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#_scRNAAutoR_Main_HNSC_Try.R
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#_scRNAAutoR_Main_HNSC_Try.R
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##### Presetting ######
rm(list = ls()) # Clean variable
memory.limit(150000)
##### Load Packages #####
library(Seurat)
library(SeuratData)
library(patchwork)
library(ggplot2)
library("stringr")
library("magrittr")
library("dplyr")
library(fgsea)
# In fuction
library("AnnotationHub")
library(ensembldb)
##### Function setting #####
## Call function
source("FUN_ReadscRNA.R")
source("FUN_Cal_Mit.R")
source("FUN_scRNAQC.R")
source("FUN_MetaSummary.R")
source("FUN_AnnoSummary.R")
source("FUN_Find_Markers.R")
source("FUN_VolcanoPlot.R")
source("FUN_Venn.R")
source("FUN_HSsymbol2MMsymbol.R")
source("FUN_Beautify_ggplot.R")
source("FUN_Beautify_UMAP.R")
source("FUN_GSEA_LargeGeneSet.R")
source("FUN_GSEA_ggplot.R")
##### Current path and new folder setting* #####
ProjectName = "LN"
Sampletype = "HNSC"
#ProjSamp.Path = paste0(Sampletype,"_",ProjectName)
Version = paste0(Sys.Date(),"_","LN_HNSC")
Save.Path = paste0(getwd(),"/",Version)
dir.create(Save.Path)
## Import information
InputFolder = "Input_files_GE"
InputAnno = "HNSC_Ano.csv"
InputGSEA = "GSEA_Geneset_Pathway_3Database_WithoutFilter.txt"
##### Parameter setting* #####
ClassSet1 = 1
ClassSet2 = "classified as cancer cell"
ClassSet3 = "Lymph node"
##### Load datasets #####
## Annotation table
list_files.df <- read.csv(paste0(InputFolder,"/",InputAnno))
Feature.set <- colnames(list_files.df)[-1]
scAnno.df <- data.frame()
Folder <- list_files.df$Folder[1]
scAnno.df <- read.table(paste0(InputFolder,"/", Folder, "/Anno.tsv"),
header=T, row.names = 1, sep="\t")
## Read 10x files
scRNA_SeuObj.list <- ReadscRNA(InputFolder, list_files.df, Mode="GE")
##### 01 Combine different datasets before QC #####
if(length(scRNA_SeuObj.list)==1){
scRNA.SeuObj <- scRNA_SeuObj.list[[1]]
}else{
# normalize and identify variable features for each dataset independently
set.seed(1) # Fix the seed
scRNA_SeuObj.list <- lapply(X = scRNA_SeuObj.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
set.seed(1) # Fix the seed
features <- SelectIntegrationFeatures(object.list = scRNA_SeuObj.list)
## Perform integration
set.seed(1) # Fix the seed
scRNA.anchors <- FindIntegrationAnchors(object.list = scRNA_SeuObj.list, anchor.features = features)
# this command creates an 'integrated' data assay
set.seed(1) # Fix the seed
scRNA.SeuObj <- IntegrateData(anchorset = scRNA.anchors)
set.seed(1) # Fix the seed
DefaultAssay(scRNA.SeuObj) <- "integrated"
}
save.image(paste0(Save.Path,"/01_Combine_different_datasets_before_QC.RData"))
##### 02 Quality Control #####
## Creative QC folder
dir.create(paste0(Save.Path,"/",ProjectName,"_QC"))
## QC for all samples
scRNA_Ori.SeuObj <- scRNA.SeuObj # Save the original obj
#Test# scRNA_Ori.SeuObj.list <- SplitObject(scRNA_Ori.SeuObj, split.by = "ID")
scRNA.SeuObj_QCTry <- scRNAQC(scRNA.SeuObj,FileName = paste0(Version,"/",ProjectName,"_QC/",ProjectName,"_QCTry"),NAno=1)
rm(scRNA.anchors,scRNA.SeuObj,scRNA.SeuObj_QCTry)
# specify that we will perform downstream analysis on the corrected data note that the
# original unmodified data still resides in the 'RNA' assay
## QC for each sample for the new integration
scRNA_SeuObj_QC.list <- list()
for (i in 1:length(scRNA_SeuObj.list)) {
Name <- names(scRNA_SeuObj.list)[[i]]
scRNA_SeuObj_QC.list[[i]] <- scRNAQC(scRNA_SeuObj.list[[i]],
FileName = paste0(Version,"/",ProjectName,"_QC/",ProjectName,"_", Name,"_QC"))
names(scRNA_SeuObj_QC.list)[[i]] <- Name
}
rm(i,Name)
save.image(paste0(Save.Path,"/02_Quality_Control.RData"))
##### 03 Combine different data sets after QC #####
if(length(scRNA_SeuObj_QC.list)==1){
scRNA.SeuObj <- scRNA_SeuObj_QC.list[[1]]
}else{
# normalize and identify variable features for each dataset independently
set.seed(1) # Fix the seed
scRNA_SeuObj_QC.list <- lapply(X = scRNA_SeuObj_QC.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
set.seed(1) # Fix the seed
features <- SelectIntegrationFeatures(object.list = scRNA_SeuObj_QC.list)
## Perform integration
set.seed(1) # Fix the seed
scRNA.anchors <- FindIntegrationAnchors(object.list = scRNA_SeuObj_QC.list, anchor.features = features)
# this command creates an 'integrated' data assay
set.seed(1) # Fix the seed
scRNA.SeuObj <- IntegrateData(anchorset = scRNA.anchors)
}
## Check QC
scRNAQC(scRNA.SeuObj,AddMitInf = "No",CheckOnly="Yes",FileName = paste0(Version,"/",ProjectName,"_QC/",ProjectName,"_QC_Check"))
save.image(paste0(Save.Path,"/03_Combine_different_data_sets_after_QC.RData"))
##### 04 Perform an integrated analysis #####
## Creative Clusters folder
dir.create(paste0(Save.Path,"/",ProjectName,"_Clusters"))
# Run the standard workflow for visualization and clustering
# # # !!
# set.seed(1) # Fix the seed
# all.genes <- rownames(scRNA.SeuObj)
# scRNA.SeuObj <- ScaleData(scRNA.SeuObj, features = all.genes)
## Issues: re_clustering in seurat v3
## https://github.com/satijalab/seurat/issues/1528
# DefaultAssay(scRNA.SeuObj) <- "RNA"
# specify that we will perform downstream analysis on the corrected data note that the
# original unmodified data still resides in the 'RNA' assay
DefaultAssay(scRNA.SeuObj) <- "integrated"
##?
