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DR_PRJCA001063_PDAC.R
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DR_PRJCA001063_PDAC.R
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## (scRNA-seq data analysis for H5AD files)
#############
rm(list = ls()) # Clean variable
memory.limit(150000)
set.seed(1) # Fsix the seeds
############# Library list #############
library(SummarizedExperiment)
library(Seurat)
library(SeuratDisk)
library(stringr)
library(SeuratWrappers)
library(monocle3)
library(AnnotationDbi)
library(org.Mm.eg.db)
library('org.Hs.eg.db')
library(Hmisc)
library(dplyr)
library(tidyverse)
library(garnett)
# library(cicero) # cicero????monocle?|?M?s????monocle3?Ĭ?
# detach("package:monocle", unload = TRUE) # ???~: package 'monocle' is required by 'cicero' so will not be detached
############# Import files settings #############
## General setting
ProjectName = "TOP2A"
Sampletype = "PDAC"
PathName = paste0(Sys.Date(),"_",ProjectName,"_",Sampletype)
Save.Path = paste0(getwd(),"/",PathName)
## Create new folder
if (!dir.exists(Save.Path)){
dir.create(Save.Path)
}
## Marker genes file
Garnett_Marker_file_Name <- c("NAKAMURA_METASTASIS_MODEL_M18483")
Garnett_Marker_file <- paste0(PathName,"/marker_file_",Garnett_Marker_file_Name,".txt")
## Cell cycle genes file
cc.genes_list <- read.csv(paste0(PathName,"/Cell cycle/regev_lab_cell_cycle_genesCh.csv")) # A list of cell cycle markers, from Tirosh et al, 2015, is loaded with Seurat.
# cc: Cell-Cycle
############# Parameter setting #############
## Gene list of interest
Main = c("TOP2A")
Main_Group = c("TOP2A","TP53","CGAS","PTK2")
Main_Group2 = c("TOP2A","CGAS","H2AX","PTK2","NSUN2","TP53","MYC","TOP2B","EXO1","KRAS","MUC1","AMBP","FXYD2","TOP2B","CCNE1")
candidates14 = c("BRIP1","KIF23","TOP2A","FOSL1","FAM25A","ANLN","NCAPH","KRT9","MCM4","CKAP2L","CENPE","RACGAP1","DTL","RAD51AP1")
Main_CNV <- c("CDT1","CDC6","RAD17","GMNN")
## Color setting
colors_cc <-c("#FF9912B3", "#2e6087", "#417034") ## Color for Cell-Cycle
colors_cc2 <- c("#59c26b", "#2e6087", "#417034") ## Color for Cell-Cycle
colors_ccOri <- c("#FF9912B3", "#32CD3299", "#4169E1B3") ## Color for Cell-Cycle
## Format of data
GeneNAFMT <- c("HuGSymbol") # Gene names format of data: HuGSymbol,MouGSymbol,HuENSEMBL,MouENSEMBL
## Cluster cells setting
k_cds_sub_DucT2 <- c(5) # k for ductal cell type2
k_cds_sub_AcinaDucT <- 7 # k for Acinar + ductal cell type
k_cds_sub_DucT2_HG <- c(4)
## Threshold of PCA scores
PCAThreshold_Pos <- 0.03
PCAThreshold_Neg <- -0.03
#********************************************************************************************************************#
################## Function setting ##################
## Call function
filePath <- ""
#?פJ ?P?@?Ӹ??Ƨ?????R?ɮ?
getFilePath <- function(fileName) {
# path <- setwd("~") #?M???Ƨ????????|
path <- setwd(getwd())
#?r???X?ֵL???j
# ?u<<-?v???????ܼƵ??Ȫ?????
filePath <<- paste0(path ,"/" , fileName)
# ???J?ɮ?
sourceObj <- source(filePath)
return(sourceObj)
}
#********************************************************************************************************************#
############# Import raw data #############
library(SummarizedExperiment)
library(Seurat)
library(SeuratDisk)
## Convert h5ad to h5seurat
Convert(paste0(PathName,"/StdWf1_PRJCA001063_CRC_besca2.raw.h5ad"), "PRJCA001063.h5seurat", assay = "RNA",) # This creates a copy of this .h5ad object reformatted into .h5seurat inside the example_dir directory
seuratObject <- LoadH5Seurat(paste0(PathName,"/PRJCA001063.h5seurat")) # This .d5seurat object can then be read in manually
## Convert Seurat Object to Monocle3 Object
library(SeuratWrappers)
cds <- as.cell_data_set(seuratObject) # Convert objects to Monocle3 'cell_data_set' objects
#---------------------------------------------------------------------------------------------------------------------#
############# Run Monocle3 #############
library(monocle3)
cds <- estimate_size_factors(cds) # issues with cds object in monocle3 #54 # https://github.com/satijalab/seurat-wrappers/issues/54
###### Pre-process the data ######
cds <- preprocess_cds(cds, num_dim = 100)
plot_pc_variance_explained(cds)
