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5_pathway_analysis.R
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###################################################
### pathway enrichment analysis
###################################################
library(tidyverse)
library(qqman)
library(clusterProfiler)
library(ReactomePA)
library(dplyr)
library(stringr)
library(magrittr)
#outFolder <- paste0("11_pathway_enrichment/")
outFolder <- paste0("11_pathway_enrichment_with_covidcontrol/")
system(paste0("mkdir -p ",outFolder))
######################################################################################################
pathway_enrich<-function(res_gene=res3,cname_select="CAM_T-cell_M",padj_cutoff=0.1,log2FoldChange_cutoff=0)
{
cat ("=============== ",cname_select,"=============== ", "\n")
pathway_enrich_cname_dir<-paste0(outFolder,cname_select,"/")
system(paste0("mkdir -p ",pathway_enrich_cname_dir))
result<-list()
aux <- res_gene %>% filter(cname==cname_select)
genes <- filter(aux,padj<padj_cutoff,abs(log2FoldChange)>log2FoldChange_cutoff) %>% dplyr::select(ENTREZID) %>% unlist
geneUniv <- aux %>% dplyr::select(ENTREZID) %>% unlist
##geneList <- aux$log2FoldChange
geneList <- -log10(aux$pvalue)
names(geneList) <- aux$ENTREZID
geneList = sort(geneList, decreasing = TRUE)
message(".................................")
message("Number of DE genes: ",length(genes))
#print(length(genes))
message(".................................")
message("enrichGO")
ego <- enrichGO(gene=genes,universe=geneUniv, OrgDb=org.Hs.eg.db,ont="BP",minGSSize=5)
print(head(ego))
result$enrichGO<-ego
#save(ego,file=paste0(pathway_enrich_cname_dir,"ego.RData"))
#write.csv(ego,file=paste0(pathway_enrich_cname_dir,"ego.csv"))
print(".................................")
print("enrichKEGG")
ekegg <- enrichKEGG(gene=genes,universe=geneUniv,organism="hsa",minGSSize=5)
print(head(ekegg))
result$enrichKEGG<-ekegg
#save(ekegg,file=paste0(pathway_enrich_cname_dir,"ekegg.RData"))
#write.csv(ekegg,file=paste0(pathway_enrich_cname_dir,"ekegg.csv"))
message(".................................")
message("enrichPathway")
erpath <- enrichPathway(gene=genes,universe=geneUniv,minGSSize=5)
print(head(erpath))
result$enrichPathway<-erpath
#save(erpath,file=paste0(pathway_enrich_cname_dir,"erpath.RData"))
