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Keren-Shaul_et_al_processing_with_plots.R
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Keren-Shaul_et_al_processing_with_plots.R
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library(SingleCellExperiment)
library(Seurat)
integr_object<-qs::qread(file ='sce.qs')
integr_object<-as.Seurat(integr_object,data = NULL)
integr_object<-NormalizeData(integr_object)
####### comparison between DAM cells and other microglial cells
load("dataset.rda")
DefaultAssay(seu.integrated)<-'RNA'
DimPlot(seu.integrated,reduction = 'tsne',split.by = 'genotype')
library(ggplot2)
pdf('feature_dim_plots.pdf')
FeaturePlot(seu.integrated, features = c('ENSMUSG00000030789'),reduction = 'tsne')+
labs(title='Itgax')
FeaturePlot(seu.integrated, features = c('ENSMUSG00000002985'),reduction = 'tsne')+
labs(title='Apoe')
FeaturePlot(seu.integrated, features = c('ENSMUSG00000007891'),reduction = 'tsne')+
labs(title='Ctsd')
FeaturePlot(seu.integrated, features = c('ENSMUSG00000015568'),reduction = 'tsne')+
labs(title='Lpl')
FeaturePlot(seu.integrated, features = c('ENSMUSG00000030579'),reduction = 'tsne')+
labs(title='Tyrobp')
FeaturePlot(seu.integrated, features = c('ENSMUSG00000068129'),reduction = 'tsne')+
labs(title='Cst7')
FeaturePlot(seu.integrated, features = c('ENSMUSG00000023992'),reduction = 'tsne')+
labs(title='Trem2')
FeaturePlot(seu.integrated, features = c('ENSMUSG00000079293'),reduction = 'tsne')+
labs(title='Clec7a')
FeaturePlot(seu.integrated, features = c('ENSMUSG00000036353'),reduction = 'tsne')+
labs(title='P2ry12')
FeaturePlot(seu.integrated, features = c('ENSMUSG00000024610'),reduction = 'tsne')+
labs(title='Cd74')
FeaturePlot(seu.integrated, features = c('ENSMUSG00000052336'),reduction = 'tsne')+
labs(title='Cx3cr1')
FeaturePlot(seu.integrated, features = c('ENSMUSG00000061311'),reduction = 'tsne')+
labs(title='Rag1')
FeaturePlot(seu.integrated, features = c('ENSMUSG00000021665'),reduction = 'tsne')+
labs(title='Hexb')
FeaturePlot(seu.integrated, features = c('ENSMUSG00000008845'),reduction = 'tsne')+
labs(title='Cd163')
FeaturePlot(seu.integrated, features = c('ENSMUSG00000004612'),reduction = 'tsne')+
labs(title='Nkg7')
DimPlot(seu.integrated,reduction = 'tsne',split.by = 'genotype',label = T,group.by = 'cluster_original')
dev.off()
seu.integrated<-subset(seu.integrated, subset=genotype=='5XFAD')
seu.integrated<-SetIdent(seu.integrated,value = 'cluster_original')
a<-FindMarkers(seu.integrated,ident.1 = 3, ident.2 = c(0,1,2),test.use = 'MAST',logfc.threshold = 0.01,min.pct = 0.01)
write.table(a, 'DAM_vs_otherMicro.txt',sep='\t',col.names = NA)
# plot
library(ggplot2)
#tspo, p2ry12, apoe, trem2,tyrobp, Cst7
seu.integrated$DAM<-factor(seu.integrated$DAM,levels = c('non-DAM','DAM'))
VlnPlot(object = seu.integrated, features = c("ENSMUSG00000068129"), pt.size =0.0, group.by = 'DAM')+ scale_x_discrete(labels=c('non-DAM','DAM'))+NoLegend()+
labs(x='Microglia subset')+ggtitle(label = 'Cst7')
ggsave('Cst7_vln_DAM_vs_nonFAM.tiff',height=5,width=3)
####### boxplots
genes_of_interest<-as.data.frame(t(as.data.frame(seu.integrated@assays$RNA@data[c("ENSMUSG00000041736",'ENSMUSG00000036353','ENSMUSG00000002985',
'ENSMUSG00000023992','ENSMUSG00000030579',
'ENSMUSG00000068129'),])))
genes_of_interest<-as.data.frame(cbind(genes_of_interest,seu.integrated$cluster_original))
colnames(genes_of_interest)[7]<-c('subcluster')
cl<-as.data.frame(genes_of_interest$subcluster)
cl$subcluster<-'Other microglia'
cl$subcluster[cl$`genes_of_interest$subcluster`==3]<-'DAM'
genes_of_interest$subcluster<-cl$subcluster
data<-genes_of_interest
data$subcluster<-factor(data$subcluster,levels = c('DAM','Other microglia'))
# ggplot(data, aes(x=factor(data$group, levels = c('CTRL','LPS')), y=Tspo)) +
# geom_boxplot(width=0.1)+geom_dotplot(binaxis='y', stackdir='center',position=position_dodge(1), dotsize = .1)+
# theme_classic()+
# labs( x='Treatment', y='Normalized expression')+
# theme(axis.text.x = element_text(size = 15),axis.text.y = element_text(size = 15))+
# theme(axis.title = element_text(size = 20))
library(ggpubr)
gene_ens='ENSMUSG00000041736'
gene='Tspo'
#data<-genes_of_interest[genes_of_interest$Tspo>0,]
ggboxplot(data, x = "subcluster",
y = gene_ens,
combine = TRUE,
color = "subcluster", palette = "jco",
ylab = "Normalized counts",
add = "jitter", # Add jittered points
add.params = list(size = 0.1, jitter = 0.2) # Point size and the amount of jittering
)+theme(legend.position = 'none')+theme(plot.title = element_text(hjust = 0.5,face = 'bold'))+ggtitle(gene)+scale_x_discrete(labels=c('DAM','non-\nDAM'))
#theme(axis.text.x = element_text(size = 15),axis.text.y = element_text(size = 15))+
#theme(axis.title = element_text(size =15))
ggsave(paste(gene,'.tiff',sep = '_'),height=4,width=2)