###### Reduce dimensionality and visualize the cells ######
##### UMAP #####
cds <- reduce_dimension(cds,preprocess_method = 'PCA')
plot_cells(cds)
##### (UMAP) Plot genes #####
plot_cells(cds, genes=c("TOP2A","TOP2B","TP53")) #error
## !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
cds@rowRanges@elementMetadata@listData$gene_short_name <- cds@assays@data@listData[["counts"]]@Dimnames[[1]]
plot_cells(cds, genes=c("TOP2A","TOP2B","TP53","CCNE1")) #ok
plot_cells(cds, genes=c(Main),cell_size=1,label_cell_groups = FALSE, show_trajectory_graph = FALSE)
plot_cells(cds, genes=c(Main_Group),cell_size=0.5,label_cell_groups = FALSE, show_trajectory_graph = FALSE)
plot_cells(cds, genes=c(Main_Group2),cell_size=0.5,label_cell_groups = FALSE, show_trajectory_graph = FALSE)
##### (UMAP) Plot different phenotype #####
plot_cells(cds, color_cells_by="Cell_type", label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds, color_cells_by="Type", label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds, color_cells_by="Patient", label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds, color_cells_by="CONDITION", label_cell_groups=FALSE, show_trajectory_graph = FALSE)
##### Group cells into clusters ######
set.seed(1) # Fix the seed
cds <- cluster_cells(cds)
plot_cells(cds, color_cells_by = "partition", label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds, color_cells_by = "cluster", label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds, label_cell_groups=FALSE, show_trajectory_graph = FALSE)
##### #####
## Plot the violin diagram
Maingroup_ciliated_genes <- c(Main_Group)
cds_marrow_cc <- cds[rowData(cds)$gene_short_name %in% Maingroup_ciliated_genes,]
cds_marrow_TOP2A <- cds[rowData(cds)$gene_short_name %in% "TOP2A",]
plot_genes_violin(cds_marrow_cc, group_cells_by="Cell_type", ncol=2, log_scale = FALSE)
plot_genes_violin(cds_marrow_cc, group_cells_by="Cell_type", ncol=2, log_scale = T)
plot_genes_violin(cds_marrow_cc, group_cells_by="Cell_type", ncol=2, log_scale = T)+
geom_boxplot(width=0.1, fill="white",alpha = 0.7) + theme(axis.text.x=element_text(angle=45, hjust=1))
source("FUN_Beautify_ggplot.R")
plot_genes_violin(cds_marrow_TOP2A, group_cells_by="Cell_type", ncol=2, log_scale = T) %>%
BeautifyggPlot(AspRat=0.5,AxisTitleSize=2, xangle =90 ,OL_Thick = 3) +
geom_boxplot(width=0.1, fill="white",alpha = 0.7,TH= 10)
############ Cell-Cycle Scoring and Regression - Monocle3 & Seurat Mutual conversion #############
############# Run Seurat #############
## Load package
library(Seurat)
library(SummarizedExperiment)
library(AnnotationDbi)
library(org.Mm.eg.db)
library('org.Hs.eg.db')
library(Hmisc)
###### Convert Monocle3 Object to Seurat Object ######
getFilePath("Monocle3_To_Seurat.R")
marrow <- Monocle3_To_Seurat(cds,"cds") #?o??function?s?b??Monocle3_To_Seurat.R?̭?
###### Assign Cell-Cycle Scores ######
getFilePath("Cell-Cycle Scoring and Regression.R")
marrow <- CCScorReg(GeneNAFMT,marrow) #?o??function?s?b??Cell-Cycle Scoring and Regression.R?̭?
RidgePlot(marrow,cols = colors_cc, features = c(Main), ncol = 1)
RidgePlot(marrow,cols = colors_cc, features = c(Main_Group), ncol = 2,y.max = 100)
RidgePlot(marrow,cols = colors_cc, features = c(Main_Group), ncol = 2,log=TRUE)
###### Insert the cell cycle results from Seurat into the Monocle3 cds object ######
cds@colData@listData$cell_cycle <- marrow@active.ident
# cds@colData@listData$cell_cycle <- marrow@meta.data[["Phase"]]
plot_cells(cds, color_cells_by="cell_cycle", label_cell_groups=FALSE ,show_trajectory_graph = F) + scale_color_manual(values = colors_cc)
## Plot the violin diagram
Maingroup_ciliated_genes <- c(Main_Group)
cds_marrow_cc <- cds[rowData(cds)$gene_short_name %in% Maingroup_ciliated_genes,]
plot_genes_violin(cds_marrow_cc, group_cells_by="cell_cycle", ncol=2, log_scale = FALSE)+ scale_fill_manual(values = colors_cc)
plot_genes_violin(cds_marrow_cc, group_cells_by="cell_cycle", ncol=2, log_scale = T)+ scale_fill_manual(values = colors_cc)
plot_genes_violin(cds_marrow_cc, group_cells_by="cell_cycle", ncol=2, log_scale = T)+ scale_fill_manual(values = colors_cc)+