# write.csv(erpath,file=paste0(pathway_enrich_cname_dir,"erpath.csv"))
#
# message(".................................")
# message("gseGO")
# # BP: biological_process, CC: cellular_component, MF: molecular_function
# gseGO.res <- gseGO(geneList, OrgDb=org.Hs.eg.db,ont="BP",minGSSize=5)
# print(head(gseGO.res))
# result$gseGO<-gseGO.res
# #save(gseGO.res,file=paste0(pathway_enrich_cname_dir,"gseGO.res.RData"))
# #write.csv(gseGO.res,file=paste0(pathway_enrich_cname_dir,"gseGO.res.csv"))
# message(".................................")
# message("gsePathway")
# gseRPath.res <- gsePathway(geneList)
# print(head(gseRPath.res))
# result$gsePathway<-gseRPath.res
return (result)
}
# load DE genes
#res <- read_tsv("./7_outputs_DESeq_ConditionsByCluster_library/ALL.combined.2022-03-06.tsv")
res <- read_tsv("./7_outputs_DESeq_ConditionsByCluster_with_covidcontrol_res1.0_library/ALL.combined.2022-03-29.tsv")
# Adding location, cell type, and origin columns
res <- res %>% separate(cname,c("Location","Cluster","Origin"),sep="_",remove=FALSE) #Cell_type
# for now
#res$Cell_type<-res$Cluster
#cell.type.annotation<-read.delim("cell.type.annotation.txt")
cell.type.annotation<-read_tsv("cell.type.annotation.v2.tsv")
clust2Names<-cell.type.annotation$Potential.final #c("Stromal-1","Macrophage-2","Macrophage-1","Endothelial-1","Monocyte","CD4_T-cell","Decidual","CD8_T-cell","LED","Stromal-2","ILC","NK-cell","Smooth muscle cells-1","Stromal Fibroblast","Macrophage-3","Endothelial-2","DC","Smooth muscle cells-2","EVT","Plasmablast","Smooth muscle cells","Macrophage-4","B-cell","Unciliated Epithelial")
clust2Names<-paste0(cell.type.annotation$Cluster,":",clust2Names)
names(clust2Names)<-as.character(cell.type.annotation$Cluster)
res$Cell_type<-clust2Names[res$Cluster]
res$cname<-paste0(res$Cell_type,"_",res$Location,"_",res$Origin)
DEGs_CAM<-res %>% filter(padj<0.1 & Location=="CAM") %>% select(gene_name) %>% unlist() %>% unique()
DEGs_PVBP<-res %>% filter(padj<0.1 & Location=="PVBP") %>% select(gene_name) %>% unlist() %>% unique()
length(intersect(DEGs_CAM,DEGs_PVBP))
# Removing na pvalues
# Grouping pvalues based on the Location,Cell_type,and Origin
# Adding a column showing the rank of each pvalue devided by the number of pvalues in each group
#ENTREZID id
eg = bitr(res$gene_name, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
names(eg)[1]="gene_name"
head(eg)
e2g <- eg$gene_name
names(e2g) <- eg$ENTREZID
# non-na ENTREZID were included
res3 <- res %>% left_join(eg) %>% filter(!is.na(ENTREZID))
#exploratory analysis
# # counting the number of DE genes per cname
DE_per_cname<-sapply(unique(res3$cname), function(x,padj_cutoff=0.1,log2FoldChange_cutoff=0.5){
aux <- res3 %>% filter(cname==x)
genes <- filter(aux,padj<padj_cutoff,abs(log2FoldChange)>log2FoldChange_cutoff) %>% dplyr::select(ENTREZID) %>% unlist
geneUniv <- aux %>% dplyr::select(ENTREZID) %>% unlist
geneList <- -log10(aux$pvalue)
names(geneList) <- aux$ENTREZID
geneList = sort(geneList, decreasing = TRUE)
length(genes)
})
cname_selected<-names(DE_per_cname)[which(DE_per_cname>0)]
result_pathway_en_list<-lapply(cname_selected, function(x) return(pathway_enrich(res3,x)))
