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Biorxiv_Fig5
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Biorxiv_Fig5
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library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(harmony)
library(EnsDb.Mmusculus.v79)
library(ggplot2)
library(tibble)
library(dplyr)
library(harmony)
library(BuenColors)
library(openxlsx)
set.seed(1234)
LEUK <- readRDS("IRIatac_LEUK.rds")
LEUKrna <- readRDS("IRIrna_LEUK.rds") #From Kirita et al
LEUKrna@meta.data[["subtype"]] <- LEUKrna@active.ident
DefaultAssay(LEUK) <- "RNA"
transfer_anchors <- FindTransferAnchors(reference = LEUKrna, query = LEUK, features = VariableFeatures(object = LEUKrna),
reference.assay = "RNA", query.assay = "RNA", reduction = "cca")
subltype.predictions <- TransferData(anchorset = transfer_anchors, refdata = LEUKrna$subtype,
weight.reduction = LEUK[["lsi"]], dims = 2:30)
LEUK <- AddMetaData(LEUK,subltype.predictions)
Idents(LEUK) <- "predicted.id"
levels(LEUK) <-levels(LEUKrna)
FigS10A <- DimPlot(LEUK,label = T,repel = T) #700x560
FigS10B <- VlnPlot(LEUK,"prediction.score.max",pt.size = 0) #630x340
LEUK@meta.data[["predicted.id"]] <- LEUK@active.ident
Idents(LEUK) <- "subtype"
Fig5A_1 <- DimPlot(LEUKrna)+NoLegend() #500x450
features <- c("Cd80","Mrc1","Itga9","Plcb1","Gpnmb","Flt3","Themis","Cd19")
levels(LEUKrna) <- rev(levels(LEUKrna)) #859x473
Fig5A_2 <- DotPlot(LEUKrna, features = features, cols = c("lightyellow","darkred")) +
RotatedAxis() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) #630x390
levels(LEUKrna) <- rev(levels(LEUKrna))
Fig5B_1 <- VlnPlot(LEUKrna,"Ccr2",pt.size = 0.1)+NoLegend() #630x340
Fig5B_2 <- VlnPlot(LEUKrna,"Csf1r",pt.size = 0.1)+NoLegend() #630x340
DefaultAssay(LEUK) <- "peaks"
Idents(LEUK) <- "subtype"
Fig5C <- DimPlot(LEUK)+NoLegend()
Fig5E_Csf1r <-CoveragePlot(
object = LEUK,
region = "chr18-61105000-61107000",
annotation = T,
peaks = F,
extend.upstream = 0,
extend.downstream = 0,
)
Fig5E_cd80 <-CoveragePlot(
object = LEUK,
region = "chr16-38456500-38458500",
annotation = T,
peaks = F,
extend.upstream = 0,
extend.downstream = 0,
)
Fig5E_itga9 <-CoveragePlot(
object = LEUK,
region = "chr9-118605750-118607750",
annotation = T,
peaks = F,
extend.upstream = 0,
extend.downstream = 0,
)
Fig5E_mrc1 <-CoveragePlot(
object = LEUK,
region = "chr2-14228000-14230000",
annotation = T,
peaks = F,
extend.upstream = 0,
extend.downstream = 0,
)
Fig5E_Plcb1 <-CoveragePlot(
object = LEUK,
region = "chr2-134785500-134787500",
annotation = T,
peaks = F,
extend.upstream = 0,
extend.downstream = 0,
)
Fig5E_ccr2 <-CoveragePlot(
object = LEUK,
region = "chr9-124101000-124103000",
annotation = T,
peaks = F,
extend.upstream = 0,
extend.downstream = 0,
)
Fig5E_flt3 <-CoveragePlot(
object = LEUK,
region = "chr5-147399250-147401250",
annotation = T,
peaks = F,
extend.upstream = 0,
extend.downstream = 0,
)
Fig5E_themis <-CoveragePlot(