set.seed(1) # Fix the seed
scRNA.SeuObj <- ScaleData(scRNA.SeuObj, verbose = FALSE)
AnnoNames.set <- colnames([email protected])
# ## Run if use filter
# set.seed(1) # Fix the seed
# scRNA.SeuObj <- FindVariableFeatures(scRNA.SeuObj)
### RunPCA
# set.seed(1) # Fix the seed
# scRNA.SeuObj <- RunPCA(scRNA.SeuObj, npcs = 30, verbose = FALSE)
set.seed(1) # Fix the seed
scRNA.SeuObj <- RunPCA(scRNA.SeuObj, features = VariableFeatures(object = scRNA.SeuObj))
print(scRNA.SeuObj[["pca"]], dims = 1:5, nfeatures = 5)
pdf(
file = paste0(Save.Path,"/",ProjectName,"_Clusters/",ProjectName,"_PCA.pdf"),
width = 10, height = 8
)
VizDimLoadings(scRNA.SeuObj, dims = 1:2, reduction = "pca")
DimPlot(scRNA.SeuObj, reduction = "pca")
DimHeatmap(scRNA.SeuObj, dims = 1, cells = 500, balanced = TRUE)
DimHeatmap(scRNA.SeuObj, dims = 1:15, cells = 500, balanced = TRUE)
DimHeatmap(scRNA.SeuObj, dims = 16:30, cells = 500, balanced = TRUE)
# # Determine the 'dimensionality' of the dataset
# # NOTE: This process can take a long time for big datasets, comment out for expediency. More
# # approximate techniques such as those implemented in ElbowPlot() can be used to reduce
# # computation time
# scRNA.SeuObj <- JackStraw(scRNA.SeuObj, num.replicate = 100)
# scRNA.SeuObj <- ScoreJackStraw(scRNA.SeuObj, dims = 1:20)
# JackStrawPlot(scRNA.SeuObj, dims = 1:20)
ElbowPlot(scRNA.SeuObj, ndims = 50)
dev.off()
ElbowPlot(scRNA.SeuObj, ndims = 50)
## Issues: RunUMAP causes R exit
## https://github.com/satijalab/seurat/issues/2259
# The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
# To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
# This message will be shown once per session
#### UMAP
set.seed(1) # Fix the seed
scRNA.SeuObj <- RunUMAP(scRNA.SeuObj, reduction = "pca", dims = 1:30)
set.seed(1) # Fix the seed
scRNA.SeuObj <- FindNeighbors(scRNA.SeuObj, reduction = "pca", dims = 1:30)
set.seed(1) # Fix the seed
scRNA.SeuObj <- FindClusters(scRNA.SeuObj, resolution = 0.5)
#### tSNE
set.seed(1) # Fix the seed
scRNA.SeuObj <- RunTSNE(scRNA.SeuObj, reduction = "pca", dims = 1:30)
set.seed(1) # Fix the seed
scRNA.SeuObj <- FindNeighbors(scRNA.SeuObj, reduction = "pca", dims = 1:30)
set.seed(1) # Fix the seed
scRNA.SeuObj <- FindClusters(scRNA.SeuObj, resolution = 0.5)
## Visualization
DimPlot(scRNA.SeuObj, reduction = "umap", group.by = colnames(list_files.df)[3] ) %>% BeautifyggPlot(.,LegPos = c(0.85, 0.15),AxisTitleSize=1.1)
pdf(
file = paste0(Save.Path,"/",ProjectName,"_Clusters/",ProjectName,"_nlDR_Cluster.pdf"),
width = 12, height = 8
)
DimPlot(scRNA.SeuObj, reduction = "umap", label = TRUE, label.size = 7, repel = TRUE) %>%
BeautifyggPlot(.,LegPos = c(1, 0.5),AxisTitleSize=1.2, LegTextSize = 14)
for (i in 1:(ncol(list_files.df)+ nrow(scAnno.df)-1)) {
print(DimPlot(scRNA.SeuObj, reduction = "umap", group.by = AnnoNames.set[i+3]) %>%
BeautifyggPlot(.,TV= -5,TitleSize = 25,LegPos = c(0.8, 0.15),AxisTitleSize=1.2, LegTextSize = 18)+
theme(plot.title = element_text(vjust = 0.85)))
print(DimPlot(scRNA.SeuObj, reduction = "umap", ncol = 2, split.by = AnnoNames.set[i+3], label = TRUE, label.size = 4) %>%
BeautifyggPlot(.,LegPos = c(1, 0.5),AxisTitleSize=1.2, TitleSize = 20,
SubTitSize = 17, LegTextSize = 14, XaThick=0.9, YaThick=0.9,OL_Thick = 1.5))
print(DimPlot(scRNA.SeuObj, reduction = "tsne", group.by = AnnoNames.set[i+3]) %>%
BeautifyggPlot(.,TV= -5,TitleSize = 25,LegPos = c(0.8, 0.15),AxisTitleSize=1.2, LegTextSize = 18)+
theme(plot.title = element_text(vjust = 0.85)))
print(DimPlot(scRNA.SeuObj, reduction = "tsne", ncol = 2, split.by = AnnoNames.set[i+3], label = TRUE, label.size = 4) %>%
BeautifyggPlot(.,LegPos = c(1, 0.5),AxisTitleSize=1.2, TitleSize = 20,
SubTitSize = 17, LegTextSize = 14, XaThick=0.9, YaThick=0.9,OL_Thick = 1.5))
}
rm(i)
dev.off()
# graphics.off()
save.image(paste0(Save.Path,"/04_Perform_an_integrated_analysis.RData"))
##### Meta Table #####
Meta.df <- MetaSummary(scRNA_SeuObj.list, scRNA.SeuObj,
scRNA_SeuObj_QC.list,scRNA_Ori.SeuObj,
SavePath = Version, projectName = paste0("/",ProjectName))
################## (Pending) Cell Cycle Regression ##################
################## (Pending) Auto Cell type annotation ##################
##### 05 Identify conserved cell type markers #####
## Creative Cell type folder
dir.create(paste0(Save.Path,"/",ProjectName,"_CT"))
## Identify conserved cell type markers
# find markers for every cluster compared to all remaining cells, report only the positive ones
set.seed(1) # Fix the seed
PBMC.markers <- FindAllMarkers(scRNA.SeuObj, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
library("magrittr")
library("dplyr")
# https://github.com/satijalab/seurat/issues/2960
# Filter the top markers and plot the heatmap
top_NSet = 7
PBMC.markers %>%
group_by(cluster) %>%
top_n(n = top_NSet, wt = avg_log2FC) -> top_N
scRNA.SeuObj <- ScaleData(scRNA.SeuObj, verbose = FALSE)
DoHeatmap(scRNA.SeuObj, features = top_N$gene) + NoLegend()
write.table(top_N, file=paste0(Save.Path,"/",ProjectName,"_Clusters/",ProjectName,"_ClusterMarker_top",top_NSet,"Gene.txt"),sep="\t", row.names=T
, quote = FALSE)
write.table(PBMC.markers, file=paste0(Save.Path,"/",ProjectName,"_Clusters/",ProjectName,"_ClusterMarker_AllGene.txt"),sep="\t", row.names=T
, quote = FALSE)
pdf(
file = paste0(Save.Path,"/",ProjectName,"_Clusters/",ProjectName,"_Heatmap_Cluster_top",top_NSet,".pdf"),
width = 10, height = 8
)
DoHeatmap(scRNA.SeuObj, features = top_N$gene,size = 2,angle = 60) +
scale_fill_gradient2(low="#5283ff",mid ="white", high ="#ff5c5c") +
theme(axis.text.y = element_text(size = 5)) +
theme(legend.position = "bottom" )
dev.off()
# --------------- Check specific tissue marker --------------- #
pdf(
file = paste0(Save.Path,"/",ProjectName,"_CT/",ProjectName,"_nlDR_CTMarker.pdf"),
width = 10, height = 8
)