geom_boxplot(width=0.1, fill="white",alpha = 0.7) + theme(axis.text.x=element_text(angle=45, hjust=1))
############## Annotate your cells according to type (Custom Marker) ##############
#################### Cell discrimination by AddModuleScore ####################
getFilePath("Monocle3_AddModuleScore.R")
set.seed(1) # Fix the seed
Marker_PDAC_file_Name <- c("GRUETZMANN_PANCREATIC_CANCER_UP")
Marker_PDAC_Name <- c("PDAC")
cds <- Monocle3_AddModuleScore(Marker_PDAC_file_Name,Marker_PDAC_Name,marrow,cds)
plot_cells(cds, color_cells_by= Marker_PDAC_Name, label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds, color_cells_by= Marker_PDAC_Name, label_cell_groups=FALSE, show_trajectory_graph = FALSE,cell_size = 1.2) +
scale_colour_gradient2(low = "#440075", mid = "#ffd261", high = "#4aff8c",
guide = "colourbar",midpoint = 0.2, labs(fill = Marker_PDAC_Name))
#################### Cell discrimination by Garnett ####################
Human_classifier_cds <- train_cell_classifier(cds = cds,
marker_file = Garnett_Marker_file, # Import the marker_file
db=org.Hs.eg.db::org.Hs.eg.db, cds_gene_id_type = "SYMBOL",
#num_unknown = 2215, max_training_samples = 10000,
marker_file_gene_id_type = "SYMBOL",cores=8)
cds_Garnett <- classify_cells(cds, Human_classifier_cds, db = org.Hs.eg.db::org.Hs.eg.db,
cluster_extend = TRUE, cds_gene_id_type = "SYMBOL")
plot_cells(cds_Garnett,group_cells_by="cluster",cell_size=1.5,
color_cells_by="cluster_ext_type", show_trajectory_graph = FALSE)
plot_cells(cds_Garnett,group_cells_by="cluster",cell_size=1.5,
color_cells_by="cluster_ext_type",label_cell_groups=FALSE, show_trajectory_graph = FALSE)
####################### Constructing single-cell trajectories #######################
cds2 <-cds # Keep the object without trajectories in cds2
cds <- learn_graph(cds)
plot_cells(cds,
color_cells_by = "cluster",
label_cell_groups=FALSE, label_leaves=FALSE, label_branch_points=FALSE, graph_label_size=1.5)
cds <- order_cells(cds)
plot_cells(cds,
color_cells_by = "pseudotime",
label_cell_groups=FALSE, label_leaves=FALSE, label_branch_points=FALSE, graph_label_size=1.5)
MainGroup_lineage_cds <- cds[rowData(cds)$gene_short_name %in% Main_Group]
plot_genes_in_pseudotime(MainGroup_lineage_cds, color_cells_by="cell_cycle",cell_size=2,
min_expr=0.5)+ scale_color_manual(values = colors_cc)
#************************************************************************************************************************#
###################################### cds_subset ########################################
################## Grab specific terms ##################
## grepl Ductal cell type 2
cds_sub_DucT2 <- cds[,grepl("Ductal cell type 2", colData(cds)$Cell_type, ignore.case=TRUE)]
plot_cells(cds_sub_DucT2, color_cells_by="partition")
plot_cells(cds_sub_DucT2, color_cells_by="partition", show_trajectory_graph = F)
plot_cells(cds_sub_DucT2, genes=c(Main),cell_size=1,label_cell_groups = FALSE, show_trajectory_graph = FALSE)
plot_cells(cds_sub_DucT2, genes=c(Main_Group),cell_size=0.5,label_cell_groups = FALSE, show_trajectory_graph = FALSE)
## Ductal cell type & cds_sub_AcinaDucT
cds_sub_AcinaDucT <- cds[,colData(cds)$Cell_type %in% c("Acinar cell","Ductal cell type 1","Ductal cell type 2")]
plot_cells(cds_sub_AcinaDucT, color_cells_by="cluster", show_trajectory_graph = F)
######################## AcinaDucT (choose_cells) ##########################
cds_sub_AcinaDucT <- choose_cells(cds)
plot_cells(cds_sub_AcinaDucT, color_cells_by="cluster", show_trajectory_graph = F)
plot_cells(cds_sub_AcinaDucT , genes=c(Main_Group), show_trajectory_graph = FALSE) #ok
plot_cells(cds_sub_AcinaDucT , genes=c("H2AX"), cell_size = 0.8,show_trajectory_graph = FALSE,label_cell_groups=FALSE) #ok
plot_cells(cds_sub_AcinaDucT , genes=c("EXO1"), cell_size = 0.8,show_trajectory_graph = FALSE,label_cell_groups=FALSE) #ok
plot_cells(cds_sub_AcinaDucT , genes=c("MYC"), cell_size = 0.8,show_trajectory_graph = FALSE,label_cell_groups=FALSE) #ok
plot_cells(cds_sub_AcinaDucT , genes=c(Main_CNV), show_trajectory_graph = FALSE) #ok
set.seed(1) # Fix the seed
cds_sub_AcinaDucT_NewK <- cluster_cells(cds_sub_AcinaDucT,k = k_cds_sub_AcinaDucT, resolution=1e-5)