names(result_pathway_en_list)<-cname_selected
save(result_pathway_en_list,file=paste0(outFolder,"pathwayEnrich_result.RData"))
which(DE_per_cname>0)
##########################################################################################
##### dot plot
##########################################################################################
######################################################
# Pathway enrichment analysis (ORA)
# based on specific cell types
######################################################
#load(paste0("11_pathway_enrichment_batch_library_corrected/pathwayEnrich_result.RData"))
load(paste0(outFolder,"pathwayEnrich_result.RData"))
cname_selected<-names(result_pathway_en_list)
cname_selected_test<-as.data.frame(cname_selected)
cname_selected_test <- cname_selected_test %>% separate(cname_selected,c("Location","Cluster","Origin"),sep="_",remove=FALSE) #Cell_type
cname_selected_test$Cell_type<-clust2Names[cname_selected_test$Cluster]
cname_selected_test$cname<-paste0(cname_selected_test$Cell_type,"_",cname_selected_test$Location,"_",cname_selected_test$Origin)
names(result_pathway_en_list)<-cname_selected_test$cname
cname_selected<-cname_selected_test$cname
# DE_per_cname_select<-names(DE_per_cname)[which(DE_per_cname>=5)]
# result_pathway_en_list<-result_pathway_en_list [DE_per_cname_select]
#cname_selected<-names(result_pathway_en_list)
#gseGO
res_gseGO_list<-lapply(cname_selected, function(x)
{
rs<-result_pathway_en_list[[x]]$gseGO@result %>% filter(qvalues<=0.05)
dim1<-dim(rs)[1]
if(min(dim1,5)>0)
{
res_en<-rs
# to calculate GeneRatio=count/setSize
#count
gene_count<- res_en %>% group_by(ID) %>% summarise(count = sum(str_count(core_enrichment, "/")) + 1)
## merge with the original dataframe
dot_df<- left_join(res_en, gene_count, by = "ID") %>% mutate(GeneRatio = count/setSize)
dot_df<-dot_df[1:min(dim1,5),c("ID","Description" ,"enrichmentScore","p.adjust","GeneRatio")]
dot_df$cname<-rep(x,min(dim1,5))
dot_df
}
})
res_df_gseGO <- do.call(rbind,res_gseGO_list)
#res_df_gseGO<-res_df_gseGO[1:15,]
pdf(paste0(outFolder,"gseGO_cname_DotPlot.pdf"),width=25,height=30)
ggplot(res_df_gseGO,
aes(x = cname, y = Description)) +
geom_point(aes(size = enrichmentScore, color = p.adjust)) +
theme_bw(base_size = 14) +
#scale_colour_gradient(limits=c(0, 0.10), low="red") +
scale_color_gradient(low = "red", high = "blue", space = "Lab")+
theme(axis.text.x = element_text(angle = 45,hjust=1),text = element_text(size=30)) +
labs(size="enrichmentScore",color="p.adjust") + #x="",y="GO term" #enrichmentScore
ylab(NULL)+
xlab(NULL) +
theme_bw()+
theme(axis.text=element_text(size=30),axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),text = element_text(size=30))
dev.off()
#enrichGO
res_enrichGO_list<-lapply(cname_selected, function(x)
{
if(length(result_pathway_en_list[[x]]$enrichGO)>0)
{
rs<-result_pathway_en_list[[x]]$enrichGO@result %>% filter(qvalue<=0.05)
dim1<-dim(rs)[1]
res_en<-NULL
if(min(dim1,5)>0)
{
res_en<-rs
res_en<-res_en[1:min(dim1,5),c("ID","Description" ,"GeneRatio","p.adjust")]
res_en$cname<-rep(x,min(dim1,5))
}
res_en }
}
)
res_df_enrichGO <- do.call(rbind,res_enrichGO_list)
res_df_enrichGO$GeneRatio<-sapply(res_df_enrichGO$GeneRatio, function(x){