# PMID: 31771616 #!!!!!!
FeaturePlot(scRNA.SeuObj, features = c("Cd3d", "Cd4", "Cd8a", "Csf1r", "Foxp3", "S100a9"), min.cutoff = "q9",
ncol = 3, coord.fixed = 1)
# T Cell: Cd3d; CD4+ T Cell: Cd4; CD8+ T Cell: Cd8a; Macrophages: Csf1r; regulatory T cells(Treg): Foxp3; Neutrophils: S100a9
# PMID: 34296197 #!!!!!
FeaturePlot(scRNA.SeuObj, features = c("Cd3d", "Cd3e", "Lyz1", "Lyz2","Clu","Cd79a","Ms4a1","Nkg7","Gzmb"), min.cutoff = "q9",
ncol = 3, coord.fixed = 1)
# T Cell: Cd3d,Cd3e; Macrophages: Lyz; Mast Cell: Clu; B Cell: Cd79a,Ms4a1; NK Cell: Nkg7,Gzmb
# http://biocc.hrbmu.edu.cn/CellMarker/
# Mast cell
FeaturePlot(scRNA.SeuObj, features = c("Cd117", "Cd25","Cd203c","Slc18a2","Kit","Fcer1a","Cd9"), min.cutoff = "q9", coord.fixed = 1)
# PMID: 30356731
FeaturePlot(scRNA.SeuObj, features = c("Cd9"), min.cutoff = "q9", coord.fixed = 1)
# PMID: 34296197 #!!!!!
FeaturePlot(scRNA.SeuObj, features = c("Cpa3"), min.cutoff = "q9", coord.fixed = 1)
# https://www.panglaodb.se/markers.html?cell_type=%27Mast%20cells%27
## Mac
# Macrophage-Markers
# https://www.biocompare.com/Editorial-Articles/566347-A-Guide-to-Macrophage-Markers/
## M0
FeaturePlot(scRNA.SeuObj, features = c("Cd68", "Adgre1","Cd14","Csf1r","Ly6c1",
"Cx3cr1","Fcgr1a","Itgam","Mertk"), min.cutoff = "q9", coord.fixed = 1)
## M1
# M1 http://biocc.hrbmu.edu.cn/CellMarker/
FeaturePlot(scRNA.SeuObj, features = c("Cd16","Cd32","Cd64","Cd68","Cd80","Cd86"), min.cutoff = "q9", coord.fixed = 1)
# M1 https://www.biocompare.com/Editorial-Articles/566347-A-Guide-to-Macrophage-Markers/
FeaturePlot(scRNA.SeuObj, features = c("Marco","Nos2","Tlr2","Cd80","Cd86","Csf2",
"Tnf","Il1b","Il6","Tlr4","Cxcl2","Ifng","Il1r1"), min.cutoff = "q9", coord.fixed = 1)
FeaturePlot(scRNA.SeuObj, features = c("Il1a","Il1b","Il6","Nos2","Tlr2","Tlr4","Cd80","Cd86"), min.cutoff = "q9", coord.fixed = 1)
## M2
# M2 http://biocc.hrbmu.edu.cn/CellMarker/
FeaturePlot(scRNA.SeuObj, features = c("Chil3","Csf1r","Mrc1","Pparg","Arg1","Cd163","Clec10a","Clec7a",
"Cd206","Cd209","Ccl18","Fizz1"), min.cutoff = "q9", coord.fixed = 1)
# https://www.biocompare.com/Editorial-Articles/566347-A-Guide-to-Macrophage-Markers/
FeaturePlot(scRNA.SeuObj, features = c("Cd115", "Cd206", "Pparg", "Arg1", "Cd163", "Cd301",
"Dectin-1", "Pdcd1lg2", "Fizz1"), min.cutoff = "q9", coord.fixed = 1)
FeaturePlot(scRNA.SeuObj, features = c("Chil3"), min.cutoff = "q9", coord.fixed = 1)
## Tumor associated macrophage(TAM)
FeaturePlot(scRNA.SeuObj, features = c("Ccr2","Csf1r","Marco","Pdl2","Cd40","Ccl2","Csf1","Cd16"),
min.cutoff = "q9", coord.fixed = 1)
# Erythrocytes
FeaturePlot(scRNA.SeuObj, features = c("Hbb-bs"), min.cutoff = "q9", coord.fixed = 1)
# Platelet
FeaturePlot(scRNA.SeuObj, features = c("Ppbp"), min.cutoff = "q9", coord.fixed = 1)
## Summary
markers.to.plot <- c("Cd3d","Cd3e", "Cd4","Cd8a", "Csf1r", "Lyz2","Chil3","Il1b", "S100a9","Nkg7",
"Gzmb", "Cd79a", "Ms4a1","Clu","Hbb-bs","Ppbp")
FeaturePlot(scRNA.SeuObj, features = markers.to.plot, min.cutoff = "q9", coord.fixed = 1)
# T Cell: Cd3d,Cd3e; CD4+ T Cell: Cd4; CD8+ T Cell: Cd8a; Macrophages: Csf1r,Lyz,Chil3; Neutrophils: S100a9;
# NK Cell: Nkg7,Gzmb; B Cell: Cd79a,Ms4a1; Mast Cell: Clu; Erythrocytes: Hbb-bs; Platelet: Ppbp
dev.off()
save.image(paste0(Save.Path,"/05_Identify_conserved_cell_type_markers.RData"))
##### 06 Cell type annotation #####
# scRNA.SeuObj.copy <- scRNA.SeuObj
## CD4+T: CD4+T Cell; CD8+T: CD8+T Cell; T: T Cell; B: B Cell; Mac: Macrophages;
## Neu: Neutrophils; NK: NK Cell; Mast: Mast Cell; Ery: Erythrocytes;
## Thr: Thrombocytes
scRNA.SeuObj <- RenameIdents(scRNA.SeuObj, `0` = "CD4+T", `1` = "B", `2` = "Mac3",
`3` = "Neu", `4` = "CD8+T", `5` = "CD8+T", `6` = "Mac2", `7` = "CD4+T",
`8` = "NK", `9` = "Neu",`10` = "Mast1", `11` = "T", `12` = "Ery", `13` = "Mac1",
`14` = "B", `15` = "B", `16` = "Mast2", `17` = "Mac0", `18` = "Neu")
Cell_Type_Order.set <- c("T", "CD4+T", "CD8+T", "B" , "Mac0", "Mac1", "Mac2", "Mac3", "Mast1", "Mast2", "NK", "Neu", "Ery")
scRNA.SeuObj$celltype <- Idents(scRNA.SeuObj)
# Idents(scRNA.SeuObj) <- "celltype"
## Heatmap
Heatmap_Color.lt <- list(low="#5283ff",mid ="white", high ="#ff5c5c")
pdf(
file = paste0(Save.Path,"/",ProjectName,"_CT/",ProjectName,"_Heatmap_CellType_top",top_NSet,".pdf"),
width = 10, height = 8
)
DoHeatmap(scRNA.SeuObj, features = top_N$gene,size = 3,angle = 60) +
scale_fill_gradient2(low = Heatmap_Color.lt[["low"]],
mid = Heatmap_Color.lt[["mid"]],
high = Heatmap_Color.lt[["high"]]) +
theme(axis.text.y = element_text(size = 5)) +