object = LEUK,
region = "chr10-28667500-28669500",
annotation = T,
peaks = F,
extend.upstream = 0,
extend.downstream = 0,
)
Fig5E_cd19 <-CoveragePlot(
object = LEUK,
region = "chr7-126414000-126416000",
annotation = T,
peaks = F,
extend.upstream = 0,
extend.downstream = 0,
)
LEUK@meta.data[["subtype"]] <- LEUK@active.ident
LEUK@meta.data[["subtype2"]] <- paste0(LEUK@meta.data[["subtype"]],"-",LEUK@meta.data[["time"]])
stat <- as.data.frame(table(LEUK@meta.data[["subtype2"]]))
write.csv(stat,"IRIatac_LEUK_percentage.csv") #process to obtain the percentage in each time point whole dataset
LEUKpercentage <- read.csv("LEUKpercentage_final.csv")
#convert data frame from a "wide" format to a "long" format
library(reshape2)
lm = melt(LEUKpercentage, id = "time")
ggplotColours <- function(n = 6, h = c(0, 360) + 15){
if ((diff(h) %% 360) < 1) h[2] <- h[2] - 360/n
hcl(h = (seq(h[1], h[2], length = n)), c = 100, l = 65)
}
colours <- ggplotColours(n=8)
library(ggplot2)
Fig5D = ggplot(lm, aes(x = time, fill = variable, y = value)) +
geom_bar(stat = "identity", colour = "black") +
scale_y_continuous(limits = c(0, 0.05),expand = c(0, 0)) +theme(
panel.background = element_rect(fill='transparent')) +
scale_fill_manual(values = colours)
#transcription factor motifs
DefaultAssay(LEUK) <- "chromvar"
library(JASPAR2022)
library(TFBSTools)
# Get a list of motif position frequency matrices from the JASPAR database
pfm <- getMatrixSet(
x = JASPAR2022,
opts = list(collection = "CORE", tax_group = 'vertebrates', all_versions = F)
)
LEUK2 <- subset(LEUK, idents = c("Mac1","Mac2","Mac3","Mac4","Mac5"))
motif_marker <- FindAllMarkers(
object = LEUK2,
only.pos = TRUE,
mean.fxn = rowMeans,
fc.name = "avg_diff"
)
motifLookup <- motif_marker$gene
motifNames <- sapply(motifLookup, function(x) pfm@listData[[x]]@name)
motif_marker$gene2<- motifNames
motif_marker %>%
group_by(cluster) %>%
top_n(n = 3, wt = avg_diff) -> top3
unique_tf.list <- unique(top3$gene)
top3 <- top3[!duplicated(top3$gene),]
LEUK2[['chromvar']]@scale.data <- LEUK2[['chromvar']]@data
aver_chromvar <- AverageExpression(LEUK2, assays = "chromvar", features = unique_tf.list,slot = "scale.data")
mat <- aver_chromvar[["chromvar"]]
rownames(mat) <- paste0(top3$gene2," (",top3$gene,")")
Fig5G <- pheatmap::pheatmap(mat,scale = "row",cluster_cols=F,cluster_rows = T,color =jdb_palette("brewer_yes"))
Idents(LEUK2) <- "time"
FigS11A <- VlnPlot(LEUK2,"MA0727.1",pt.size = 0)+NoLegend()
FigS11B <- VlnPlot(LEUK2,"MA1128.1",pt.size = 0)+NoLegend()
Idents(LEUK2) <- "subtype"
DefaultAssay(LEUK2) <- "RNA"
features <- c("Il1b","Cd80","Nos2","Chil3", "Tnf","C1qc","Mrc1","Cd86","Tgfb1","Ccl2","Arg1","Cd163","Il10","Ccr2")
levels(LEUK2) <- rev(levels(LEUK2))
Fig5F <- DotPlot(LEUK2, features = features, cols = c("lightyellow","darkred")) +
RotatedAxis() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
levels(LEUK2) <- rev(levels(LEUK2))