numden<-unlist(strsplit(x,"/"))
return (as.numeric(numden[1])/as.numeric(numden[2]))
})
res_df_enrichGO<-res_df_enrichGO %>% filter(p.adjust<0.1)
mt<-matrix(nrow=length(unique(res_df_enrichGO$Description)),ncol=length(unique(res_df_enrichGO$cname)),0)
rownames(mt)<-unique(res_df_enrichGO$Description)
colnames(mt)<-unique(res_df_enrichGO$cname)
for ( i in unique(res_df_enrichGO$Description))
{
print(i)
inx<-which(res_df_enrichGO$Description==i)
mt[i,res_df_enrichGO$cname[inx]]<-1
}
orderpathways<-rowSums(mt)
orderpathways<-orderpathways[order(orderpathways,decreasing = TRUE)]
res_df_enrichGO$orderpathways<-orderpathways[res_df_enrichGO$Description]
res_df_enrichGO <- res_df_enrichGO %>% arrange(desc(orderpathways),.by_group = TRUE) %>%ungroup()
res_df_enrichGO$cname <- factor(res_df_enrichGO$cname, levels = unique(res_df_enrichGO$cname))
#res_df_enrichGO$Description <- factor(res_df_enrichGO$Description, levels = unique(res_df_enrichGO$Description))
pdf(paste0(outFolder,"enrichGO_cname_DotPlot.pdf"),width=40,height=30)
ggplot(res_df_enrichGO, # you can replace the numbers to the row number of pathway of your interest
aes(x = cname, y = reorder(Description,orderpathways))) +
geom_point(aes(size = GeneRatio, color = p.adjust)) +
theme_bw(base_size = 14) +
scale_color_gradient(low = "red", high = "blue", space = "Lab")+
labs(size="GeneRatio",color="p.adjust") + #x="",y="GO term"
ylab(NULL)+
xlab(NULL)+
coord_fixed(ratio = 1)+
theme_bw()+
theme(axis.text=element_text(size=25),axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),text = element_text(size=25))
dev.off()
#enrichKEGG
res_enrichKEGG_list<-lapply(cname_selected, function(x)
{
if(length(result_pathway_en_list[[x]]$enrichKEGG)>0)
{
rs<-result_pathway_en_list[[x]]$enrichKEGG@result %>% filter(qvalue<=0.05)
dim1<-dim(rs)[1]
res_en<-NULL
if(min(dim1,5)>0)
{
res_en<-rs
res_en<-res_en[1:min(dim1,5),c("ID","Description" ,"GeneRatio","p.adjust")]
res_en$cname<-rep(x,min(dim1,5))
}
res_en }
}
)
res_df_enrichKEGG <- do.call(rbind,res_enrichKEGG_list)
res_df_enrichKEGG$GeneRatio<-sapply(res_df_enrichKEGG$GeneRatio, function(x){
numden<-unlist(strsplit(x,"/"))
return (as.numeric(numden[1])/as.numeric(numden[2]))
})
res_df_enrichKEGG<-res_df_enrichKEGG %>% filter(p.adjust<0.1)
mt<-matrix(nrow=length(unique(res_df_enrichKEGG$Description)),ncol=length(unique(res_df_enrichKEGG$cname)),0)
rownames(mt)<-unique(res_df_enrichKEGG$Description)
colnames(mt)<-unique(res_df_enrichKEGG$cname)
for ( i in unique(res_df_enrichKEGG$Description))
{
inx<-which(res_df_enrichKEGG$Description==i)
mt[i,res_df_enrichKEGG$cname[inx]]<-1
}
orderpathways<-rowSums(mt)
orderpathways<-orderpathways[order(orderpathways,decreasing = TRUE)]
res_df_enrichKEGG$orderpathways<-orderpathways[res_df_enrichKEGG$Description]
res_df_enrichKEGG <- res_df_enrichKEGG %>% arrange(desc(orderpathways),.by_group = TRUE) %>%ungroup()
res_df_enrichKEGG$cname <- factor(res_df_enrichKEGG$cname, levels = unique(res_df_enrichKEGG$cname))
pdf(paste0(outFolder,"enrichKEGG_cname_DotPlot.pdf"),width=20,height=25)
ggplot(res_df_enrichKEGG, # you can replace the numbers to the row number of pathway of your interest