theme(legend.position = "bottom")+
theme(aspect.ratio=1)
dev.off()
# ## Ch
# # https://github.com/satijalab/seurat/issues/2960
# set.seed(1) # Fix the seed
# scRNA.SeuObj <- ScaleData(scRNA.SeuObj, verbose = FALSE)
DoHeatmap(scRNA.SeuObj, features = top_N$gene,size = 4,angle = 90) + NoLegend()
DoHeatmap(scRNA.SeuObj, features = top_N$gene,group.by = "celltype",size = 4,angle = 90) + NoLegend()
# Color
DoHeatmap(scRNA.SeuObj, features = top_N$gene,group.by = "celltype",size = 3,angle = 60) +
scale_fill_gradient2(low = Heatmap_Color.lt[["low"]],
mid = Heatmap_Color.lt[["mid"]],
high = Heatmap_Color.lt[["high"]])
DoHeatmap(scRNA.SeuObj, features = top_N$gene,group.by = "seurat_clusters",size = 3,angle = 90) + NoLegend()
pdf(
file = paste0(Save.Path,"/",ProjectName,"_CT/",ProjectName,"_Heatmap_CellType_top",top_NSet,".pdf"),
width = 10, height = 8
)
DoHeatmap(scRNA.SeuObj, features = top_N$gene,group.by = "celltype",size = 2,angle = 45) +
scale_fill_gradient2(low = Heatmap_Color.lt[["low"]],
mid = Heatmap_Color.lt[["mid"]],
high = Heatmap_Color.lt[["high"]]) +
theme(axis.text.y = element_text(size = 5)) +
theme(legend.position = "bottom" )
dev.off()
## UMAP tSNE
DimPlot(scRNA.SeuObj, label = TRUE) %>% BeautifyggPlot(.,LegPos = c(1, 0.5))
DimPlot(scRNA.SeuObj,group.by = "celltype",label.size = 7,label = TRUE,
pt.size =2) %>% BeautifyUMAP(FileName = paste0("/",Version,"/",ProjectName,"_CT/",ProjectName,"_nlDR_CellType"))
DimPlot(scRNA.SeuObj,group.by = colnames(list_files.df)[2],
pt.size =0.5) %>% BeautifyUMAP(FileName = paste0("/",Version,"/",ProjectName,"_CT/",ProjectName,"_nlDR_",colnames(list_files.df)[2]))
DimPlot(scRNA.SeuObj,group.by = "seurat_clusters",label.size = 7, label = TRUE,
pt.size =1) %>% BeautifyUMAP(FileName = paste0("/",Version,"/",ProjectName,"_Clusters/",ProjectName,"_nlDR_Clusters"))
pdf(
file = paste0(Save.Path,"/",ProjectName,"_CT/",ProjectName,"_nlDR_CellType_Sup.pdf"),
width = 12, height = 8
)
DimPlot(scRNA.SeuObj, reduction = "umap", label = TRUE, label.size = 7, repel = TRUE) %>%
BeautifyggPlot(.,LegPos = c(1, 0.5),AxisTitleSize=1.2, LegTextSize = 14)
for (i in 1:(ncol(list_files.df)+nrow(scAnno.df)-1)) {
print(DimPlot(scRNA.SeuObj, reduction = "umap", group.by = AnnoNames.set[i+3]) %>%
BeautifyggPlot(.,TV= -5,TitleSize = 25,LegPos = c(0.8, 0.15),AxisTitleSize=1.2, LegTextSize = 18)+
theme(plot.title = element_text(vjust = 0.85)))
print(DimPlot(scRNA.SeuObj, reduction = "umap", ncol = 2, split.by = AnnoNames.set[i+3], label = TRUE, label.size = 4) %>%
BeautifyggPlot(.,LegPos = c(1, 0.5),AxisTitleSize=1.2, TitleSize = 20,
SubTitSize = 17, LegTextSize = 14, XaThick=0.9, YaThick=0.9,OL_Thick = 1.5))
print(DimPlot(scRNA.SeuObj, reduction = "tsne", group.by = AnnoNames.set[i+3]) %>%
BeautifyggPlot(.,TV= -5,TitleSize = 25,LegPos = c(0.8, 0.15),AxisTitleSize=1.2, LegTextSize = 18)+
theme(plot.title = element_text(vjust = 0.85)))
print(DimPlot(scRNA.SeuObj, reduction = "tsne", ncol = 2, split.by = AnnoNames.set[i+3], label = TRUE, label.size = 4) %>%
BeautifyggPlot(.,LegPos = c(1, 0.5),AxisTitleSize=1.2, TitleSize = 20,
SubTitSize = 17, LegTextSize = 14, XaThick=0.9, YaThick=0.9,OL_Thick = 1.5))
}
rm(i)
dev.off()
## DotPlot
DotPlot_Color1.set <- c("#de3767", "#de3767", "#4169e1", "#4169e1")
DotPlot_Color2.set <- c("#5b8e7d","#7b2cbf")
DotPlot_Color3.set <- c("#de3767", "#4169e1")
pdf(
file = paste0(Save.Path,"/",ProjectName,"_CT/",ProjectName,"_DotPlot_CellType",".pdf"),
width = 10, height = 8
)
# https://satijalab.org/seurat/reference/dotplot
DotPlot(scRNA.SeuObj, features = markers.to.plot, cols = c("lightgrey", "blue"),
dot.scale = 8) + RotatedAxis()%>%
BeautifyggPlot(.,LegPos = "bottom",AxisTitleSize=1, TitleSize = 20, xangle =90,
LegDir = "horizontal",SubTitSize = 17 , LegTextSize = 14, XaThick=1, YaThick=1,XtextSize=12, YtextSize=12)
DotPlot(scRNA.SeuObj, features = markers.to.plot, cols = DotPlot_Color1.set,
dot.scale = 8, split.by = "sample") + RotatedAxis()
# https://github.com/satijalab/seurat/issues/1541
DotPlot(scRNA.SeuObj, features = markers.to.plot, cols = DotPlot_Color2.set,
dot.scale = 8, split.by = "Cachexia") + RotatedAxis()
DotPlot(scRNA.SeuObj, features = markers.to.plot, cols = DotPlot_Color3.set,
dot.scale = 8, split.by = "Sex") + RotatedAxis()
dev.off()
rm(top_N, top_NSet)
save.image(paste0(Save.Path,"/06_Cell_type_annotation.RData"))
