getwd()
setwd("G:\\lung fibrosis")
##########mouse stable state--------------
mouse_ssfibro=readRDS("./Mouse_SS_Fibro.RDS")
library(Seurat)
head(mouse_ssfibro@meta.data)
DimPlot(mouse_ssfibro,label = TRUE,raster=FALSE)
table(mouse_ssfibro$Tissue)
dim(mouse_ssfibro)
Idents(mouse_ssfibro)=mouse_ssfibro$Tissue
lung_ss=subset(mouse_ssfibro,idents = "Lung")
DimPlot(lung_ss,label = TRUE)
str(lung_ss)
subset_data=lung_ss
subset_data[["percent.mt"]] <- PercentageFeatureSet(subset_data, pattern = "^mt-")
grep(pattern = "mt",x = rownames(subset_data),value = TRUE)
VlnPlot(subset_data, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
library(dplyr)
subset_data = subset_data %>%
Seurat::NormalizeData(verbose = FALSE) %>%
FindVariableFeatures(selection.method = "vst", nfeatures = 2000) %>%
ScaleData(verbose = FALSE) %>%
RunPCA(npcs = 50, verbose = FALSE)
dims = 1:30
subset_data <- subset_data %>%
RunUMAP(reduction = "pca", dims = dims) %>%
RunTSNE(reduction = "pca", dims = dims) %>%
FindNeighbors(reduction = "pca", dims = dims)
#subset_data@meta.data$stim <- c(rep("Exp", length(grep("1$",colnames(subset_data)))),rep("Con", length(grep("2$",colnames(subset_data)))))
table(subset_data)
DimPlot(subset_data,label = TRUE,label.size = 7)+
ggtitle("stable_state_mouse_lung")
DotPlot(subset_data,features = c("Inmt","Cd34","Sepp1","Ly6a",
"Gpx3","Selenop"))
library(stringr)
library(ggplot2)
DotPlot(subset_data,features = str_to_title(c("GPX1","GPX3",
"Dio1","Gpx4","Rps14","Selh","Selk","Selm","Selo","Sels","Selt",
"Sep15","Sepp1","Sepw1","Sepx1","Txnrd1",
"Txnrd2","Txnrd3")))+
RotatedAxis()+ggtitle("stable_state_mouse_lung")
##selected stable lung--------
dim(subset_data)
################mouse ps fibro----------
mouse_ps_fibro=readRDS("./Mouse_PS_Fibro.RDS")
head(mouse_ps_fibro@meta.data)
table(mouse_ps_fibro$Tissue)
Idents(mouse_ps_fibro)=mouse_ps_fibro$Tissue
lung_fibro=subset(mouse_ps_fibro,idents = c('Lung Bleo_Fibrosis D14',
"Lung Fibrosis D21", #"Lung Nin_Fibrosis D11",
"Lung Bleo_Fibrosis D11"))
DimPlot(lung_fibro,label = TRUE)
mouse_selected_lung_ps=lung_fibro
{
mouse_selected_lung_ps[["percent.mt"]] <- PercentageFeatureSet(mouse_selected_lung_ps, pattern = "^mt-")
VlnPlot(mouse_selected_lung_ps, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
mouse_selected_lung_ps = mouse_selected_lung_ps %>%
Seurat::NormalizeData(verbose = FALSE) %>%
FindVariableFeatures(selection.method = "vst", nfeatures = 2000) %>%
ScaleData(verbose = FALSE) %>%
RunPCA(npcs = 50, verbose = FALSE)
table(Idents(mouse_selected_lung_ps))
mouse_selected_lung_ps$stim=Idents(mouse_selected_lung_ps)
#mouse_selected_lung_ps@meta.data$stim <- c(rep("Exp", length(grep("1$",colnames(mouse_selected_lung_ps)))),rep("Con", length(grep("2$",colnames(mouse_selected_lung_ps)))))
table(mouse_selected_lung_ps$stim)
##########################run harmony
#BiocManager::install('harmony')
library('harmony')
mouse_selected_lung_ps <- mouse_selected_lung_ps %>% RunHarmony("stim", plot_convergence = TRUE)
harmony_embeddings <- Embeddings(mouse_selected_lung_ps, 'harmony')
#######################cluster
dims = 1:30
mouse_selected_lung_ps <- mouse_selected_lung_ps %>%
RunUMAP(reduction = "harmony", dims = dims) %>%
RunTSNE(reduction = "harmony", dims = dims) %>%
FindNeighbors(reduction = "harmony", dims = dims)%>%
FindClusters()
DimPlot(mouse_selected_lung_ps,label = TRUE,label.size = 7)+
ggtitle("mouse_selected_lung_ps")
}
DotPlot(mouse_selected_lung_ps,features = str_to_title(c("GPX1","GPX3",
"Dio1","Gpx4","Rps14","Selh","Selk","Selm","Selo","Sels","Selt",
"Sep15","Sepp1","Sepw1","Sepx1","Txnrd1",
