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Alyssa Salazar
AlyssaS/
RenalTumor
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#' @description: annotated the cell type



library(Seurat)

library(harmony)

library(clustree)

library(ggpubr)

library(dplyr)

library(patchwork)

library(ComplexHeatmap)

library(circlize)

library(vegan)

set.seed(101)

library(openxlsx)

library(future)

plan("multiprocess", workers = 10) 

options(future.globals.maxSize = 50000 * 1024^2) # set 50G RAM

setwd(dir = "/data/active_data/lzl/RenalTumor-20200713/DataAnalysis-20210803/scRNA")

source(file = "/home/longzhilin/Analysis_Code/code/ratio.plot.R")



#### Harmony corrected result

data.merge <- readRDS("data.merge.harmony.PC40.SCT.feature3000.rds")

DefaultAssay(data.merge) <- "RNA"



pdf("2.Cluster/cluster.pdf")

DimPlot(object = data.merge, reduction = 'tsne',label = TRUE, group.by = "seurat_clusters")+NoLegend()

DimPlot(object = data.merge, reduction = 'umap',label = TRUE, group.by = "seurat_clusters")+NoLegend()

dev.off()



#### Plot the ratio of each cell type and sample situation

pdf("2.Cluster/cluster.number.ratio.pdf", height = 4, width = 7)

ratio.plot(seurat.object = data.merge, id.vars1 = "orig.ident", id.vars2 = "seurat_clusters")

dev.off()



expMatrix <- GetAssayData(data.merge, slot = "scale.data")

highVariableGenes <- VariableFeatures(data.merge)

expMatrix.high <- expMatrix[highVariableGenes,]

meanExpCluster <- apply(expMatrix.high, 1, function(x){

    mean.value <- tapply(x, data.merge$seurat_clusters, mean)

    return(mean.value)

})



corrMatrix <- (1- cor(t(meanExpCluster), method="pearson"))/2

library(ape)

## dd <- dist(M)

hc <- hclust(as.dist(corrMatrix),method="complete")

pdf("2.Cluster/clustersimilarity.HVG.pdf")

plot.phylo(as.phylo(hc), type = "fan", cex = 0.8,no.margin = FALSE)

dev.off()



#### Differential expression

Idents(data.merge) <- data.merge$seurat_clusters

cluster.all.markers <- FindAllMarkers(data.merge, only.pos = TRUE, group.by = "seurat_clusters", test.use = "MAST", latent.vars = "orig.ident")

cluster.sig.markers <- cluster.all.markers[which(cluster.all.markers$p_val_adj<0.05),]

saveRDS(cluster.sig.markers, file = "2.Cluster/AnnotateCellType/cluster.sig.markers.rds")

write.table(cluster.sig.markers, file = "2.Cluster/AnnotateCellType/cluster.sig.markers.txt", quote = F, sep = "\t", row.names = F)

cluster.sig.markers.top <- cluster.sig.markers %>% group_by(cluster) %>% top_n(n = 30, wt = avg_log2FC)

write.table(cluster.sig.markers.top, file = "2.Cluster/AnnotateCellType/cluster.sig.markers.top30.txt", col.names = T, row.names = F, sep = "\t", quote = F)

top.genes <- cluster.sig.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_log2FC)

pdf("2.Cluster/AnnotateCellType/cluster.topgenes.pdf")

DoHeatmap(data.merge, features = unique(top.genes$gene), size = 2) + NoLegend()

dev.off()



####Method 1: The expression of the classic marker

cell.type.markers <- read.table(file = "2.Cluster/AnnotateCellType/cellMarker.txt", header = T, stringsAsFactors = F, sep = "\t")

exp.matrix <- GetAssayData(data.merge, slot = "data")

index <- match(cell.type.markers$Gene, rownames(exp.matrix))

gene.matrix <- exp.matrix[na.omit(index),]

cell.type.markers <- cell.type.markers[which(!is.na(index)),]



# Calculate the average expression of the cell type of each cluster

cluster.score <- apply(gene.matrix, 1, function(x){

    a <- tapply(x, data.merge$seurat_clusters, mean)

    return(a)