# ##### Export marker gene from specific cluster #####
# # For performing differential expression after integration, we switch back to the original data
# set.seed(1) # Fix the seed
# DefaultAssay(scRNA.SeuObj) <- "RNA"
#
# # nk.markers <- FindConservedMarkers(scRNA.SeuObj, ident.1 = 6, grouping.var = "sample", verbose = FALSE)
# library(BiocManager)
# library(multtest)
# nk.markers <- FindConservedMarkers(scRNA.SeuObj, ident.1 = 'NK', grouping.var = "sample", verbose = FALSE)
# head(nk.markers)
#
# rm(nk.markers)
# ##### Identify differential expressed genes across conditions #####
# library(ggplot2)
# library(cowplot)
# theme_set(theme_cowplot())
# CD4T.cells <- subset(scRNA.SeuObj, idents = "CD4+T")
# Idents(CD4T.cells) <- "Cachexia"
# avg.CD4T.cells <- as.data.frame(log1p(AverageExpression(CD4T.cells, verbose = FALSE)$RNA))
# avg.CD4T.cells$gene <- rownames(avg.CD4T.cells)
#
# MacrophageM2 <- subset(scRNA.SeuObj, idents = "Mac2")
# Idents(MacrophageM2) <- "Cachexia"
# avg.MacrophageM2 <- as.data.frame(log1p(AverageExpression(MacrophageM2, verbose = FALSE)$RNA))
# avg.MacrophageM2$gene <- rownames(avg.MacrophageM2)
#
# genes.to.label = c("Sox17", "Mrpl15", "Lypla1", "Tcea1", "Rgs20", "Atp6v1h", "Rb1cc1", "4732440D04Rik", "St18")
# p1 <- ggplot(avg.CD4T.cells, aes(EO, LO)) + geom_point() + ggtitle("Cachexia T.cells")
# p1 <- LabelPoints(plot = p1, points = genes.to.label, repel = TRUE)
# p2 <- ggplot(avg.MacrophageM2, aes(EO, LO)) + geom_point() + ggtitle("Cachexia Macrophage")
# p2 <- LabelPoints(plot = p2, points = genes.to.label, repel = TRUE)
# p1 + p2
# rm(p1 , p2 ,CD4T.cells, MacrophageM2, avg.CD4T.cells, avg.MacrophageM2)
##### 07 Count Cell number #####
## Annotation Summary Table
AnnoSummary.lt <- AnnoSummary(scRNA.SeuObj, list_files.df, scAnno.df,
ClassSet = ClassSet1, ClassSet2 = ClassSet2)
Anno.df <- AnnoSummary.lt[["Anno.df"]]
ClassSet2.set <- Anno.df[,ClassSet2] %>% unique()
#### Export table ####
write.table( AnnoSummary.lt[["Freq_All_Cla.lt"]][["Freq_All_Cla.df"]] ,
file = paste0(Save.Path,"/",ProjectName,"_CellCount_CT_Cla.tsv"),
sep = "\t",
quote = F,
row.names = F
)
#### LinePlot ####
# https://ithelp.ithome.com.tw/articles/10186047
# Freq_All.df$Cell_Type <- factor(Freq_All.df$Cell_Type,
# levels = sort(unique(as.character(Freq_All.df$Cell_Type))))
Freq_All.df <- AnnoSummary.lt[["Freq_All.df"]]
Freq_All.df$Cell_Type <- factor(Freq_All.df$Cell_Type,
levels = Cell_Type_Order.set)
## Plot by State
CellNum_P1 <- ggplot(Freq_All.df, aes(x = factor(Cell_Type), y = Number,
colour = Pheno_Type, group = Pheno_Type)) +
geom_line(linetype = "dashed",size=1.5) +
geom_point(shape = 12, size = 4, fill = "white")+ theme_bw()+
theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())
CellNum_P1 %>% BeautifyggPlot(.,AspRat=1,LegPos = c(0.86, 0.85),AxisTitleSize=1.7,
XtextSize=15, YtextSize=15, xangle = 90,
LegTextSize = 15) +
theme(panel.border = element_rect(fill=NA,color="black", size=2, linetype="solid")) -> CellNum_P1
CellNum_P1
CellNum_P2 <- ggplot(Freq_All.df, aes(x = factor(Cell_Type), y = Percent,
colour = Pheno_Type, group = Pheno_Type)) +
geom_line(linetype = "dashed",size=1.5) +
geom_point(shape = 12, size = 4, fill = "white")+ theme_bw()+
theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())
CellNum_P2 %>% BeautifyggPlot(.,AspRat=1,LegPos = c(0.86, 0.85),AxisTitleSize=1.7,
XtextSize=18, YtextSize=18, xangle = 90,
LegTextSize = 15) +
theme(panel.border = element_rect(fill=NA,color="black", size=2, linetype="solid")) -> CellNum_P2
CellNum_P2
## Combine sex
Freq_All_Cla.df <- AnnoSummary.lt[["Freq_All_Cla.lt"]][["Freq_All_Cla.df"]]
# Freq_All_Cla.df$Cell_Type <- factor(Freq_All_Cla.df$Cell_Type,
# levels = sort(unique(as.character(Freq_All_Cla.df$Cell_Type))))
Freq_All_Cla.df$Cell_Type <- factor(Freq_All_Cla.df$Cell_Type,
levels = Cell_Type_Order.set)
CellNum_P3 <- ggplot(Freq_All_Cla.df, aes(x = factor(Cell_Type), y = Number,
colour = Pheno_Type,
group = Pheno_Type,linetype=Pheno_Type)) +
geom_line(aes(linetype=Pheno_Type))+
geom_line(size=1.5) +
scale_linetype_manual(name="Pheno_Type",
values=c("solid", "dotted","dotdash", "solid", "dotted", "dotdash"), # values=c("solid", "dashed", "dotted", "dotdash", "longdash", "twodash")
#labels=c("EO","EO.F","EO.M","LO","LO.F","LO.M")
) +
scale_color_manual(values = c('#ba0449','#ff52bd','#f0679b','#3d3c99','#5292f2','#33aef5'))+
geom_point(shape = 12, size = 4, fill = "white") + theme_bw()+
theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())
CellNum_P3 %>% BeautifyggPlot(.,AspRat=1,LegPos = c(0.4, 0.8),AxisTitleSize=1.7,
XtextSize=18, YtextSize=18,xangle = 90,
LegTextSize = 15) +
