从巨噬细胞macrophage_clean先去除线粒体再提取AM1 AM2 AM3

从巨噬细胞macrophage_clean提取AM1 AM2 AM3

library(CellChat)
library(patchwork)
library(ggplot2)
library(ggalluvial)
library(svglite)
library(Seurat)
library(openxlsx)
library(harmony)
library(dplyr)

getwd()
path="G:/silicosis/sicosis/yll/macrophage/no cluster2/0.3/pure_cluster013_in_allmerge"
dir.create(path)
setwd(path)
#https://www.jianshu.com/p/cef5663888ff
load("G:/silicosis/sicosis/silicosis-1122-merge/silicosis_cluster_merge.rds")## 改路径
table(All.merge$cell.type)load("G:/silicosis/sicosis/yll/macrophage/no cluster2/macrophage_clean.rds")
table(subset_data$orig.ident)
getwd()

#macrophage_clean 分群情况

DimPlot(subset_data,label = TRUE)
FeaturePlot(subset_data,features = c('Fabp4','Hmox1','Ctsk','Fabp5','Chil3','S100a1','Wfdc21'))
DotPlot(subset_data,features = c('Fabp4','Hmox1','Ctsk','Chil3','S100a1','Wfdc21'))my015subsetdata=subset(subset_data,idents = c('0','1','4','5'))
subset_data=my015subsetdata
#重新分组 去除批次效应
if(1==1){#重新分组 去除批次效应subset_data[["percent.mt"]] <- PercentageFeatureSet(subset_data, pattern = "^mt-")VlnPlot(subset_data, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)subset_data = subset_data %>%Seurat::NormalizeData(verbose = FALSE) %>%  FindVariableFeatures(selection.method = "vst", nfeatures = 2000) %>%ScaleData(verbose = FALSE) %>%RunPCA(npcs = 50, verbose = FALSE)ElbowPlot(subset_data, ndims = 50)VlnPlot(subset_data, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)subset_data <- subset_data %>% RunHarmony("stim", plot_convergence = TRUE)harmony_embeddings <- Embeddings(subset_data, 'harmony') #######################clusterdims = 1:30subset_data <- subset_data %>% RunUMAP(reduction = "harmony", dims = dims) %>% RunTSNE(reduction = "harmony", dims = dims) %>% FindNeighbors(reduction = "harmony", dims = dims)
}getwd()
DimPlot(subset_data,label = TRUE)
table(Idents(subset_data))#R中seurat包DimPlot如何根据不同的resolution画图 seurat查看某一分辨率下的dimplot
#树状图
library(clustree)
for (res in seq(0,0.8,0.1)) {subset_data=FindClusters(subset_data,graph.name = "RNA_snn",resolution = res,algorithm = 1)
}
subset_data=FindClusters(subset_data,resolution = 0.25)
apply(subset_data@meta.data[,grep("RNA_snn_res",colnames(subset_data@meta.data))],2,table)mytree=clustree(subset_data@meta.data,prefix="RNA_snn_res.")
mytreewhile (dev.off()) {dev.off()
}
DimPlot(subset_data,label = TRUE)
DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.5")
DotPlot(subset_data,group.by="RNA_snn_res.0.5",features = c('Car4','Ctsk','Chil3','S100a1','Wfdc21','Fabp5','Fabp4','Hmox1'))
DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.5")
DimPlot(subset_data,label = TRUE,split.by = 'stim',group.by="RNA_snn_res.0.4")subset_data=subset(subset_data,idents = c('0','1','2','3','4','5','7')) #去掉线粒体含量高的c6
#重新分组 去除批次效应
if(1==1){subset_data = subset_data %>%Seurat::NormalizeData(verbose = FALSE) %>%  FindVariableFeatures(selection.method = "vst", nfeatures = 2000) %>%ScaleData(verbose = FALSE) %>%RunPCA(npcs = 50, verbose = FALSE)
ElbowPlot(subset_data, ndims = 50)
VlnPlot(subset_data, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
subset_data <- subset_data %>% RunHarmony("stim", plot_convergence = TRUE)
harmony_embeddings <- Embeddings(subset_data, 'harmony')
#######################cluster
dims = 1:30
subset_data <- subset_data %>% RunUMAP(reduction = "harmony", dims = dims) %>% RunTSNE(reduction = "harmony", dims = dims) %>% FindNeighbors(reduction = "harmony", dims = dims)}subset_data=FindClusters(subset_data)
DimPlot(subset_data,label = TRUE)
DimPlot(subset_data,label = TRUE,split.by = 'stim')#0.5分辨率下 合并04   合并23
if(1==1){table(Idents(subset_data))Idents(subset_data)=subset_data@meta.data$RNA_snn_res.0.5table(Idents(subset_data))subset_data=RenameIdents(subset_data,'4'='0','3'='2')table(Idents(subset_data))AM_markers012=FindAllMarkers(subset_data,min.pct = 0.1,logfc.threshold = 0.5,only.pos = TRUE)getwd()write.xlsx(AM_markers012,file = "G:/silicosis/sicosis/yll/macrophage/no cluster2/0.3/pure_cluster013_in_allmerge/AM_Marker4-0=3-2.xlsx")