})

cluster.score.normailzed <- decostand(cluster.score, "range", 2) 

cellType.cluster.score <- apply(cluster.score, 1, function(x){

    a <- tapply(x, cell.type.markers$CellType, mean)

    return(a)

})

cellType.cluster.score.normailzed <- decostand(cellType.cluster.score, "range", 1)

annotation.colors <- Palettes$stallion2[1:length(unique(cell.type.markers$CellType))]

names(annotation.colors) <- unique(cell.type.markers$CellType)

row.annotations <- rowAnnotation(Type = factor(cell.type.markers$CellType, 

                                 levels = unique(cell.type.markers$CellType)),

                                 col = list(Type = annotation.colors))

pdf("2.Cluster/AnnotateCellType/cluster.signature.expression.pdf")

col_fun1 <- colorRamp2(c(0, 3), c("white", "#ff5a36"))

col_fun2 <- colorRamp2(c(0, 0.5, 1), c("#1e90ff", "white", "#ff5a36"))



row_split <- factor(cell.type.markers$CellType, levels = unique(cell.type.markers$CellType))

Heatmap(t(cluster.score.normailzed), col = col_fun2, row_split = row_split, width = unit(10, "cm"), height = unit(12, "cm"), cluster_columns = F, cluster_rows = F, show_column_names = T, show_row_names = T, row_names_gp = gpar(fontsize = 6), column_names_gp = gpar(fontsize = 6),

        heatmap_legend_param = list(

                title = "Expression", at = c(0, 1), 

                labels = c("min", "max")))

Heatmap(cellType.cluster.score, name = "Expression", col = col_fun1, width = unit(8, "cm"), height = unit(8, "cm"), cluster_columns = T , cluster_rows = T, show_column_names = T, show_row_names = T, row_names_gp = gpar(fontsize = 8), column_names_gp = gpar(fontsize = 6))

Heatmap(cellType.cluster.score.normailzed, col = col_fun2, width = unit(8, "cm"), height = unit(8, "cm"), cluster_columns = T , cluster_rows = F, show_column_names = T, show_row_names = T, row_names_gp = gpar(fontsize = 8), column_names_gp = gpar(fontsize = 6), 

        heatmap_legend_param = list(

                title = "Expression", at = c(0, 1), 

                labels = c("min", "max")))



a <- data.merge

a$seurat_clusters <- factor(a$seurat_clusters, levels = rownames(cluster.score.normailzed))

DotPlot(a, cols = c("#1e90ff", "#ff5a36"), features = cell.type.markers$Gene, group.by = "seurat_clusters", dot.scale = 4) + RotatedAxis() + theme(axis.text.x = element_text(size = 6)) + theme(axis.text.y = element_text(size = 6))

DotPlot(a, cols = c("#1e90ff", "#ff5a36"), features = cell.type.markers$Gene, group.by = "seurat_clusters", dot.scale = 4) + RotatedAxis() + NoLegend()+ theme(axis.text.x = element_text(size = 8)) + theme(axis.text.y = element_text(size = 8))

dev.off()



#####annotated the cell type

#RNA low resolution

cluster.label <- data.merge@meta.data$seurat_clusters

cluster.label <- gsub("^0$", "NK/NKT cell", cluster.label)

cluster.label <- gsub("^1$", "Macrophage", cluster.label)

cluster.label <- gsub("^2$", "CD8+ T cell", cluster.label)

cluster.label <- gsub("^3$", "CD4+ T cell", cluster.label)

cluster.label <- gsub("^4$", "Tumor", cluster.label)

cluster.label <- gsub("^5$", "Endothelium (VCAM1-)", cluster.label)

cluster.label <- gsub("^6$", "Mesangial cell", cluster.label)

cluster.label <- gsub("^7$", "Monocyte", cluster.label) # --- monocyte CD14

cluster.label <- gsub("^8$", "Mast cell", cluster.label)

cluster.label <- gsub("^9$", "B cell", cluster.label)

cluster.label <- gsub("^10$", "B cell", cluster.label)

cluster.label <- gsub("^11$", "Monocyte", cluster.label) # --- monocyte CD16

cluster.label <- gsub("^12$", "Endothelium (VCAM1+)", cluster.label)

cluster.label <- gsub("^13$", "Treg", cluster.label)

cluster.label <- gsub("^14$", "Dendritic cell", cluster.label)

cluster.label <- gsub("^15$", "Unknown1", cluster.label)

cluster.label <- gsub("^16$", "Proliferative CD8+ T cell", cluster.label)

cluster.label <- gsub("^17$", "Neutrophil", cluster.label)

cluster.label <- gsub("^18$", "Unknown2", cluster.label)

data.merge <- AddMetaData(data.merge, cluster.label, col.name = "cellType_low")

