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Sup_scRNA_Preprocessing.R
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Sup_scRNA_Preprocessing.R
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##### Presetting ######
rm(list = ls()) # Clean variable
memory.limit(150000)
##### Load Packages #####
if(!require("tidyverse")) install.packages("tidyverse")
if(!require("Seurat")) install.packages("Seurat")
if(!require("SeuratData")) install.packages("SeuratData")
if(!require("patchwork")) install.packages("patchwork")
library(tidyverse)
library(Seurat)
library(SeuratData)
library(patchwork)
##### Function setting #####
## Call function
source("FUN_Beautify_ggplot.R")
# source("FUN_Find_Markers.R")
# source("FUN_VolcanoPlot.R")
# source("FUN_GSEA_LargeGeneSet.R")
# source("FUN_GSEA_ggplot.R")
##### Current path and new folder setting* #####
ProjectName = "ifnb"
Sampletype = "PBMC"
#ProjSamp.Path = paste0(Sampletype,"_",ProjectName)
Version = paste0(Sys.Date(),"_",ProjectName,"_",Sampletype)
Save.Path = paste0(getwd(),"/",Version)
## Create new folder
if (!dir.exists(Save.Path)){ dir.create(Save.Path)}
## Import information
InputFolder = "Input_files_10x"
InputAnno = "PBMC_Ano.csv"
InputGSEA = "GSEA_Geneset_Pathway_3Database_WithoutFilter.txt"
##### Load dataset* #####
# install dataset
InstallData("ifnb")
# load dataset
LoadData("ifnb")
# split the dataset into a list of two seurat objects (stim and CTRL)
ifnb.list <- SplitObject(ifnb, split.by = "stim")
# normalize and identify variable features for each dataset independently
ifnb.list <- lapply(X = ifnb.list, FUN = function(x) {
x <- NormalizeData(x)
x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
# select features that are repeatedly variable across datasets for integration
features <- SelectIntegrationFeatures(object.list = ifnb.list)
##### Perform integration #####
immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features)
# this command creates an 'integrated' data assay
immune.combined <- IntegrateData(anchorset = immune.anchors)
##### Perform an integrated analysis #####
# specify that we will perform downstream analysis on the corrected data note that the
# original unmodified data still resides in the 'RNA' assay
DefaultAssay(immune.combined) <- "integrated"
# Run the standard workflow for visualization and clustering
immune.combined <- ScaleData(immune.combined, verbose = FALSE)
immune.combined <- RunPCA(immune.combined, npcs = 30, verbose = FALSE)
immune.combined <- RunUMAP(immune.combined, reduction = "pca", dims = 1:30)
immune.combined <- FindNeighbors(immune.combined, reduction = "pca", dims = 1:30)
immune.combined <- FindClusters(immune.combined, resolution = 0.5)
# Visualization
p1 <- DimPlot(immune.combined, reduction = "umap", group.by = "stim")
p2 <- DimPlot(immune.combined, reduction = "umap", label = TRUE, repel = TRUE)
p1 + p2
p1 %>% BeautifyggPlot(.,LegPos = c(1, 0.5))
DimPlot(immune.combined, reduction = "umap", split.by = "stim")
##### Identify conserved cell type markers #####
# For performing differential expression after integration, we switch back to the original
# data
DefaultAssay(immune.combined) <- "RNA"
