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Smith_et_al_processing_with_plots.R
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Smith_et_al_processing_with_plots.R
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#######
object <- qs::qread("sce.qs")
object<-object[,colData(object)$cluster_celltype=='Micro']
gc()
#######
library(Seurat)
object <- readRDS('object.RDS')
object<-SetIdent(object)
object<-subset(object, subset=new_celltype=='Microglia')
object<-as.SingleCellExperiment(object)
source('~/Basic_R_scRNAseq/make_pseudobulk.R')
object<-make_pseudobulk(object, pseudobulk_ID = 'Case.Region', pb_columns = c('Region','Braak','Disease','Sex','p_tau','Amyloid_beta','HLA'))
gc()
qs::qsave(object,'micro_pseudobulk.qs')
gc()
########
#########
data<-object$sumDat
metadata<-object$annot_pb
rownames(metadata)<-as.character(metadata$group_sample)
data<-as.data.frame(t(data))
data<-data[,!duplicated(colnames(data))]
data<-voom(data)$E
results<-merge(data,metadata,by='row.names')
results$diagnosis<-factor(results$Disease,levels = c('Control','AD'))
results<-results[results$Region=='EC',]
# results$diagnosis<-factor(results$diagnosis,levels = c('Control','MS'))
# results$diagnosis<-factor(results$diagnosis,levels = c('CNTRL','AD'))
#########
####### AUCell on micro
library(AUCell)
cells_rankings<-AUCell_buildRankings(object$sumDat)
Important_GeneSets <- readRDS("~/AD/Gene_sets/Important_GeneSets.RDS")
list<-list()
list[['DAM']]<-Important_GeneSets$DAM_logFC0.25_padj0.05
list[['Homeostatic']]<-Important_GeneSets$Rangaraju_micro$Homeostatic
list[['Homeostatic']]<-Important_GeneSets$Rangaraju_micro$Homeostatic
list[['Proinflammatory']]<-Important_GeneSets$Rangaraju_micro$Pro_inflammatory
list[['Core_microglia']]<-Important_GeneSets$Butovsky_micro
cells_AUC<-AUCell_calcAUC(list,cells_rankings)
saveRDS(cells_AUC,'AUCell_gene_sets_.RDS')
##### limma comparisons
library(limma)
sce<-readRDS('sce.RDS')
sce<-subset(sce, subset=new_celltype=='Microglia')
sce<-subset(sce,subset=Gliasubcluster!='9')
gc()
cells_AUC <- readRDS("AUCell_gene_sets.RDS")
# ### quantification using limma
#
#
#
library(limma)
cluster<-'Micro1'
a<-as.data.frame(as.character(sce$Gliasubcluster))
colnames(a)<-'Gliasubcluster'
a$clusters<-NA
a$clusters[a$Gliasubcluster==cluster]<-'test'
a$clusters[a$Gliasubcluster!=cluster]<-'control'
sce$clusters<-as.character(a$clusters)
diagnosis<-sce$Disease
diagnosis<-factor(diagnosis,levels = c('Control','AD'))
cluster<-sce$Gliasubcluster
cluster<-factor(cluster)
clusters<-factor(sce$clusters, levels = c('control','test'))
region<-factor(sce$Region, levels=c('SSC','EC'))
individual<-sce$Case.Region
mm <- model.matrix(~0+cluster)
dc <- duplicateCorrelation(log2(cells_AUC@assays@data@listData$AUC[,colnames(sce)]), design=mm, block=individual)
fit <- lmFit(log2(cells_AUC@assays@data@listData$AUC[,colnames(sce)])*10, design=mm, block=individual, correlation=dc$consensus)
#fit <- lmFit(log2(cells_AUC@assays@data@listData$AUC[,colnames(sce)]), mm)
contr <- makeContrasts(contrasts = 'clusterMicro6-clusterMicro1', levels = colnames(fit$coefficients))
tmp <- contrasts.fit(fit,contrasts = contr) #coefficients = 3)
tmp <- eBayes(tmp)
top.table <- topTable(tmp, sort.by = 'none', n = Inf)
write.table(top.table,'Micro6_vs_Micro1_gene_sets.txt',sep='\t',col.names = NA)
## vlnplots
library(ggplot2)
sce<-readRDS('sce.RDS')
sce<-subset(sce, subset=new_celltype=='Microglia')
sce<-subset(sce,subset=Gliasubcluster!='9')
[email protected]<-cbind([email protected],t(getAUC(cells_AUC)))
sce<-subset(sce,subset=Gliasubcluster %in% c('Micro1','Micro6'))
gc()
VlnPlot(object = sce, features = c("TREM2"), pt.size =0.0, group.by = 'Gliasubcluster')+ scale_x_discrete(labels=c('Homeostatic','Activated'))+NoLegend()+
labs(x='Microglia subset')#,y='Gene set enrichment')+ggtitle(label = 'Core microglial signature')
ggsave('TREM2_vln_Micro6_vs_homeost.tiff',height=5,width=3)
####### pseudobulk boxplots
#################
sce<-qs::qread('micro1_6_pseudobulk_cluster_sce.qs')
rownames(sce)<-as.character(rowData(sce)$external_gene_name)
source('~/Basic_R_scRNAseq/make_pseudobulk.R')
sce<-make_pseudobulk(sce, pseudobulk_ID = 'manifest', pb_columns = c('Region','Disease','Sex','Case.Region','Gliasubcluster'))
gc()
#### boxplots
library(ggpubr)
data<-voom(sce$sumDat)$E
metadata<-sce$annot_pb
rownames(metadata)<-as.character(metadata$group_sample)
metadata<-metadata[colnames(data),]
data<-t(data)
results<-merge(data,metadata,by='row.names')
ggboxplot(results, x = "Gliasubcluster",
y = 'C1QB',
combine = TRUE,
color = "Gliasubcluster", palette = c( '#FF0000','#00A08A'),
ylab = 'Gene expression (normalized counts)',
xlab='Microglia subset',
add = c("jitter"), # Add jittered points
add.params = list(size = 0.1, jitter = 0.2) )+theme(legend.position = 'right')+theme(plot.title = element_text(hjust = 0.5,face = 'bold'))+
scale_x_discrete(labels=c('Homeostatic','Activated'))+NoLegend()
ggsave('C1QB_Micro6_vs_homeost_boxplot.tiff',height=4,width=4)