# Remove cells that cannot be annotated or the cell number less than 100 cells

data.merge.pro <- subset(data.merge, subset = seurat_clusters %in% c(0:14, 16,17))

data.merge.pro$seurat_clusters <- factor(data.merge.pro$seurat_clusters, levels = c(0:14, 16,17))

saveRDS(data.merge.pro, "data.merge.pro.rds")



pdf("2.Cluster/AnnotateCellType/cellType.pro.pdf")

DimPlot(object = data.merge, reduction = 'tsne',label = TRUE, group.by = "cellType_low")+NoLegend()

DimPlot(object = data.merge, reduction = 'tsne',label = TRUE, group.by = "cellType_low")

DimPlot(object = data.merge, reduction = 'umap',label = TRUE, group.by = "cellType_low")+NoLegend()

DimPlot(object = data.merge, reduction = 'umap',label = TRUE, group.by = "cellType_low")

dev.off()



##Plot--- celltype marker plot

plot.cellType <- c("CD4+ T cell", "Treg", "CD8+ T cell", "NK/NKT cell", "Proliferative CD8+ T cell", "B cell", "Macrophage", "Monocyte", "Dendritic cell", "Neutrophil", "Mast cell", "Endothelium (VCAM1+)", "Endothelium (VCAM1-)", "Mesangial cell", "Tumor")

pdf("2.Cluster/AnnotateCellType/cellType.ratio.pdf", height = 4, width = 7)

ratio.plot(seurat.object = data.merge, id.vars1 = "orig.ident", id.vars2 = "cellType_low", angle = 60)

dev.off()



pdf("2.Cluster/AnnotateCellType/cellType.marker.dotplot.pdf", height = 4)

data.merge$cellType_low <- factor(data.merge$cellType_low, levels = plot.cellType)

DotPlot(data.merge, cols = c("#1e90ff", "#F15F30"), features = cell.type.markers$Gene, group.by = "cellType_low", dot.scale = 3.5)  + NoLegend() + theme(axis.text.y = element_text(size = 4)) + labs(x= "", y = "") + theme(axis.text.x = element_text(size = 6, angle = 90))

DotPlot(data.merge, cols = c("#1e90ff", "#F15F30"), features = cell.type.markers$Gene, group.by = "cellType_low", dot.scale = 3.5)  + theme(axis.text.y = element_text(size = 4)) + labs(x= "", y = "") + theme(axis.text.x = element_text(size = 5, angle = 90))

dev.off()



#### Cell type specific gene

Idents(data.merge) <- data.merge$cellType_low

idents <- as.character(levels(data.merge))

cellType.all.markers <- FindAllMarkers(data.merge, 

                                       group.by = "cellType_low", 

                                       logfc.threshold = 0, 

                                       min.pct = 0.1, 

                                       test.use = "MAST", 

                                       latent.vars = "orig.ident")

saveFormat <- lapply(idents, function(x){

  index <- which(cellType.all.markers$cluster == x)

  DEGs <- cellType.all.markers[index,]

  DEGs.up <- DEGs %>% filter(avg_log2FC>0) %>% arrange(desc(avg_log2FC))

  DEGs.down <- DEGs %>% filter(avg_log2FC<0) %>% arrange(avg_log2FC)

  DEGs <- rbind(DEGs.up, DEGs.down)

  return(DEGs)

})

write.xlsx(saveFormat, file = "2.Cluster/AnnotateCellType/celltype.all.DEGs.xlsx", sheetName = idents, rowNames = F)

saveRDS(cellType.all.markers, file = "2.Cluster/AnnotateCellType/cellType.all.DEGs.rds")