nk.markers <- FindConservedMarkers(immune.combined, ident.1 = 6, grouping.var = "stim", verbose = FALSE)
head(nk.markers)
FeaturePlot(immune.combined, features = c("CD3D", "SELL", "CREM", "CD8A", "GNLY", "CD79A", "FCGR3A",
"CCL2", "PPBP"), min.cutoff = "q9")
immune.combined <- RenameIdents(immune.combined, `0` = "CD14_Mono", `1` = "CD4_Naive_T", `2` = "CD4_Memory_T",
`3` = "CD16_Mono", `4` = "B", `5` = "CD8_T", `6` = "NK", `7` = "T_activated", `8` = "DC", `9` = "B_Activated",
`10` = "Mk", `11` = "pDC", `12` = "Eryth", `13` = "Mono_Mk_Doublets", `14` = "HSPC")
immune.combined$celltype <- Idents(immune.combined)
DimPlot(immune.combined, label = TRUE) %>% BeautifyggPlot(.,LegPos = c(1, 0.5))
DimPlot(immune.combined, label = TRUE,group.by = "celltype") %>% BeautifyggPlot(.,LegPos = c(1, 0.5))
Idents(immune.combined) <- factor(Idents(immune.combined), levels = c("HSPC", "Mono_Mk_Doublets",
"pDC", "Eryth", "Mk", "DC", "CD14_Mono", "CD16_Mono", "B_Activated", "B", "CD8_T", "NK", "T_activated",
"CD4_Naive_T", "CD4_Memory_T"))
markers.to.plot <- c("CD3D", "CREM", "HSPH1", "SELL", "GIMAP5", "CACYBP", "GNLY", "NKG7", "CCL5",
"CD8A", "MS4A1", "CD79A", "MIR155HG", "NME1", "FCGR3A", "VMO1", "CCL2", "S100A9", "HLA-DQA1",
"GPR183", "PPBP", "GNG11", "HBA2", "HBB", "TSPAN13", "IL3RA", "IGJ", "PRSS57")
DotPlot(immune.combined, features = markers.to.plot, cols = c("blue", "red"), dot.scale = 8, split.by = "stim") +
RotatedAxis()
##---------------------------------------------------------------------------------------------------------------##
# ##### Find Marker in different Cell type and VolcanoPlot (SPA) ########
# ### Define group by different phenotype ###
# source("FUN_Find_Markers.R")
# immune.combined$celltype.Stim <- paste(Idents(immune.combined), immune.combined$stim, sep = "_")
# Idents(immune.combined) <- "celltype.Stim"
#
# DefaultAssay(immune.combined) <- "RNA"
#
#
# Idents(immune.combined) <- "celltype.Stim"
# #CellType.list <- as.character(unique(immune.combined@meta.data[["celltype"]]))
# dir.create(paste0(Save.Path,"/PBMC_SPA_FindMarkers"))
#
# CellType.list <- as.character(unique(immune.combined@meta.data[["celltype"]]))
# CCMarker_SPA.lt <- list()
# for(i in c(1:length(CellType.list))){
# try({
# CCMarker_SPA.lt[[i]] <- Find_Markers(immune.combined,
# paste0(CellType.list[i],"_STIM"),
# paste0(CellType.list[i],"_CTRL"),
# CellType.list[i],
# Path = Save.Path,
# ResultFolder = "PBMC_SPA_FindMarkers")
#
# # names(CCMarker_SPA.lt)[[i]] <- paste0("CCMarker_SPA.lt.",CellType.list[i])
# names(CCMarker_SPA.lt)[[i]] <- paste0(CellType.list[i])
# })
# }
# rm(i)
#
# CCMarker_SPA.lt <- CCMarker_SPA.lt[!unlist(lapply(CCMarker_SPA.lt,is.null))]
#
#
# ## Generate pdf and tif file for VolcanoPlot
# dir.create(paste0(Save.Path,"/PBMC_SPA_VolcanoPlot/"))
#
# pdf(file = paste0(Save.Path,"/PBMC_SPA_VolcanoPlot/PBMC_SPA_VolcanoPlot.pdf"),width = 7, height = 7 )
# for (i in 1:length(CellType.list)) {
# try({
# print(VolcanoPlot(CCMarker_SPA.lt[[i]][["TarMarker.S"]],