aes(x = cname, y = reorder(Description,orderpathways))) +
geom_point(aes(size = GeneRatio, color = p.adjust)) +
scale_color_gradient(low = "red", high = "blue", space = "Lab")+
theme(axis.text.x = element_text(angle = 45,hjust=1),text = element_text(size=30)) +
labs(size="GeneRatio",color="p.adjust") + #x="",y="GO term"
ylab(NULL)+
xlab(NULL)+
#theme_black()+
theme_bw()+
theme(axis.text=element_text(size=30),axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),text = element_text(size=30))
dev.off()
#enrichPathway
res_enrichPathway_list<-lapply(cname_selected, function(x)
{
if(length(result_pathway_en_list[[x]]$enrichPathway)>0)
{
rs<-result_pathway_en_list[[x]]$enrichPathway@result %>% filter(qvalue<=0.05)
dim1<-dim(rs)[1]
res_en<-NULL
if(min(dim1,5)>0)
{
res_en<-rs
res_en<-res_en[1:min(dim1,5),c("ID","Description" ,"GeneRatio","p.adjust")]
res_en$cname<-rep(x,min(dim1,5))
}
res_en }
}
)
res_df_enrichPathway <- do.call(rbind,res_enrichPathway_list)
res_df_enrichPathway$GeneRatio<-sapply(res_df_enrichPathway$GeneRatio, function(x){
numden<-unlist(strsplit(x,"/"))
return (as.numeric(numden[1])/as.numeric(numden[2]))
})
res_df_enrichPathway<-res_df_enrichPathway %>% filter(p.adjust<0.1)
mt<-matrix(nrow=length(unique(res_df_enrichPathway$Description)),ncol=length(unique(res_df_enrichPathway$cname)),0)
rownames(mt)<-unique(res_df_enrichPathway$Description)
colnames(mt)<-unique(res_df_enrichPathway$cname)
for ( i in unique(res_df_enrichPathway$Description))
{
inx<-which(res_df_enrichPathway$Description==i)
mt[i,res_df_enrichPathway$cname[inx]]<-1
}
orderpathways<-rowSums(mt)
orderpathways<-orderpathways[order(orderpathways,decreasing = TRUE)]
res_df_enrichPathway$orderpathways<-orderpathways[res_df_enrichPathway$Description]
res_df_enrichPathway <- res_df_enrichPathway %>% arrange(desc(orderpathways),.by_group = TRUE) %>%ungroup()
res_df_enrichPathway$cname <- factor(res_df_enrichPathway$cname, levels = unique(res_df_enrichPathway$cname))
pdf(paste0(outFolder,"enrichPathway_cname_DotPlot.pdf"),width=30,height=35)
ggplot(res_df_enrichPathway, # you can replace the numbers to the row number of pathway of your interest
aes(x = cname, y = reorder(Description,orderpathways))) +
geom_point(aes(size = GeneRatio, color = p.adjust)) +
scale_color_gradient(low = "red", high = "blue", space = "Lab")+
labs(size="GeneRatio",color="p.adjust") + #x="",y="GO term"
ylab(NULL)+
xlab(NULL)+
coord_fixed(ratio = 1)+
theme_bw()+
theme(axis.text=element_text(size=30),axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),text = element_text(size=30))
dev.off()
############################################################
### Pathway enrichment analysis (ORA)
### DE genes combined
############################################################
#genes <- filter(res3,padj<0.1,abs(log2FoldChange)>0.5) %>% dplyr::select(ENTREZID) %>% unlist %>% unique
genes <- filter(res3,padj<0.1) %>% dplyr::select(ENTREZID) %>% unlist %>% unique
geneUniv <- res3 %>% dplyr::select(ENTREZID) %>% unlist %>% unique
geneList <- -log10(res3$pvalue)
names(geneList) <- res3$ENTREZID
geneList = sort(geneList, decreasing = TRUE)
message(".................................")