"Txnrd2","Txnrd3")))+
RotatedAxis()+ggtitle("mouse_selected_lung_ps")
DotPlot(mouse_selected_lung_ps,features = c("Inmt","Cd34","Sepp1","Ly6a",
"Gpx3","Selenop","Selp"))
dim(mouse_selected_lung_ps)
#######merge stable and perturbed- stable and fibrosi-------
subset_data$stim="stable"
All.merge=merge(subset_data,mouse_selected_lung_ps)
head(All.merge@meta.data)
table(All.merge$stim)
table(is.na(All.merge$stim))
All.merge$stim=case_when(is.na(All.merge$stim)~"stable",
.default = All.merge$stim)
table(All.merge$stim)
{
All.merge[["percent.mt"]] <- PercentageFeatureSet(All.merge, pattern = "^mt-")
VlnPlot(All.merge, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
All.merge = All.merge %>%
Seurat::NormalizeData(verbose = FALSE) %>%
FindVariableFeatures(selection.method = "vst", nfeatures = 2000) %>%
ScaleData(verbose = FALSE) %>%
RunPCA(npcs = 50, verbose = FALSE)
#All.merge@meta.data$stim <- c(rep("Exp", length(grep("1$",colnames(All.merge)))),rep("Con", length(grep("2$",colnames(All.merge)))))
table(All.merge$stim)
library('harmony')
All.merge <- All.merge %>% RunHarmony("stim", plot_convergence = TRUE)
harmony_embeddings <- Embeddings(All.merge, 'harmony')
#######################cluster
dims = 1:30
All.merge <- All.merge %>%
RunUMAP(reduction = "harmony", dims = dims) %>%
RunTSNE(reduction = "harmony", dims = dims) %>%
FindNeighbors(reduction = "harmony", dims = dims)
All.merge=FindClusters(All.merge)
DimPlot(All.merge,label = TRUE,label.size = 7)
}
All.merge=All.merg
library(stringr)
DotPlot(All.merge,features = str_to_title(c("GPX1","GPX3",
"Dio1","Gpx4","Rps14","Selh","Selk","Selm","Selo","Sels","Selt",
"Sep15","Sepp1","Sepw1","Sepx1","Txnrd1",
"Txnrd2","Txnrd3")))+
RotatedAxis()+ggtitle("allmerge_stable and fibrosis")
DotPlot(All.merge,features = c("Inmt","Cd34","Sepp1","Ly6a",
"Gpx3","Selenop","Selp"))
DimPlot(All.merge,label = TRUE,
split.by = "stim")
table(All.merge$stim)
All.merge$group =ifelse(All.merge$stim=="stable","Stable","Fibrosis")
FeaturePlot(All.merge,features = c("Inmt","Cd34","Sepp1","Ly6a",
"Gpx3","Selenop"),split.by = "group")
grep(pattern = "sele|sep|sel|inmt|selen",rownames(All.merge),ignore.case = TRUE,
value = TRUE)
All.merg=RenameIdents(All.merge,"2"="universal fib",
"5"="universal fib",
"13"="universal fib",
"0"="specialized fib",
"3"="specialized fib")
ggplot(All.merg@meta.data,
aes(x=Idents(All.merg),fill=Idents(All.merg))) + geom_bar(position = "fill")
#+scale_fill_manual(values = c("royalblue", "red"))#scale_fill_gradientn(colors = colorRampPalette(c("royalblue", "firebrick3"))(12))#colorRampPalette(c("royalblue","firebrick3"))(2)
ggplot(All.merg@meta.data,
aes(x=Idents(All.merg), fill=group)) + geom_bar(position = "fill")+
RotatedAxis()
table(All.merg$group,Idents(All.merg)) #%>%summarise()
table(All.merg$group)
getwd()
save(All.merg,file = "All.merg.rds")
dir.create("G:/lung fibrosis/silicosis_cross_tissue_fibroblasts")
setwd("G:/lung fibrosis/silicosis_cross_tissue_fibroblasts")
load("G:/lung fibrosis/All.merg.rds")
library(ggplot2)
library(Seurat)
head(All.merg@meta.data)
table(All.merg$stim)
table(All.merg$group)
Idents(All.merg)=All.merg$group
degs_cross_tissue_mice_fibrosis_vs_stable=FindMarkers(All.merg,
ident.1 = "Fibrosis",
ident.2 = "Stable")
degs_cross_tissue_mice_fibrosis_vs_stable$gene=rownames(degs_cross_tissue_mice_fibrosis_vs_stable)
DT::datatable(degs_cross_tissue_mice_fibrosis_vs_stable)
openxlsx::write.xlsx(degs_cross_tissue_mice_fibrosis_vs_stable,
file ="degs_cross_tissue_mice_fibrosis_vs_stable.xlsx" )
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