# CCMarker_SPA.lt[[i]][["TarMarker.S_Pos_List"]],
# CCMarker_SPA.lt[[i]][["TarMarker.S_Neg_List"]], ShowGeneNum = 6)+
# ggtitle(paste0("PBMC_",CellType.list[i]))
# )
# })
# }
# # graphics.off()
# dev.off()
# rm(i)
#
# for (i in 1:length(CellType.list)) {
# try({
# tiff(file = paste0(Save.Path,"/PBMC_SPA_VolcanoPlot/PBMC_SPA_VolcanoPlot",CellType.list[i],".tif"), width = 17, height = 17, units = "cm", res = 200)
# print(VolcanoPlot(CCMarker_SPA.lt[[i]][["TarMarker.S"]],
# CCMarker_SPA.lt[[i]][["TarMarker.S_Pos_List"]],
# CCMarker_SPA.lt[[i]][["TarMarker.S_Neg_List"]])+ ggtitle(paste0("PBMC_",CellType.list[i]))
# )
#
# graphics.off()
# })
# }
# rm(i)
#
#
#
#
# ##### 09_1 GSEA Analysis (SPA) #####
#
# ## Load the GSEA Dataset
# load("GSEA_Analysis_Geneset.RData")
# InputGSEA = "GSEA_Geneset_Pathway_3Database_WithoutFilter.txt"
#
#
# ## Geneset from GSEA
# # Pathway.all <- read.delim(paste0(getwd(),"/Pathway.all.v7.4.symbols.gmt"),header = F)
# Pathway.all <- read.delim2(paste0(getwd(),"/",InputGSEA),
# col.names = 1:max(count.fields(paste0(getwd(),"/",InputGSEA))),
# header = F,sep = "\t")
#
#
# ## Run GSEA
# GSEA_Large <- list()
# GSEA_Large.df <- as.data.frame(matrix(nrow=0,ncol=10))
# colnames(GSEA_Large.df) <- c("GeneType","PhenoType","pathway","pval","padj","log2err","ES", "NES" ,"size","leadingEdge")
# GSEA_Large.df.TOP <- GSEA_Large.df
#
# dir.create(paste0(Save.Path,"/PBMC_GSEA"))
#
#
# pdf(file = paste0(Save.Path, "/PBMC_GSEA/PBMC_GSEA_SPA.pdf"),width = 15, height = 7 )
#
# for(i in 1:length(CellType.list)){
#
# gseaDat <- CCMarker_SPA.lt[[paste0(CellType.list[i])]][["TarMarker.All"]]
# gseaDat <- data.frame(row.names(gseaDat),gseaDat)
# colnames(gseaDat)[[1]] <- c("Gene")
# ranks <- gseaDat$avg_log2FC
# names(ranks) <- gseaDat$Gene
# # head(ranks)
# # barplot(sort(ranks, decreasing = T))
#
# GSEA_Large.Output <- FUN_GSEA_LargeGeneSet(ranks,Pathway.all,10)
#
# fgseaRes <- GSEA_Large.Output[["fgseaRes"]]
# # head(fgseaRes[order(padj, -abs(NES)), ], n=10)
#
# pathwaysH <- GSEA_Large.Output[["Pathway.all.list"]]
#
# # plot.new()
# # plotEnrichment(pathwaysH[[as.character(fgseaRes$pathway[1])]], ranks)
#
# topPathways <- GSEA_Large.Output[["topPathways"]]
#
# library(ggplot2)
# plot.new()
# plotGseaTable(pathwaysH[topPathways$pathway],
# ranks,
# fgseaRes,
# gseaParam = 0.5) + title( paste0("SC.",CellType.list[i]), adj = 0, line =3)
#
# plotEnrichment_Pos1 <- plotEnrichment(pathwaysH[[as.character(topPathways[1,1])]], ranks)+ labs(title= paste0("SC.",CellType.list[i],": ",as.character(topPathways[1,1])))
# #plotEnrichment_Pos1
# plotEnrichment_Neg1 <- plotEnrichment(pathwaysH[[as.character(topPathways[length(as.data.frame(topPathways)[,1]),1])]], ranks)+ labs(title= paste0("SC.",CellType.list[i],": ",as.character(topPathways[length(as.data.frame(topPathways)[,1]),1])))
# #plotEnrichment_Neg1
#
# Sum <- list(gseaDat,ranks,pathwaysH,fgseaRes,plotEnrichment_Pos1,plotEnrichment_Neg1)
# names(Sum) <- c("gseaDat","ranks","pathwaysH","fgseaRes","plotEnrichment_Pos1","plotEnrichment_Neg1")
# GSEA_Large[[i]] <- Sum
# names(GSEA_Large)[[i]] <- paste0(CellType.list[i])