plot_cells(cds_sub_AcinaDucT_NewK, color_cells_by = "cluster",cell_size=2, label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds_sub_AcinaDucT_NewK, color_cells_by = "cluster",cell_size=2,
label_cell_groups=TRUE, show_trajectory_graph = FALSE, group_label_size = 5)
######## Reorganize the Cluster for AcinaDucT ####
cds_sub_AcinaDucT_NewK_ReCluster <- cds_sub_AcinaDucT_NewK
colData(cds_sub_AcinaDucT_NewK_ReCluster)$assigned_cell_type <-
as.character(clusters(cds_sub_AcinaDucT_NewK_ReCluster)[colnames(cds_sub_AcinaDucT_NewK_ReCluster)])
colData(cds_sub_AcinaDucT_NewK_ReCluster)$assigned_cell_type <-
dplyr::recode(colData(cds_sub_AcinaDucT_NewK_ReCluster)$assigned_cell_type,
"6"="AC",
"24"="nAtD",
"29"="nAtD",
"14"="aAtD",
"8"="ND01",
"1"="ND02",
"3"="ND03",
"13"="ND04",
"2"="AD",
"28"="CDOri",
"18"="CoreCD01",
"26"="CoreCD02",
# "?" ="CoreCD03",
"4"="CoreCD00",
"19"="CoreCD04",
"9"="CoreCD05",
"16"="CoreCD06",
"15"="CoreCD07",
# "?" ="CoreCD08",
"11"="DistalCD01",
"25"="DistalCD02",
"30"="DistalCD02",
"22"="DistalCD03",
"12"="DistalCD04",
"21"="DistalCD05",
"10"="DistalCD06",
"17"="DistalCD07",
"27"="DistalCD07",
"7"="DistalCD08",
"23"="DistalCD09",
"5"="DistalCD10",
"20"="DistalCD11")
cds_sub_AcinaDucT_NewK_ReCluster@colData@listData[["ReCluster"]] <- cds_sub_AcinaDucT_NewK_ReCluster@colData@listData[["assigned_cell_type"]]
## CoreCD03
cds_sub_AcinaDucT_NewK_ReCluster_CoreCD03 <- choose_cells(cds_sub_AcinaDucT_NewK_ReCluster)
colData(cds_sub_AcinaDucT_NewK_ReCluster_CoreCD03)$assigned_cell_type <- "CoreCD03"
cds_sub_AcinaDucT_NewK_ReCluster_CoreCD03@colData@listData[["ReCluster"]] <- cds_sub_AcinaDucT_NewK_ReCluster_CoreCD03@colData@listData[["assigned_cell_type"]]
## CoreCD08
cds_sub_AcinaDucT_NewK_ReCluster_CoreCD08 <- choose_cells(cds_sub_AcinaDucT_NewK_ReCluster)
colData(cds_sub_AcinaDucT_NewK_ReCluster_CoreCD08)$assigned_cell_type <- "CoreCD08"
cds_sub_AcinaDucT_NewK_ReCluster_CoreCD08@colData@listData[["ReCluster"]] <- cds_sub_AcinaDucT_NewK_ReCluster_CoreCD08@colData@listData[["assigned_cell_type"]]
colData(cds_sub_AcinaDucT_NewK_ReCluster)[colnames(cds_sub_AcinaDucT_NewK_ReCluster_CoreCD03),]$assigned_cell_type <- colData(cds_sub_AcinaDucT_NewK_ReCluster_CoreCD03)$assigned_cell_type
colData(cds_sub_AcinaDucT_NewK_ReCluster)[colnames(cds_sub_AcinaDucT_NewK_ReCluster_CoreCD08),]$assigned_cell_type <- colData(cds_sub_AcinaDucT_NewK_ReCluster_CoreCD08)$assigned_cell_type
cds_sub_AcinaDucT_NewK_ReCluster@colData@listData[["ReCluster"]] <- cds_sub_AcinaDucT_NewK_ReCluster@colData@listData[["assigned_cell_type"]]
plot_cells(cds_sub_AcinaDucT_NewK_ReCluster, color_cells_by = "ReCluster",cell_size=2, label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds_sub_AcinaDucT_NewK_ReCluster, color_cells_by = "ReCluster",cell_size=2,
label_cell_groups=TRUE, show_trajectory_graph = FALSE, group_label_size =4)
############ Find marker genes expressed by each cluster (AcinaDucT) ############
set.seed(1) # Fix the seed
marker_test_res_AcinaDucT <- top_markers(cds_sub_AcinaDucT_NewK_ReCluster,
genes_to_test_per_group = 25, group_cells_by="ReCluster")
top_specific_markers_AcinaDucT <- marker_test_res_AcinaDucT %>% filter(fraction_expressing >= 0.10) %>%
group_by(cell_group) %>% top_n(10, pseudo_R2)
top_specific_marker_ids_AcinaDucT <- unique(top_specific_markers_AcinaDucT %>% pull(gene_id))
plot_genes_by_group(cds_sub_AcinaDucT_NewK_ReCluster, top_specific_marker_ids_AcinaDucT, group_cells_by="cluster",
ordering_type="maximal_on_diag", max.size=3)
plot_genes_by_group(cds_sub_AcinaDucT_NewK_ReCluster, top_specific_marker_ids_AcinaDucT,group_cells_by="cluster",
ordering_type="cluster_row_col",max.size=3)
## Get marker genes from each cluster
top_specific_markers_AcinaDucT_SubAD <- top_specific_markers_AcinaDucT[top_specific_markers_AcinaDucT$cell_group =="AD",]
#top_specific_markers_AcinaDucT_Sub2 <- top_specific_markers_AcinaDucT[top_specific_markers_AcinaDucT$cell_group =="2",]
#...
marker_test_res_AcinaDucT_SubAD <- marker_test_res_AcinaDucT[marker_test_res_AcinaDucT$cell_group =="AD",]
#marker_test_res_AcinaDucT_Sub2 <- marker_test_res_AcinaDucT[marker_test_res_AcinaDucT$cell_group =="2",]
#...