theme(panel.border = element_rect(fill=NA,color="black", size=2, linetype="solid")) -> CellNum_P3
CellNum_P3
library(eoffice)
topptx(CellNum_P3,paste0(Save.Path,"/Temp.pptx"))
CellNum_P4 <- ggplot(Freq_All_Cla.df, aes(x = factor(Cell_Type), y = Percent,
colour = Pheno_Type,
group = Pheno_Type,linetype=Pheno_Type)) +
geom_line(aes(linetype=Pheno_Type))+
geom_line(size=1.5) +
scale_linetype_manual(name="Pheno_Type",
values=c("solid", "dotted","dotdash", "solid", "dotted", "dotdash"), # values=c("solid", "dashed", "dotted", "dotdash", "longdash", "twodash")
#labels=c("EO","EO.F","EO.M","LO","LO.F","LO.M")
) +
scale_color_manual(values = c('#ba0449','#ff52bd','#f0679b','#3d3c99','#5292f2','#33aef5'))+
geom_point(shape = 12, size = 4, fill = "white") +
theme(panel.border = element_blank(),panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"))+
#theme_set(theme_bw())+ # Remove the background
theme_bw()+
theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())
CellNum_P4 %>% BeautifyggPlot(.,AspRat=1,LegPos = c(0.15, 0.82),AxisTitleSize=1.7,
XtextSize=18, YtextSize=,18, xangle = 90,
LegTextSize = 15) +
theme(panel.border = element_rect(fill=NA,color="black", size=2, linetype="solid")) -> CellNum_P4
CellNum_P4
rm(Freq_All_Cla.df)
#### BarPlot ####
# https://blog.gtwang.org/r/ggplot2-tutorial-layer-by-layer-plotting/3/
colnames(Anno.df)[ncol(Anno.df)] <- "Cell_Type"
Anno.df$Cell_Type <- factor(Anno.df$Cell_Type,
levels = sort(unique(as.character(Anno.df$Cell_Type))))
# sample
BarPlot1_1 <- ggplot(Anno.df, aes(Cell_Type, fill=Anno.df[,1])) +
geom_bar(position="dodge")+theme_bw()+
theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())
BarPlot1_1 %>% BeautifyggPlot(.,AspRat=1,LegPos = c(0.86, 0.85),AxisTitleSize=1.7,
XtextSize=18, YtextSize=,18, xangle = 90,
LegTextSize = 15) +
theme(panel.border = element_rect(fill=NA,color="black", size=2, linetype="solid"))+
labs(fill=colnames(Anno.df)[1]) -> BarPlot1_1
BarPlot1_1
#### Export PDF file ####
pdf(file = paste0(Save.Path,"/",ProjectName,"_CellCount_LinePlot.pdf"),
width = 7, height = 7 )
CellNum_P4
CellNum_P3
CellNum_P1
CellNum_P2
for (i in 1:(ncol(Anno.df)-1)) {
# sample
BarPlot1_1 <- ggplot(Anno.df, aes(Cell_Type, fill = Anno.df[,i])) +
geom_bar(position="dodge")+theme_bw()+
theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())
BarPlot1_1 %>% BeautifyggPlot(.,AspRat=1,LegPos = c(0.86, 0.85),AxisTitleSize=1.7,
XtextSize=18, YtextSize=,18, xangle = 90,
LegTextSize = 15) +
theme(panel.border = element_rect(fill=NA,color="black", size=2, linetype="solid"))+
labs(fill=colnames(Anno.df)[i]) -> BarPlot1_1
print(BarPlot1_1)
BarPlot1_2 <- ggplot(Anno.df, aes(Cell_Type, fill = Anno.df[,i])) +
geom_bar(position="fill")+theme_bw()+
theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())
BarPlot1_2 %>% BeautifyggPlot(.,AspRat=1,LegPos = c(-0.08, -0.08),AxisTitleSize=1.7,
XtextSize=18, YtextSize=,18, xangle = 90,
LegTextSize = 15) +
theme(panel.border = element_rect(fill=NA,color="black", size=2, linetype="solid")) +
labs(fill=colnames(Anno.df)[i]) -> BarPlot1_2
print(BarPlot1_2)
rm(BarPlot1_1,BarPlot1_2)
}
rm(i)
dev.off() # graphics.off()
rm(CellNum_P1,CellNum_P2,CellNum_P3,CellNum_P4)
rm(Anno.df)
#### Save RData ####
save.image(paste0(Save.Path,"/07_Count_Cell_number.RData"))
##### 08_1 Find CCmarker in different Cell type and VolcanoPlot (SSA) ########
#### Define group by different phenotype ####
source("FUN_Find_Markers.R")
scRNA.SeuObj$celltype <- Idents(scRNA.SeuObj)
# for (i in 1:(ncol(list_files.df)-1)) {
# scRNA.SeuObj[[paste0("celltype.",colnames(list_files.df)[1+i])]] <- paste(Idents(scRNA.SeuObj), as.matrix(scRNA.SeuObj[[colnames(list_files.df)[1+i]]]), sep = "_")
#
# }
scRNA.SeuObj[[paste0("celltype.",ClassSet2)]] <- paste(Idents(scRNA.SeuObj),
as.matrix(scRNA.SeuObj[[ClassSet2]]), sep = "_")
scRNA.SeuObj[[paste0("celltype.",ClassSet2,".",ClassSet3)]] <- paste(Idents(scRNA.SeuObj),
as.matrix(scRNA.SeuObj[[ClassSet2]]),
as.matrix(scRNA.SeuObj[[ClassSet3]]), sep = "_")
Idents(scRNA.SeuObj) <- paste0("celltype.",ClassSet2,".",ClassSet3)
DefaultAssay(scRNA.SeuObj) <- "RNA"
ClassSet2.set <- list_files.df[[ClassSet2]] %>% unique()
ClassSet3.set <- list_files.df[[ClassSet3]] %>% unique()
CellType.list <- as.character(unique([email protected][["celltype"]]))
# CellType.list <- CellType.list[-9]
####-------------- Find Marker gene in Male --------------####
Sep_Cla3_FMar.Path <- paste0(Sampletype,"_",ProjectName,"_Separate_",ClassSet3.set[1],"_FindMarkers")