message("enrichGO")
ego <- enrichGO(gene=genes,universe=geneUniv, OrgDb=org.Hs.eg.db,ont="BP")
print(head(ego))
res_df_enrichGO<-ego@result %>% filter(qvalue<=0.1)
res_df_enrichGO$GeneRatio<-sapply(res_df_enrichGO$GeneRatio, function(x){
numden<-unlist(strsplit(x,"/"))
return (as.numeric(numden[1])/as.numeric(numden[2]))
})
mt<-matrix(nrow=length(unique(res_df_enrichGO$Description)),ncol=length(unique(res_df_enrichGO$GeneRatio)),0)
rownames(mt)<-unique(res_df_enrichGO$Description)
colnames(mt)<-unique(res_df_enrichGO$GeneRatio)
for ( i in unique(res_df_enrichGO$Description))
{
print(i)
inx<-which(res_df_enrichGO$Description==i)
mt[i,res_df_enrichGO$GeneRatio[inx]]<-1
}
orderpathways<-rowSums(mt)
orderpathways<-orderpathways[order(orderpathways,decreasing = TRUE)]
res_df_enrichGO$orderpathways<-orderpathways[res_df_enrichGO$Description]
res_df_enrichGO <- res_df_enrichGO %>% arrange(desc(orderpathways),.by_group = TRUE) %>%ungroup()
#res_df_enrichGO$GeneRatio <- factor(res_df_enrichGO$GeneRatio, levels = unique(res_df_enrichGO$GeneRatio))
res_df_enrichGO<-res_df_enrichGO[1:min(nrow(res_df_enrichGO),30),]
pdf(paste0(outFolder,"enrichGO_combined_DotPlot.pdf"),width=18,height=20)
ggplot(res_df_enrichGO, # you can replace the numbers to the row number of pathway of your interest
aes(x = GeneRatio, y = reorder(Description,orderpathways))) +
geom_point(aes(size = GeneRatio, color = p.adjust)) +
theme_bw(base_size = 14) +
#scale_colour_gradient(limits=c(0, 0.10), low="red") +
scale_color_gradient(low = "red", high = "blue", space = "Lab")+
theme(axis.text.x = element_text(angle = 45,hjust=1),text = element_text(size=30)) +
labs(size="GeneRatio",color="p.adjust") + #x="",y="GO term"
ylab(NULL)+
xlab(NULL)+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),text = element_text(size=30))
dev.off()
print(".................................")
print("enrichKEGG")
ekegg <- enrichKEGG(gene=genes,universe=geneUniv,organism="hsa")
print(head(ekegg))
res_df_enrichKEGG<-ekegg@result%>% filter(qvalue<=0.1)
res_df_enrichKEGG$GeneRatio<-sapply(res_df_enrichKEGG$GeneRatio, function(x){
numden<-unlist(strsplit(x,"/"))
return (as.numeric(numden[1])/as.numeric(numden[2]))
})
res_df_enrichKEGG<-res_df_enrichKEGG[1:30,]
pdf(paste0(outFolder,"enrichKEGG.combined_DotPlot.pdf"),width=20,height=15)
ggplot(res_df_enrichKEGG, # you can replace the numbers to the row number of pathway of your interest
aes(x = GeneRatio, y = Description)) +
geom_point(aes(size = GeneRatio, color = p.adjust)) +
theme_bw(base_size = 14) +
scale_color_gradient(low = "red", high = "blue", space = "Lab")+
theme(axis.text.x = element_text(angle = 45,hjust=1),text = element_text(size=10)) +
labs(size="GeneRatio",color="p.adjust") + #x="",y="GO term"
ylab(NULL)+
xlab(NULL)+
theme_bw()+
theme(axis.text=element_text(size=30), axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),text = element_text(size=30))
dev.off()
message(".................................")
message("enrichPathway")
erpath <- enrichPathway(gene=genes,universe=geneUniv)
print(head(erpath))
res_df_enrichPathway<-erpath@result%>% filter(qvalue<=0.1)
res_df_enrichPathway$GeneRatio<-sapply(res_df_enrichPathway$GeneRatio, function(x){
numden<-unlist(strsplit(x,"/"))
return (as.numeric(numden[1])/as.numeric(numden[2]))
})
res_df_enrichPathway<-res_df_enrichPathway[1:30,]
pdf(paste0(outFolder,"enrichPathway.combined_DotPlot.pdf"),width=30,height=25)
ggplot(res_df_enrichPathway, # you can replace the numbers to the row number of pathway of your interest
aes(x = GeneRatio, y = Description)) +
geom_point(aes(size = GeneRatio, color = p.adjust)) +
theme_bw(base_size = 11) +
scale_color_gradient(low = "red", high = "blue", space = "Lab")+
theme(axis.text.x = element_text(angle = 45,hjust=1),text = element_text(size=20)) +
labs(size="GeneRatio",color="p.adjust") + #x="",y="GO term"
ylab(NULL)+
xlab(NULL)+
theme_bw()+
theme(axis.text=element_text(size=30), axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),text = element_text(size=30))
dev.off()
############################################################
### Pathway enrichment analysis (ORA)
### DE genes combined - Location based
############################################################
sapply (unique(res3$Location), function(x)
{
loc<-x
#genes <- filter(res3,padj<0.1,abs(log2FoldChange)>0.5) %>% dplyr::select(ENTREZID) %>% unlist %>% unique
genes <- filter(res3,padj<0.1 & Location==loc) %>% dplyr::select(ENTREZID) %>% unlist %>% unique
geneUniv <- filter(res3,Location==loc) %>% dplyr::select(ENTREZID) %>% unlist %>% unique
geneList <- -log10(res3$pvalue)
names(geneList) <- res3$ENTREZID
geneList = sort(geneList, decreasing = TRUE)
message(".................................")