}#0.3分辨率下，合并3 0
if(1==1){table(Idents(subset_data))Idents(subset_data)=subset_data@meta.data$RNA_snn_res.0.3table(Idents(subset_data))DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.3")subset_data=RenameIdents(subset_data,'3'='0') #合并三群和0群 即 把3变为0table(Idents(subset_data))}#0.3分辨率下，合并2 0
if(1==1){table(Idents(subset_data))Idents(subset_data)=subset_data@meta.data$RNA_snn_res.0.3table(Idents(subset_data))DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.3")DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.5")DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.4")subset_data=RenameIdents(subset_data,'2'='0') #合并2群和0群 即 把2变为0  把table(Idents(subset_data))subset_data=RenameIdents(subset_data,'3'='2')DimPlot(subset_data,label = TRUE)DimPlot(subset_data,label = TRUE,split.by='stim')subset_data=RenameIdents(subset_data,'0'='AM1','2'='AM2','1'='AM3')#比例图ggplot(subset_data@meta.data, aes(x=Idents(subset_data), fill=stim)) + geom_bar(position = 'fill')#save(subset_data,file ="G:/silicosis/sicosis/yll/macrophage/no cluster2/0.3/pure_cluster013_in_allmerge/macrophage0.3combine2-0.rds" )
load(file = "G:/silicosis/sicosis/yll/macrophage/no cluster2/0.3/pure_cluster013_in_allmerge/macrophage0.3combine2-0.rds")
table(subset_data$orig.ident)
table(Idents(subset_data))}getwd()
#0.7分辨率下，合并3 2 0   4变为2
if(1==1){table(Idents(subset_data))Idents(subset_data)=subset_data@meta.data$RNA_snn_res.0.7table(Idents(subset_data))DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.3")DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.5")DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.4")DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.6")DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.7")subset_data=RenameIdents(subset_data,'2'='0','3'='0','4'='2') #合并2群和0群 即 把2变为0table(Idents(subset_data))
}#0.5分辨率下，合并3 2 0   4变为2
if(1==1){table(Idents(subset_data))Idents(subset_data)=subset_data@meta.data$RNA_snn_res.0.7table(Idents(subset_data))DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.2")DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.25")DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.3")DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.5")DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.4")DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.6")DimPlot(subset_data, reduction = "umap",label = TRUE,group.by="RNA_snn_res.0.7")subset_data=RenameIdents(subset_data,'2'='0','3'='0','4'='2') #合并2群和0群 即 把2变为0table(Idents(subset_data))
}
#save(subset_data,file="G:/silicosis/sicosis/yll/macrophage/no cluster2/0.3/pure_cluster013_in_allmerge/macrophage_0145_noMT.rds")
load("G:/silicosis/sicosis/yll/macrophage/no cluster2/0.3/pure_cluster013_in_allmerge/macrophage_0145_noMT.rds")#比例图
ggplot(subset_data@meta.data, aes(x=RNA_snn_res.0.3, fill=stim)) + geom_bar(position = 'fill')ggplot(subset_data@meta.data, aes(x=Idents(subset_data), fill=stim)) + geom_bar(position = 'fill')FeaturePlot(subset_data,features = c('C1qa','C1qb','C1qc'))#查看markers
#Idents(subset_data)=subset_data@meta.data$RNA_snn_res.0.3
DimPlot(subset_data,label = TRUE)
AM_markers012=FindAllMarkers(subset_data,min.pct = 0.1,logfc.threshold = 0.5,only.pos = TRUE)
getwd()
write.xlsx(AM_markers012,file = "G:/silicosis/sicosis/yll/macrophage/no cluster2/0.3/pure_cluster013_in_allmerge/AM_Markero.3r_combine2-0.xlsx")

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从巨噬细胞macrophage_clean先去除线粒体再提取AM1 AM2 AM3

从巨噬细胞macrophage_clean提取AM1 AM2 AM3

从巨噬细胞macrophage_clean先去除线粒体再提取AM1 AM2 AM3相关推荐

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从巨噬细胞macrophage_clean先去 除线粒体再提取AM1 AM2 AM3

从巨噬细胞macrophage_clean提取AM1 AM2 AM3

从巨噬细胞macrophage_clean先去 除线粒体再提取AM1 AM2 AM3相关推荐

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从巨噬细胞macrophage_clean先去除线粒体再提取AM1 AM2 AM3

从巨噬细胞macrophage_clean先去除线粒体再提取AM1 AM2 AM3相关推荐