## Export a marker genes information file
write.table(marker_test_res_AcinaDucT, file=paste0(PathName,"/",RVersion,"/",RVersion,"_",
"AcinaDucT_marker_test_res_GPG50.txt"), sep="\t", row.names=FALSE)
## Generate a Garnett file
# Require that markers have at least JS specificty score > 0.1 and be significant in the logistic test for identifying their cell type:
garnett_markers_AcinaDucT <- marker_test_res_AcinaDucT %>% filter(marker_test_q_value < 0.05 & specificity >= 0.1) %>%
group_by(cell_group) %>% top_n(100, marker_score)
# # Exclude genes that are good markers for more than one cell type:
# garnett_markers_DucT2 <- garnett_markers_DucT2 %>%
# group_by(gene_short_name) %>%
# filter(n() == 1)
generate_garnett_marker_file(garnett_markers_AcinaDucT,max_genes_per_group = 100,
file=paste0(PathName,"/",RVersion,"/",RVersion,"_","AcinaDucT_marker_Garnett_GPG50_q005spe01.txt"))
################ Plot Cell-Cycle Scoring and Regression (AcinaDucT) ################
## Convert Monocle3 Object to Seurat Object # getFilePath("Monocle3_To_Seurat.R")
marrow_sub_AcinaDucT_NewK_ReCluster <- Monocle3_To_Seurat(cds_sub_AcinaDucT_NewK_ReCluster,"sub_AcinaDucT") #sub_DT2TOP2ACTR:sub_DucT2_TOP2ACenter
###### Insert the cell cycle results from Monocle3 cds_sub into the Seurat marrow_sub ######
marrow_sub_AcinaDucT_NewK_ReCluster@active.ident <- cds_sub_AcinaDucT_NewK_ReCluster@colData@listData$cell_cycle
RidgePlot(marrow_sub_AcinaDucT_NewK_ReCluster,cols = colors_cc, features = c(Main_Group), ncol = 2)
source("FUN_Beautify_UMAP.R")
plot_cells(cds_sub_AcinaDucT_NewK_ReCluster, genes=c("TOP2A"),cell_size=2, label_cell_groups=FALSE, show_trajectory_graph = FALSE) %>%
BeautifyUMAP(LegTextSize = 12,LegPos = c(0.15, 0.15), XtextSize=15, YtextSize=15)
plot_cells(cds_sub_AcinaDucT_NewK_ReCluster, color_cells_by="cell_cycle",cell_size=2, label_cell_groups=FALSE, show_trajectory_graph = FALSE) + scale_color_manual(values = colors_cc)
plot_cells(cds, genes=c("TOP2A"),cell_size=2, label_cell_groups=FALSE, show_trajectory_graph = FALSE) %>%
BeautifyUMAP(LegTextSize = 12,LegPos = c(0.15, 0.15), XtextSize=15, YtextSize=15)
## Plot the violin diagram
Maingroup_ciliated_genes <- c(Main_Group)
cds_marrow_cc_AcinaDucT_NewK_ReCluster <- cds_sub_AcinaDucT_NewK_ReCluster[rowData(cds_sub_AcinaDucT_NewK_ReCluster)$gene_short_name %in% Maingroup_ciliated_genes,]
plot_genes_violin(cds_marrow_cc_AcinaDucT_NewK_ReCluster, group_cells_by="cell_cycle", ncol=2, log_scale = F)+
scale_fill_manual(values = colors_cc)+ylim(0, 15)
plot_genes_violin(cds_marrow_cc_AcinaDucT_NewK_ReCluster, group_cells_by="cell_cycle", ncol=2, log_scale = T)+
scale_fill_manual(values = colors_cc)+ylim(0.001, 15)
plot_genes_violin(cds_marrow_cc, group_cells_by="cell_cycle", ncol=2, log_scale = T)+ scale_fill_manual(values = colors_cc)+
geom_boxplot(width=0.1, fill="white", alpha = 0.7) + theme(axis.text.x=element_text(angle=45, hjust=1))+ylim(0.001, 15)
#################### Cell discrimination by AddModuleScore (AcinaDucT) ####################
getFilePath("Monocle3_AddModuleScore.R")
set.seed(1) # Fix the seed
Marker_PDAC_file_Name <- c("GRUETZMANN_PANCREATIC_CANCER_UP")
Marker_PDAC_Name <- c("PDAC")
cds_sub_AcinaDucT_NewK_ReCluster <- Monocle3_AddModuleScore(Marker_PDAC_file_Name,Marker_PDAC_Name,
marrow_sub_AcinaDucT_NewK_ReCluster,cds_sub_AcinaDucT_NewK_ReCluster)
plot_cells(cds_sub_AcinaDucT_NewK_ReCluster, color_cells_by= Marker_PDAC_Name, label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds_sub_AcinaDucT_NewK_ReCluster, color_cells_by= Marker_PDAC_Name, label_cell_groups=FALSE, show_trajectory_graph = FALSE,cell_size = 1.2) +
scale_colour_gradient2(low = "#440075", mid = "#ffd261", high = "#4aff8c",
guide = "colourbar",midpoint = 0.2, labs(fill = Marker_PDAC_Name))
Marker_EMT_file_Name <- c("HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION")
Marker_EMT_Name <- c("EMT")