dir.create(paste0(Save.Path,"/",Sep_Cla3_FMar.Path))
# About 15 mins
CCMarker_Male.lt <- list()
for(i in c(1:length(CellType.list))){
try({
CCMarker_Male.lt[[i]] <- Find_Markers(scRNA.SeuObj,
paste0(CellType.list[i],"_",ClassSet2.set[1],"_",ClassSet3.set[1]),
paste0(CellType.list[i],"_",ClassSet2.set[2],"_",ClassSet3.set[1]),
CellType.list[i],
Path = Save.Path,
ResultFolder = paste0(Sep_Cla3_FMar.Path),
ProjectTitle = ProjectName)
# names(CCMarker_Male.lt)[[i]] <- paste0("CCMarker_Male.lt.",CellType.list[i])
names(CCMarker_Male.lt)[[i]] <- paste0(CellType.list[i])
})
}
rm(i)
CCMarker_Male.lt <- CCMarker_Male.lt[!unlist(lapply(CCMarker_Male.lt,is.null))]
## Generate pdf and tif file for Male VolcanoPlot
Sep_Cla3_Volcano.Path <- paste0(Sampletype,"_",ProjectName,"_Separate_",ClassSet3.set[1],"_VolcanoPlot")
dir.create(paste0(Save.Path,"/",Sep_Cla3_Volcano.Path ))
pdf(file = paste0(Save.Path,"/",Sep_Cla3_Volcano.Path,"/",Sep_Cla3_Volcano.Path,".pdf"),
width = 7, height = 7 )
for (i in 1:length(CellType.list)) {
try({
print(VolcanoPlot(CCMarker_Male.lt[[i]][[paste0(ProjectName, "Marker.S")]],
CCMarker_Male.lt[[i]][[paste0(ProjectName, "Marker.S_Pos_List")]],
CCMarker_Male.lt[[i]][[paste0(ProjectName, "Marker.S_Neg_List")]], ShowGeneNum = 6)+
ggtitle(paste0(ProjectName,"_",ClassSet3.set[1],"_",CellType.list[i]))
)
})
}
dev.off() # graphics.off()
rm(i)
for (i in 1:length(CellType.list)) {
try({
tiff(file = paste0(Save.Path,"/",Sep_Cla3_Volcano.Path,"/",Sep_Cla3_Volcano.Path,"_",CellType.list[i],".tif"),
width = 17, height = 17, units = "cm", res = 200)
print(VolcanoPlot(CCMarker_Male.lt[[i]][[paste0(ProjectName, "Marker.S")]],
CCMarker_Male.lt[[i]][[paste0(ProjectName, "Marker.S_Pos_List")]],
CCMarker_Male.lt[[i]][[paste0(ProjectName, "Marker.S_Neg_List")]])+
ggtitle(paste0(ProjectName,"_",ClassSet3.set[1],"_",CellType.list[i]))
)
graphics.off()
})
}
rm(i,Sep_Cla3_FMar.Path)
####-------------- Find Marker gene in Female --------------####
Sep_Cla3_FMar.Path <- paste0(Sampletype,"_",ProjectName,"_Separate_",ClassSet3.set[2],"_FindMarkers")
dir.create(paste0(Save.Path, "/", Sep_Cla3_FMar.Path))
# About 15 mins
CCMarker_Female.lt <- list()
for(i in c(1:length(CellType.list))){
try({
CCMarker_Female.lt[[i]] <- Find_Markers(scRNA.SeuObj,
paste0(CellType.list[i],"_",ClassSet2.set[1],"_",ClassSet3.set[2]),
paste0(CellType.list[i],"_",ClassSet2.set[2],"_",ClassSet3.set[2]),
CellType.list[i],
Path = Save.Path,
ResultFolder = paste0(Sep_Cla3_FMar.Path),
ProjectTitle = ProjectName)
# names(CCMarker_Female.lt)[[i]] <- paste0("CCMarker_Female.lt.",CellType.list[i])
names(CCMarker_Female.lt)[[i]] <- paste0(CellType.list[i])
})
}
rm(i)
CCMarker_Female.lt <- CCMarker_Female.lt[!unlist(lapply(CCMarker_Female.lt,is.null))]
## Generate pdf and tif file for Female VolcanoPlot
Sep_Cla3_Volcano.Path <- paste0(Sampletype,"_",ProjectName,"_Separate_",ClassSet3.set[2],"_VolcanoPlot")
dir.create(paste0(Save.Path,"/",Sep_Cla3_Volcano.Path ))
# dir.create(paste0(Save.Path,"/PBMC_SSA_Female_VolcanoPlot/"))
pdf(file = paste0(Save.Path,"/",Sep_Cla3_Volcano.Path,"/",Sep_Cla3_Volcano.Path,".pdf"),
width = 7, height = 7 )
for (i in 1:length(CellType.list)) {
try({
print(VolcanoPlot(CCMarker_Female.lt[[i]][[paste0(ProjectName, "Marker.S")]],
CCMarker_Female.lt[[i]][[paste0(ProjectName, "Marker.S_Pos_List")]],
CCMarker_Female.lt[[i]][[paste0(ProjectName, "Marker.S_Neg_List")]], ShowGeneNum = 6)+
ggtitle(paste0(ProjectName,"_",ClassSet3.set[2],"_",CellType.list[i]))
)
})
}
dev.off() # graphics.off()
rm(i)
for (i in 1:length(CellType.list)) {
try({
tiff(file = paste0(Save.Path,"/",Sep_Cla3_Volcano.Path,"/",Sep_Cla3_Volcano.Path,"_",CellType.list[i],".tif"),
width = 17, height = 17, units = "cm", res = 200)
print(VolcanoPlot(CCMarker_Female.lt[[i]][[paste0(ProjectName, "Marker.S")]],
CCMarker_Female.lt[[i]][[paste0(ProjectName, "Marker.S_Pos_List")]],
CCMarker_Female.lt[[i]][[paste0(ProjectName, "Marker.S_Neg_List")]])+
ggtitle(paste0(ProjectName,"_",ClassSet3.set[2],"_",CellType.list[i]))
)
graphics.off()
})
}
rm(i,Sep_Cla3_FMar.Path)
save.image(paste0(Save.Path,"/08_1_Find_",Sampletype,"_",ProjectName,"marker_in_different_Cell_type_and_VolcanoPlot(Separate).RData"))
##### 08_2 Find CCmarker in different Cell type and VennDiagrame (SSA_IntersectCT) ########
##-------------- Intersect_CellType --------------##
intersect_CellType <- intersect(names(CCMarker_Male.lt),names(CCMarker_Female.lt))
CCMarker_Male.lt <- CCMarker_Male.lt[names(CCMarker_Male.lt) %in% intersect_CellType]