message("enrichGO")
ego <- enrichGO(gene=genes,universe=geneUniv, OrgDb=org.Hs.eg.db,ont="BP")
print(head(ego))
res_df_enrichGO<-ego@result %>% filter(qvalue<=0.1)
res_df_enrichGO$GeneRatio<-sapply(res_df_enrichGO$GeneRatio, function(x){
numden<-unlist(strsplit(x,"/"))
return (as.numeric(numden[1])/as.numeric(numden[2]))
})
mt<-matrix(nrow=length(unique(res_df_enrichGO$Description)),ncol=length(unique(res_df_enrichGO$GeneRatio)),0)
rownames(mt)<-unique(res_df_enrichGO$Description)
colnames(mt)<-unique(res_df_enrichGO$GeneRatio)
for ( i in unique(res_df_enrichGO$Description))
{
print(i)
inx<-which(res_df_enrichGO$Description==i)
mt[i,res_df_enrichGO$GeneRatio[inx]]<-1
}
orderpathways<-rowSums(mt)
orderpathways<-orderpathways[order(orderpathways,decreasing = TRUE)]
res_df_enrichGO$orderpathways<-orderpathways[res_df_enrichGO$Description]
res_df_enrichGO <- res_df_enrichGO %>% arrange(desc(orderpathways),.by_group = TRUE) %>%ungroup()
#res_df_enrichGO$GeneRatio <- factor(res_df_enrichGO$GeneRatio, levels = unique(res_df_enrichGO$GeneRatio))
res_df_enrichGO<-res_df_enrichGO %>% filter (p.adjust<0.1)
res_df_enrichGO<-res_df_enrichGO[1:min(nrow(res_df_enrichGO),30),]
pdf(paste0(outFolder,"enrichGO_combined_",loc,"_DotPlot.pdf"),width=18,height=20)
ggplot(res_df_enrichGO, # you can replace the numbers to the row number of pathway of your interest
aes(x = GeneRatio, y = reorder(Description,orderpathways))) +
geom_point(aes(size = GeneRatio, color = p.adjust)) +
theme_bw(base_size = 14) +
#scale_colour_gradient(limits=c(0, 0.10), low="red") +
scale_color_gradient(low = "red", high = "blue", space = "Lab")+
theme(axis.text.x = element_text(angle = 45,hjust=1),text = element_text(size=30)) +
labs(size="GeneRatio",color="p.adjust") + #x="",y="GO term"
ylab(NULL)+
xlab(NULL)+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),text = element_text(size=30))
dev.off()
print(".................................")