cds_sub_AcinaDucT_NewK_ReCluster <- Monocle3_AddModuleScore(Marker_EMT_file_Name,Marker_EMT_Name,
marrow_sub_AcinaDucT_NewK_ReCluster,cds_sub_AcinaDucT_NewK_ReCluster)
plot_cells(cds_sub_AcinaDucT_NewK_ReCluster, color_cells_by= Marker_EMT_Name, label_cell_groups=FALSE, show_trajectory_graph = FALSE,cell_size = 1.2) +
scale_colour_gradient2(low = "#440075", mid = "#ffd261", high = "#4aff8c",
guide = "colourbar",midpoint = 0.2, labs(fill = Marker_EMT_Name))
Marker_ACST_file_Name <- c("HP_ABNORMALITY_OF_CHROMOSOME_STABILITY")
Marker_ACST_Name <- c("ACST")
cds_sub_AcinaDucT_NewK_ReCluster <- Monocle3_AddModuleScore(Marker_ACST_file_Name,Marker_ACST_Name,
marrow_sub_AcinaDucT_NewK_ReCluster,cds_sub_AcinaDucT_NewK_ReCluster)
plot_cells(cds_sub_AcinaDucT_NewK_ReCluster, color_cells_by= Marker_ACST_Name, label_cell_groups=FALSE, show_trajectory_graph = FALSE,cell_size = 1.2) +
scale_colour_gradient2(low = "darkblue", mid = "#f7c211", high = "green",
guide = "colourbar",midpoint = 0.15, labs(fill = Marker_ACST_Name))
Marker_Mig_file_Name <- c("GOBP_POSITIVE_REGULATION_OF_EPITHELIAL_CELL_MIGRATION")
Marker_Mig_Name <- c("Migration")
cds_sub_AcinaDucT_NewK_ReCluster <- Monocle3_AddModuleScore(Marker_Mig_file_Name,Marker_Mig_Name,
marrow_sub_AcinaDucT_NewK_ReCluster,cds_sub_AcinaDucT_NewK_ReCluster)
plot_cells(cds_sub_AcinaDucT_NewK_ReCluster, color_cells_by= Marker_Mig_Name, label_cell_groups=FALSE, show_trajectory_graph = FALSE,cell_size = 1.2) +
scale_colour_gradient2(low = "darkblue", mid = "#f7c211", high = "green",
guide = "colourbar",midpoint = 0.15, labs(fill = Marker_Mig_Name))
Marker_Meta_file_Name <- c("NAKAMURA_METASTASIS_MODEL_UP")
Marker_Meta_Name <- c("Metastasis")
cds_sub_AcinaDucT_NewK_ReCluster <- Monocle3_AddModuleScore(Marker_Meta_file_Name,Marker_Meta_Name,
marrow_sub_AcinaDucT_NewK_ReCluster,cds_sub_AcinaDucT_NewK_ReCluster)
plot_cells(cds_sub_AcinaDucT_NewK_ReCluster, color_cells_by= Marker_Meta_Name, label_cell_groups=FALSE, show_trajectory_graph = FALSE,cell_size = 1.2) +
scale_colour_gradient2(low = "darkblue", mid = "#f7c211", high = "green",
guide = "colourbar",midpoint = 0.15, labs(fill = Marker_Meta_Name))
Marker_NE_file_Name <- c("REACTOME_INITIATION_OF_NUCLEAR_ENVELOPE_NE_REFORMATION")
Marker_NE_Name <- c("NE")
cds_sub_AcinaDucT_NewK_ReCluster <- Monocle3_AddModuleScore(Marker_NE_file_Name,Marker_NE_Name,
marrow_sub_AcinaDucT_NewK_ReCluster,cds_sub_AcinaDucT_NewK_ReCluster)
plot_cells(cds_sub_AcinaDucT_NewK_ReCluster, color_cells_by= Marker_NE_Name, label_cell_groups=FALSE, show_trajectory_graph = FALSE,cell_size = 1.2) +
scale_colour_gradient2(low = "darkblue", mid = "#f7c211", high = "green",
guide = "colourbar",midpoint = 0.15, labs(fill = Marker_NE_Name))
Marker_NP_file_Name <- c("REACTOME_NUCLEAR_PORE_COMPLEX_NPC_DISASSEMBLY")
Marker_NP_Name <- c("NPC")
cds_sub_AcinaDucT_NewK_ReCluster <- Monocle3_AddModuleScore(Marker_NP_file_Name,Marker_NP_Name,
marrow_sub_AcinaDucT_NewK_ReCluster,cds_sub_AcinaDucT_NewK_ReCluster)
plot_cells(cds_sub_AcinaDucT_NewK_ReCluster, color_cells_by= Marker_NP_Name, label_cell_groups=FALSE, show_trajectory_graph = FALSE,cell_size = 1.2) +
scale_colour_gradient2(low = "darkblue", mid = "#f7c211", high = "green",
guide = "colourbar",midpoint = 0.15, labs(fill = Marker_NP_Name))
Marker_ATR_file_Name <- c("REACTOME_ACTIVATION_OF_ATR_IN_RESPONSE_TO_REPLICATION_STRESS")
Marker_ATR_Name <- c("ATR")
cds_sub_AcinaDucT_NewK_ReCluster <- Monocle3_AddModuleScore(Marker_ATR_file_Name,Marker_ATR_Name,
marrow_sub_AcinaDucT_NewK_ReCluster,cds_sub_AcinaDucT_NewK_ReCluster)
plot_cells(cds_sub_AcinaDucT_NewK_ReCluster, color_cells_by= Marker_ATR_Name, label_cell_groups=FALSE, show_trajectory_graph = FALSE,cell_size = 1.2) +
scale_colour_gradient2(low = "darkblue", mid = "#f7c211", high = "green",
guide = "colourbar",midpoint = 0.15, labs(fill = Marker_ATR_Name))
Marker_HYPOXIA_file_Name <- c("M5891_HALLMARK_HYPOXIA")
