,features = "WWC1",assay = "RNA",group.by = "ct2")+NoLegend()
p1<-VlnPlot(coembed_pat2,features = "WWC1",assay = "RNA",group.by = "ct")+NoLegend()
#added links from patients to integrate
levels(integrated$ct)<-c("Pneumocyte","Pneumocyte","Endothelial","B-lymphocytes","Basal","Endothelial","Endothelial","Ciliated","Club","Erythrocyte","Endothelial","Macrophage","Fibroblast","Fibroblast","Myofibroblast","T-lymphocytes","Pericyte","NE","T-lymphocytes")
p_hum1<-CoveragePlot(integrated,region = "CTSH",extend.upstream = 5000,extend.downstream = 50000,region.highlight = StringToGRanges("chr15-79286170-79286670"),group.by = "ct",links = T,expression.assay = "RNA")
p_hum2<-CoveragePlot(integrated,region = "WWC1",extend.upstream = 5000,extend.downstream = 5000,region.highlight = StringToGRanges("chr5-167728749-167729249"),group.by = "ct",links = T,expression.assay = "RNA")
# extract gene annotations from EnsDb
annotations <- GetGRangesFromEnsDb(ensdb = EnsDb.Mmusculus.v79)
# change to UCSC style since the data was mapped to hg19
seqlevelsStyle(annotations) <- 'UCSC'
seqlevels(annotations)<-paste0("chr",seqlevels(annotations))
# add the gene information to the object
Annotation(M_subset) <- annotations
M_subset$ct<-as.factor(Idents(M_subset))
levs<-levels(M_subset$ct)
levs[1]<-"Pneumocytes"
levs[6]<-"Pneumocytes"
levels(M_subset$ct)<-levs
p_mouse1<-CoveragePlot(M_subset,region = "Ctsh",extend.upstream = 50000,extend.downstream = 5000,assay = "mm10",region.highlight = StringToGRanges("chr9-90007109-90007629"),group.by = "ct")
p_mouse2<-CoveragePlot(M_subset,region = "Wwc1",extend.upstream = 50000,extend.downstream = 5000,assay = "mm10",region.highlight = StringToGRanges("chr11-35971356-35971819"),group.by = "ct")
M_subsetmus
#overlap with GWAS? NO
sum(GWAS_all$coord_peak_hg38=="chr15_78993828_78994328")
sum(GWAS_all$coord_peak_hg38=="chr15_63985749_63986249")
sum(GWAS_all$coord_peak_hg38=="chr16_10601431_10601931")
sum(GWAS_all$coord_peak_hg38=="chr18_23189946_23190446")
sum(GWAS_all$coord_peak_hg38=="chr19_50215169_50215669")
sum(GWAS_all$coord_peak_hg38=="chr2_227164005_227164505")
sum(GWAS_all$coord_peak_hg38=="chr4_185303442_185303942")
sum(GWAS_all$coord_peak_hg38=="chr6_75334266_75334766")