CCMarker_Female.lt <- CCMarker_Female.lt[names(CCMarker_Female.lt) %in% intersect_CellType]
CellType.list <- names(CCMarker_Male.lt)
##-------------- Venn Pos --------------##
source("FUN_Venn.R")
# pdf(file = paste0(Save.Path,"/PBMC_Female_VolcanoPlot.pdf"),width = 7, height = 7 )
Sep_Cla3_Venn.Path <- paste0(Sampletype,"_",ProjectName,"_Separate_","_VennDiagrame")
dir.create(paste0(Save.Path,"/",Sep_Cla3_Venn.Path))
Venn_CCMarker_Pos <- list()
for(i in c(1:length(CellType.list))){
try({
Venn_CCMarker_Pos[[i]] <- Venn_Intersect(CCMarker_Male.lt[[paste0(CellType.list[i])]][[paste0(ProjectName, "Marker.S_Pos_List")]],
CCMarker_Female.lt[[paste0(CellType.list[i])]][[paste0(ProjectName, "Marker.S_Pos_List")]],
CellType.list[i],"Pos","#9d0208","#f08080",SampleType = Sampletype,
PathName = paste0(Save.Path,"/",Sep_Cla3_Venn.Path),
ClassSet3_1 = ClassSet3.set[1], ClassSet3_2 =ClassSet3.set[2])
names(Venn_CCMarker_Pos)[[i]] <- paste0("Venn_",ProjectName,"Marker.",CellType.list[i],"_Pos")
})
}
rm(i)
##-------------- Venn Neg --------------##
Venn_CCMarker_Neg <- list()
for(i in c(1:length(CellType.list))){
try({
Venn_CCMarker_Neg[[i]] <- Venn_Intersect(CCMarker_Male.lt[[paste0(CellType.list[i])]][[paste0(ProjectName, "Marker.S_Neg_List")]],
CCMarker_Female.lt[[paste0(CellType.list[i])]][[paste0(ProjectName, "Marker.S_Neg_List")]],
CellType.list[i],"Neg","#00296b","#1368aa",SampleType=Sampletype,
PathName = paste0(Save.Path,"/",Sep_Cla3_Venn.Path))
names(Venn_CCMarker_Neg)[[i]] <- paste0("Venn_",ProjectName,"Marker.",CellType.list[i],"_Neg")
})
}
rm(i,Sep_Cla3_Venn.Path)
save.image(paste0(Save.Path,"/08_2_Find_",Sampletype,"_",ProjectName,"marker_in_different_Cell_type_and_Venn.RData"))
##### 08_3 Find CCmarker in different Cell type and VolcanoPlot (SPA) ########
### Define group by different phenotype ###
source("FUN_Find_Markers.R")
Idents(scRNA.SeuObj) <- "celltype.Cachexia"
Sep_Cla3_FMar.Path <- paste0(Sampletype,"_",ProjectName,"_Pooled_FindMarkers")
dir.create(paste0(Save.Path,"/",Sep_Cla3_FMar.Path))
CCMarker_SPA.lt <- list()
for(i in c(1:length(CellType.list))){
try({
CCMarker_SPA.lt[[i]] <- Find_Markers(scRNA.SeuObj,
paste0(CellType.list[i],"_",ClassSet2.set[1]),
paste0(CellType.list[i],"_",ClassSet2.set[2]),
CellType.list[i],
Path = Save.Path,
ResultFolder = Sep_Cla3_FMar.Path,
ProjectTitle = ProjectName)
# names(CCMarker_SPA.lt)[[i]] <- paste0("CCMarker_SPA.lt.",CellType.list[i])
names(CCMarker_SPA.lt)[[i]] <- paste0(CellType.list[i])
})
}
rm(i,Sep_Cla3_FMar.Path)
CCMarker_SPA.lt <- CCMarker_SPA.lt[!unlist(lapply(CCMarker_SPA.lt,is.null))]
## Generate pdf and tif file for VolcanoPlot
Sep_Cla3_Volcano.Path <- paste0(Sampletype,"_",ProjectName,"_Pooled_","_VolcanoPlot")
dir.create(paste0(Save.Path,"/",Sep_Cla3_Volcano.Path))
pdf(file = paste0(Save.Path,"/",Sep_Cla3_Volcano.Path,"/",Sep_Cla3_Volcano.Path,".pdf"),width = 7, height = 7 )
for (i in 1:length(CellType.list)) {
try({
print(VolcanoPlot(CCMarker_SPA.lt[[i]][[paste0(ProjectName,"Marker.S")]],
CCMarker_SPA.lt[[i]][[paste0(ProjectName,"Marker.S_Pos_List")]],
CCMarker_SPA.lt[[i]][[paste0(ProjectName,"Marker.S_Neg_List")]], ShowGeneNum = 6)+
ggtitle(paste0(Sampletype,"_",CellType.list[i]))
)
})
}
# graphics.off()
dev.off()
rm(i)
for (i in 1:length(CellType.list)) {
try({
tiff(file = paste0(Save.Path,"/",Sep_Cla3_Volcano.Path,"/",Sep_Cla3_Volcano.Path,"_",CellType.list[i],".tif"), width = 17, height = 17, units = "cm", res = 200)
print(VolcanoPlot(CCMarker_SPA.lt[[i]][[paste0(ProjectName,"Marker.S")]],
CCMarker_SPA.lt[[i]][[paste0(ProjectName,"Marker.S_Pos_List")]],
CCMarker_SPA.lt[[i]][[paste0(ProjectName,"Marker.S_Neg_List")]])+
ggtitle(paste0(Sampletype,"_",CellType.list[i]))
)
graphics.off()
})
}
rm(i,Sep_Cla3_Volcano.Path)
save.image(paste0(Save.Path,"/08_3_Find__",Sampletype,"_",ProjectName,"marker_in_different_Cell_type_and_VolcanoPlot(Pooled).RData"))
################## (Pending) CCmarker matrix (Heatmap) ##################
################## (Pending) CCmarker matrix LogFC (Heatmap) ##################
#####------------------------------------------------------------------------------------------------------------#####
##### 09_0 GSEA Analysis (Geneset Prepare) #####
# https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2018/RNASeq2018/html/06_Gene_set_testing.nb.html
# install # https://bioconductor.org/packages/release/bioc/html/GSEABase.html
library(fgsea)
source("FUN_GSEA_LargeGeneSet.R")
source("FUN_HSsymbol2MMsymbol.R")
source("FUN_GSEA_ggplot.R")