Marker_HYPOXIA_Name <- c("HYPOXIA")
cds_sub_AcinaDucT_NewK_ReCluster <- Monocle3_AddModuleScore(Marker_HYPOXIA_file_Name,Marker_HYPOXIA_Name,
marrow_sub_AcinaDucT_NewK_ReCluster,cds_sub_AcinaDucT_NewK_ReCluster)
plot_cells(cds_sub_AcinaDucT_NewK_ReCluster, color_cells_by= Marker_HYPOXIA_Name, label_cell_groups=FALSE, show_trajectory_graph = FALSE,cell_size = 1.2) +
scale_colour_gradient2(low = "darkblue", mid = "#f7c211", high = "green",
guide = "colourbar",midpoint = 0.15, labs(fill = Marker_HYPOXIA_Name))
####################### Constructing single-cell trajectories (AcinaDucT) #######################
set.seed(1)
cds_sub_AcinaDucT2 <- choose_cells(cds2)
cds_sub_AcinaDucT2 <- cluster_cells(cds_sub_AcinaDucT2)
cds_sub_AcinaDucT2 <- learn_graph(cds_sub_AcinaDucT2, use_partition = F)
plot_cells(cds_sub_AcinaDucT2,
color_cells_by = "cluster",
label_cell_groups=FALSE, label_leaves=FALSE, label_branch_points=FALSE, graph_label_size=1.5)
cds_sub_AcinaDucT2 <- order_cells(cds_sub_AcinaDucT2)
plot_cells(cds_sub_AcinaDucT2,
color_cells_by = "pseudotime",
label_cell_groups=FALSE, label_leaves=FALSE, label_branch_points=FALSE, graph_label_size=1.5)
# MainGroup_lineage_cds_sub_AcinaDucT2 <- cds_sub_AcinaDucT2[rowData(cds_sub_AcinaDucT2)$gene_short_name %in% Main_Group]
#
# plot_genes_in_pseudotime(MainGroup_lineage_cds_sub_AcinaDucT2, color_cells_by="cell_cycle",cell_size=2,
# min_expr=0.5)+ scale_color_manual(values = colors_cc)
##
cds_sub_AcinaToDucT2 <- choose_cells(cds3)
cds_sub_AcinaToDucT2 <- cluster_cells(cds_sub_AcinaToDucT2)
cds_sub_AcinaToDucT2 <- learn_graph(cds_sub_AcinaToDucT2, use_partition = F)
plot_cells(cds_sub_AcinaToDucT2,
color_cells_by = "cluster",
label_cell_groups=FALSE, label_leaves=FALSE, label_branch_points=FALSE, graph_label_size=1.5)
###### PCA for trajectories ######
for (i in c(1:8)) {
cds_sub_DucT2_TOP2ACenter_Tn <- choose_graph_segments(cds_sub_AcinaToDucT2 ,clear_cds = FALSE)
plot_cells(cds_sub_DucT2_TOP2ACenter_Tn, color_cells_by="cluster",cell_size=2,
label_cell_groups=FALSE) + scale_color_manual(values = colors_cc)
###### Convert Monocle3 Object to Seurat Object ######
# getFilePath("Monocle3_To_Seurat.R")
marrow_sub_DucT2_TOP2ACenter_Tn <- Monocle3_To_Seurat(cds_sub_DucT2_TOP2ACenter_Tn,paste0("sub_DT2TOP2ACTR_T", i)) #sub_DT2TOP2ACTR:sub_DucT2_TOP2ACenter
# ###### Insert the cell cycle results from Monocle3 cds_sub into the Seurat marrow_sub ######
# marrow_sub_DucT2_TOP2ACenter_Tn@active.ident <- cds_sub_DucT2_TOP2ACenter_Tn@colData@listData$cell_cycle
assign(paste0("marrow_sub_DucT2_TOP2ACenter_T", i),marrow_sub_DucT2_TOP2ACenter_Tn)
plot_cells(cds_sub_DucT2_TOP2ACenter_Tn, color_cells_by="cluster", label_cell_groups=FALSE) + scale_color_manual(values = colors_cc)
assign(paste0("cds_sub_DucT2_TOP2ACenter_T", i),cds_sub_DucT2_TOP2ACenter_Tn)
###### PCA Scores for finding significantly different genes at the endpoints ######
getFilePath("PCA_Threshold.R")
PCA_DT2TOP2ACTR_Tn <- assign(paste0("PCA_DT2TOP2ACTR_T", i),marrow_sub_DucT2_TOP2ACenter_Tn@reductions[["pca"]]@feature.loadings)
assign(paste0("PCA_DT2TOP2ACTR_T", i,"_PC_Sum"),PCA_Threshold_Pos(PCA_DT2TOP2ACTR_Tn, i ,PCAThreshold_Pos))
assign(paste0("PCA_DT2TOP2ACTR_T", i,"_NC_Sum"),PCA_Threshold_Neg(PCA_DT2TOP2ACTR_Tn, i ,PCAThreshold_Neg))
rm(cds_sub_DucT2_TOP2ACenter_Tn,marrow_sub_DucT2_TOP2ACenter_Tn)
}
cds_sub_DucT2_TOP2ACenter_T4@rowRanges@elementMetadata@listData$gene_short_name <- cds_sub_DucT2_TOP2ACenter_T4@assays@data@listData[["counts"]]@Dimnames[[1]]
plot_cells(cds_sub_DucT2_TOP2ACenter_T4, label_cell_groups=FALSE, show_trajectory_graph = T)
#************************************************************************************************************************#
######################## DucT2_TOP2A_Center ##########################
cds_sub_DucT2_TOP2ACenter <- choose_cells(cds_sub_AcinaDucT_NewK_ReCluster)