#
# fgseaRes2 <- data.frame(paste0(CellType.list[i]),fgseaRes)
# colnames(fgseaRes2)[[1]] <- c("PhenoType")
# GSEA_Large.df <- rbind(GSEA_Large.df,fgseaRes2 )
#
# topPathways2 <- data.frame(paste0(CellType.list[i]),topPathways)
# colnames(topPathways2)[[1]] <- c("PhenoType")
# GSEA_Large.df.TOP <- rbind(GSEA_Large.df.TOP, topPathways2)
#
# rm(gseaDat,ranks,pathwaysH,fgseaRes,fgseaRes2,plotEnrichmen,Sum,topPathways,topPathways2)
#
# }
#
# dev.off()
#
# ## GSEA_Large.Sum.TOP ##
# GSEA_Large.Sum.TOP <- rbind(GSEA_Large.df.TOP)
# GSEA_Large.Sum.TOP <- GSEA_Large.Sum.TOP[,!colnames(GSEA_Large.Sum.TOP) %in% c("leadingEdge")]
# write.table(GSEA_Large.Sum.TOP, file=paste0(Save.Path,"/PBMC_GSEA/PBMC_GSEA_Pathway_LargeTOP_SPA.txt"),sep="\t",
# row.names=F, quote = FALSE)
#
# ##### Bubble plot #####
# library(ggplot2)
# library(scales)
# GSEA_Color.lt = list(high = "#ef476f",mid = "white",low = "#0077b6")
#
# GSEA_Large.Sum.TOP$PhenoType <- factor(GSEA_Large.Sum.TOP$PhenoType,
# levels = Cell_Type_Order.set)
#
# GSEA_ggplot_SPA.lt <- GSEA_ggplot(GSEA_Large.Sum.TOP,NES_Th = 1.5, padj_Th = 0.01)
# GSEA_Large.Sum.TOP.S <- GSEA_ggplot_SPA.lt[["GSEA_TOP.df"]]
# # GSEA_Large.Sum.TOP.S <- GSEA_Large.Sum.TOP[abs(GSEA_Large.Sum.TOP$NES) > 1,]
# # GSEA_Large.Sum.TOP.S <- GSEA_Large.Sum.TOP.S[abs(GSEA_Large.Sum.TOP.S$padj) < 0.05,]
# # GSEA_Large.Sum.TOP.S <- GSEA_Large.Sum.TOP[abs(GSEA_Large.Sum.TOP$padj) < 0.25,]
# # GSEA_Large.Sum.TOP.S <- GSEA_Large.Sum.TOP.S[abs(GSEA_Large.Sum.TOP.S$pval) < 0.05,]
#
# pdf(file = paste0(Save.Path,"/PBMC_GSEA/PBMC_GSEA_Bubble_SPA.pdf"),width = 17, height = 12 )
# GSEA_ggplot_SPA.lt[["BBPlot_Ori"]]
# GSEA_ggplot_SPA.lt[["BBPlot"]]
# GSEA_ggplot_SPA.lt[["BBPlot2"]]
# GSEA_ggplot_SPA.lt[["BBPlotB1"]]
# GSEA_ggplot_SPA.lt[["BBPlotB1"]]
# dev.off()
#
#
# ##### Extract SubType #####
# ## Mac
# GSEA_Mac.df <- GSEA_Large.Sum.TOP.S[grep("Mac",GSEA_Large.Sum.TOP.S$PhenoType),]
#
# BBPlot_Mac <- ggplot(GSEA_Mac.df,aes(x=PhenoType, y = pathway, color = NES, size = -log10(padj))) +
# geom_point() +
# scale_size_area(max_size = 5)+
# scale_colour_gradient2(low = GSEA_Color.lt[["low"]], mid = GSEA_Color.lt[["mid"]], high = GSEA_Color.lt[["high"]],
# guide = "colourbar",midpoint = 0)+ theme(legend.position = "bottom")+ theme_bw()+
# theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())
#
# BBPlot_Mac
#
# BBPlot_MacB <- BBPlot_Mac %>% BeautifyggPlot(LegPos = "bottom",LegBox = "horizontal",LegDir="horizontal", xangle =90,OL_Thick = 1.5,
# XtextSize=15, YtextSize=10, AxisTitleSize=1, AspRat=4, XaThick=0.8, YaThick=0.8)
#
# BBPlot_MacB1 <- BBPlot_MacB %>%
# insert_left(GSEA_ggplot_SPA.lt[["Y_Order"]],width = 0.2)
# BBPlot_MacB1
#
# pdf(file = paste0(Save.Path,"/PBMC_GSEA/PBMC_GSEA_Bubble_SPA_SubType_Mac.pdf"),width = 17, height = 20 )
# BBPlot_MacB
# BBPlot_MacB1
# dev.off()
#
#
# rm(p2,p3,BBPlotB1,BBPlotB2,BBPlotB,BBPlot_Cluster,df1.1.clust.Pheno,df1.1.clust.Pathway,
# df1.1,df1,BBPlot,BBPlot_Fib,BBPlot_FibB,BBPlot_T,BBPlot_TB)
#### Save RData ####
save.image(paste0(Save.Path,"/GseaGo_",ProjectName,"_",Sampletype,".RData"))