#require logfc.threshold >= 0.25 & p_val_adj < 0.05

cellType.sig.DEGs <- cellType.all.markers %>% filter(avg_log2FC >=0.25 & p_val_adj < 0.05) %>% arrange(desc(avg_log2FC))

saveFormat <- lapply(idents, function(x){

  index <- which(cellType.sig.DEGs$cluster == x)

  DEGs <- cellType.sig.DEGs[index,]

  DEGs <- DEGs %>% arrange(desc(avg_log2FC))

  return(DEGs)

})

write.xlsx(saveFormat, file = "2.Cluster/AnnotateCellType/cellType.sig.pos.DEGs.xlsx", sheetName = idents, rowNames = F)

saveRDS(cellType.sig.DEGs, file = "2.Cluster/AnnotateCellType/cellType.sig.pos.DEGs.rds")

top.genes <- cellType.sig.DEGs %>% group_by(cluster) %>% top_n(n = 5, wt = avg_log2FC)

pdf("2.Cluster/AnnotateCellType/cellType.topgenes.pdf")

DoHeatmap(data.merge, features = unique(top.genes$gene), size = 2) + NoLegend()

dev.off()



Tumor.info <- data.merge@meta.data[which(data.merge@meta.data$cellType_low=="Tumor"),]

cell.number <- as.data.frame(table(Tumor.info$orig.ident))

pdf("2.Cluster/AnnotateCellType/Tumor.patient.pdf")

ggbarplot(cell.number, x="Var1", y="Freq", fill = "Var1", color = "Var1", palette = Palettes$group_pal[1:length(unique(Tumor.info$orig.ident))],

          sort.by.groups=FALSE,

          label = T, xlab = "", ylab = "Cell Number") + theme(legend.position="none") 

dev.off()



cellType.ratio <- as.data.frame(table(data.merge$cellType_low))

cellType.ratio$Type <- rep("Lymphoid cells", nrow(cellType.ratio))

idx <- which(cellType.ratio$Var1 %in% c("Macrophage", "Monocyte", "Dendritic cell", "Neutrophil", "Mast cell"))

cellType.ratio$Type[idx] <- "Myeloid cells"

idx <- which(cellType.ratio$Var1 %in% c("Endothelium (VCAM1+)", "Endothelium (VCAM1-)", "Mesangial cell"))

cellType.ratio$Type[idx] <- "Other cells"

idx <- which(cellType.ratio$Var1 == "Tumor")

cellType.ratio$Type[idx] <- "Tumor cells"

group.ratio <- tapply(cellType.ratio$Freq, cellType.ratio$Type, sum)

group.ratio <- data.frame(Type = names(group.ratio), Ratio = group.ratio)

labs <- paste0(group.ratio$Type, " (", round(group.ratio$Ratio/sum(group.ratio$Ratio), 4)*100, "%)")

pdf("2.Cluster/AnnotateCellType/lineage.ratio.pdf")

p <- ggpie(group.ratio, "Ratio", label = labs,

      fill = "Type", color = "white", lab.pos = "in",

      palette = "npgs")

print(p)

dev.off()



#### functional enrichment

## GSEA software --- recalculate the result

a <- data.frame(geneName = Tumor.DEGs$gene, Rank = Tumor.DEGs$avg_log2FC)

write.csv(a, file = "2.Cluster/AnnotateCellType/GSEA/Tumor.DEGs.csv")



#### The proportion of various cell types in each patient

library(plotly)

pdf("2.Cluster/AnnotateCellType/cellType.patient.ratio.pdf")

patients <- unique(data.merge$orig.ident)

res <- sapply(patients, function(x){

  a <- subset(data.merge, subset = orig.ident==x)

  cellType.ratio <- as.data.frame(table(as.character(a$cellType_low)))

  group.ratio <- cellType.ratio$Freq/sum(cellType.ratio$Freq)

  group.ratio <- data.frame(Type = cellType.ratio$Var1, Ratio = group.ratio)

  labs <- paste0(round(group.ratio$Ratio, 4)*100, "%")

  p <- ggpie(group.ratio, "Ratio", label = labs,

        fill = "Type", color = "white", lab.pos = "out",

        palette = "npgs")

  print(p)

})

dev.off()