plot_cells(cds_sub_DucT2_TOP2ACenter, genes=c("TOP2A"),cell_size=2, label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds_sub_DucT2_TOP2ACenter, color_cells_by="cell_cycle",cell_size=2, label_cell_groups=FALSE, show_trajectory_graph = FALSE) + scale_color_manual(values = colors_cc)
###### Convert Monocle3 Object to Seurat Object ######
# getFilePath("Monocle3_To_Seurat.R")
marrow_sub_DucT2_TOP2ACenter <- Monocle3_To_Seurat(cds_sub_DucT2_TOP2ACenter,"sub_DT2TOP2ACTR") #sub_DT2TOP2ACTR:sub_DucT2_TOP2ACenter
###### Insert the cell cycle results from Monocle3 cds_sub into the Seurat marrow_sub ######
marrow_sub_DucT2_TOP2ACenter@active.ident <- cds_sub_DucT2_TOP2ACenter@colData@listData$cell_cycle
## Plot the RidgePlot
RidgePlot(marrow_sub_DucT2_TOP2ACenter,cols = colors_cc, features = c(Main), ncol = 1)
RidgePlot(marrow_sub_DucT2_TOP2ACenter,cols = colors_cc, features = c(Main_Group), ncol = 2,log=TRUE)
RidgePlot(marrow_sub_DucT2_TOP2ACenter,cols = colors_cc, features = c(Main_Group), ncol = 2,y.max = 100)
## Plot the Violin Plot
cds_sub_DT2TOP2ACTR_Maingroup <- cds_sub_DucT2_TOP2ACenter[rowData(cds_sub_DucT2_TOP2ACenter)$gene_short_name %in% Main_Group,]
plot_genes_violin(cds_sub_DT2TOP2ACTR_Maingroup, group_cells_by="cell_cycle", ncol=2, log_scale = FALSE) +
scale_fill_manual(values = colors_cc) +
theme(axis.text.x=element_text(angle=45, hjust=1))+ylim(0, 15)
plot_genes_violin(cds_sub_DT2TOP2ACTR_Maingroup, group_cells_by="cell_cycle", ncol=2, log_scale = T) +
scale_fill_manual(values = colors_cc) + theme(axis.text.x=element_text(angle=45, hjust=1))+ylim(0.001, 15)
plot_genes_violin(cds_sub_DT2TOP2ACTR_Maingroup, group_cells_by="cell_cycle", ncol=2, log_scale = T)+ scale_fill_manual(values = colors_cc)+
geom_boxplot(width=0.1, fill="white",alpha=(0.7)) + theme(axis.text.x=element_text(angle=45, hjust=1))+ylim(0.001, 15)
## Plot pseudotime
MainGroup_lineage_sub_DT2TOP2ACTR <- cds_sub_DucT2_TOP2ACenter[rowData(cds_sub_DucT2_TOP2ACenter)$gene_short_name %in% Main_Group]
plot_genes_in_pseudotime(MainGroup_lineage_sub_DT2TOP2ACTR,
color_cells_by="cell_cycle",cell_size=2,
min_expr=0.5)+ scale_color_manual(values = colors_cc)
#************************************************************************************************************************#
############ Working with 3D trajectories ############
cds_3d <- reduce_dimension(cds, max_components = 3,preprocess_method = 'PCA')
cds_3d <- cluster_cells(cds_3d)
# cds_3d <- learn_graph(cds_3d)
# # cds_3d <- order_cells(cds_3d, root_pr_nodes=get_earliest_principal_node(cds))
# # # Error in get_earliest_principal_node(cds) :
# # # ?S???o?Ө??? "get_earliest_principal_node"
# cds_3d <- order_cells(cds_3d)
#
# cds_3d_plot_obj <- plot_cells_3d(cds_3d, color_cells_by="partition")
plot_cells_3d(cds_3d)
plot_cells_3d(cds_3d, color_cells_by="cluster", show_trajectory_graph = FALSE)
plot_cells_3d(cds_3d, color_cells_by="Cell_type", show_trajectory_graph = FALSE)
plot_cells_3d(cds_3d, color_cells_by="Type", show_trajectory_graph = FALSE)
plot_cells_3d(cds_3d, color_cells_by="Patient", show_trajectory_graph = FALSE)
plot_cells_3d(cds_3d, color_cells_by="PDAC_Marker", show_trajectory_graph = FALSE)
plot_cells_3d(cds_3d, genes = Main, show_trajectory_graph = FALSE)
plot_cells_3d(cds_3d, color_cells_by="cell_cycle", show_trajectory_graph = FALSE)
cds_3d_sub_DucT2_TOP2ACenter <- reduce_dimension(cds_sub_DucT2_TOP2ACenter, max_components = 3,preprocess_method = 'PCA')
plot_cells_3d(cds_3d_sub_DucT2_TOP2ACenter, genes = Main, show_trajectory_graph = FALSE)
plot_cells_3d(cds_3d_sub_DucT2_TOP2ACenter, color_cells_by="cell_cycle", show_trajectory_graph = FALSE)
cds_3d_sub_DucT2 <- reduce_dimension(cds_sub_DucT2, max_components = 3,preprocess_method = 'PCA')
plot_cells_3d(cds_3d_sub_DucT2, genes = Main, show_trajectory_graph = FALSE)
plot_cells_3d(cds_3d_sub_DucT2, color_cells_by="cell_cycle", show_trajectory_graph = FALSE)