##########################################细分
library(Seurat)
library(dplyr)
###############Myeloid
Myeloid<-subset(sce.mergeTEN, idents = c(16))
Myeloid <- NormalizeData(Myeloid, normalization.method = "LogNormalize", scale.factor = 10000)
Myeloid <- FindVariableFeatures(Myeloid, selection.method = "vst", nfeatures = 2000)
Myeloid@[email protected]
length(Myeloid@[email protected])
###scaling the data###
all.genes <- rownames(Myeloid)
all.genes[1000:10010]
Myeloid <- ScaleData(Myeloid,vars.to.regress = c("percent.mt"))
Myeloid
###perform linear dimensional reduction###
Myeloid <- RunPCA(Myeloid, features = VariableFeatures(object = Myeloid))
print(Myeloid[["pca"]], dims = 1:5, nfeatures = 5)
#dev.off()
VizDimLoadings(Myeloid, dims = 1:2, reduction = "pca")
ElbowPlot(Myeloid,ndims = 50)
####cluster the cells###
Myeloid <- FindNeighbors(Myeloid, dims = 1:30)
####Run non-linear dimensional reduction (UMAP/tSNE)###
Myeloid <- RunUMAP(Myeloid, dims = 1:30)
Myeloid<-RunTSNE(Myeloid,dims=1:30)
Myeloid <- FindClusters(Myeloid, resolution = 1)###
DimPlot(Myeloid, reduction = "umap",label = T)
DimPlot(Myeloid, reduction = "tsne",label = T)
table(Idents(Myeloid))
#FindAllMarkers
Myeloid.markers <- FindAllMarkers(Myeloid, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
markers_df = Myeloid.markers %>% group_by(cluster)
write.csv(markers_df,"/home/yifan/project/LJ.22.02.sc/rdata/Myeloid.markers.csv")
########################判断细胞种类
############巨噬细胞6；M1（FCGR3A）和M2（CD163）
FeaturePlot(Myeloid,features = c("Cd163","Cd68","Cd11b","F4/80","MerTK","Cd68",
"Fcgr3a","Cd163"),min.cutoff = 0,max.cutoff = 0.01,reduction = "umap",label = T)
###########################SingleR对小鼠免疫单细胞自动注释
library(SingleR)
sce_for_SingleR <- GetAssayData(Myeloid, slot="data")
[email protected]$seurat_clusters
#############
mouseImmu <- ImmGenData()
pred.mouseImmu <- SingleR(test = sce_for_SingleR, ref = mouseImmu, labels = mouseImmu$label.main,
method = "cluster", clusters = clusters,
assay.type.test = "logcounts", assay.type.ref = "logcounts")
#################
mouseRNA <- MouseRNAseqData()
pred.mouseRNA <- SingleR(test = sce_for_SingleR, ref = mouseRNA, labels = mouseRNA$label.fine ,
method = "cluster", clusters = clusters,
assay.type.test = "logcounts", assay.type.ref = "logcounts")
cellType=data.frame(ClusterID=levels([email protected]$seurat_clusters),
mouseImmu=pred.mouseImmu$labels,
mouseRNA=pred.mouseRNA$labels )
###############展示柱状图
library(ggplot2)
###################
ggplot() + geom_bar(data = [email protected], aes(x = orig.ident, fill = factor(seurat_clusters)),
+ position = "fill")
ggplot() + geom_bar(data = [email protected], aes(x = seurat_clusters, fill = factor(orig.ident)),
+ position = "fill")
########
DimPlot(Myeloid, reduction = "umap",label = TRUE, group.by="orig.ident")
DimPlot(Myeloid,reduction = "umap",label = T,split.by = "orig.ident")
FeaturePlot(Myeloid,features = c("Zabp1"),min.cutoff = 0,max.cutoff = 0.01,reduction = "umap",label = T)
#############
saveRDS(Myeloid,file="/home/yifan/project/LJ.22.02.sc/rdata/Myeloid.rds")