print("enrichKEGG")
ekegg <- enrichKEGG(gene=genes,universe=geneUniv,organism="hsa")
print(head(ekegg))
res_df_enrichKEGG<-ekegg@result%>% filter(qvalue<=0.1)
res_df_enrichKEGG$GeneRatio<-sapply(res_df_enrichKEGG$GeneRatio, function(x){
numden<-unlist(strsplit(x,"/"))
return (as.numeric(numden[1])/as.numeric(numden[2]))
})
res_df_enrichKEGG<-res_df_enrichKEGG[1:30,]
pdf(paste0(outFolder,"enrichKEGG.combined_",loc,"_DotPlot.pdf"),width=15,height=8)
ggplot(res_df_enrichKEGG, # you can replace the numbers to the row number of pathway of your interest
aes(x = GeneRatio, y = Description)) +
geom_point(aes(size = GeneRatio, color = p.adjust)) +
theme_bw(base_size = 14) +
scale_color_gradient(low = "red", high = "blue", space = "Lab")+
theme(axis.text.x = element_text(angle = 45,hjust=1),text = element_text(size=10)) +
labs(size="GeneRatio",color="p.adjust") + #x="",y="GO term"
ylab(NULL)+
xlab(NULL)+
theme_bw()+
theme(axis.text=element_text(size=30), axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),text = element_text(size=30))
dev.off()
message(".................................")
message("enrichPathway")
erpath <- enrichPathway(gene=genes,universe=geneUniv)
print(head(erpath))
res_df_enrichPathway<-erpath@result%>% filter(qvalue<=0.1)
res_df_enrichPathway$GeneRatio<-sapply(res_df_enrichPathway$GeneRatio, function(x){
numden<-unlist(strsplit(x,"/"))
return (as.numeric(numden[1])/as.numeric(numden[2]))
})
res_df_enrichPathway<-res_df_enrichPathway[1:30,]
pdf(paste0(outFolder,"enrichPathway.combined_",loc,"_DotPlot.pdf"),width=20,height=20)
ggplot(res_df_enrichPathway, # you can replace the numbers to the row number of pathway of your interest
aes(x = GeneRatio, y = Description)) +
geom_point(aes(size = GeneRatio, color = p.adjust)) +
theme_bw(base_size = 11) +
scale_color_gradient(low = "red", high = "blue", space = "Lab")+
theme(axis.text.x = element_text(angle = 45,hjust=1),text = element_text(size=20)) +
labs(size="GeneRatio",color="p.adjust") + #x="",y="GO term"
ylab(NULL)+
xlab(NULL)+
theme_bw()+
theme(axis.text=element_text(size=30), axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),text = element_text(size=30))
dev.off()
})
#
#
# ##################################################
# # WikiPathways
# ##################################################
#
#
# res <- read_tsv("./7_outputs_DESeq_ConditionsByCluster_bath_library/ALL.combined.2021-08-30.tsv")
# res <- res %>% separate(cname,c("Cell_type","Origin"),sep="_",remove=FALSE)
# ## cluster colors
#
# clust2Names<-c("Stromal-1","Macrophage-2","Macrophage-1","Endothelial-1","Monocyte",
# "CD4_T-cell","Decidual","CD8_T-cell","LED","Stromal-2","ILC","NK-cell","Smooth muscle cells-1","Myofibroblast",
# "Macrophage-3","Endothelial-2","DC","Smooth muscle cells-2","EVT","Plasmablast","Smooth muscle cells-3","Macrophage-4","B-cell","Unciliated Epithelial")
#
# names(clust2Names)<-as.character(c(0:23))
# cluster.Colors<-c("#DF7D99","#838EDF","#4E65A6","#FFC000","#2BA3D3","#9ABF5C","#D14357","#329B2D",
# "#D5438E","#ED4315","#76956C","#7BC791","#CA8588","#F88091","#72C6C8","#E4652C","#9B91B9","#A37584","2C3E18","#745B48",
# "#AA5485","#4E747A","#C59A89","#C9C76F")
# names(cluster.Colors)<-c("Stromal-1","Macrophage-2","Macrophage-1","Endothelial-1","Monocyte",
# "CD4_T-cell","Decidual","CD8_T-cell","LED","Stromal-2","ILC","NK-cell","Smooth muscle cells-1","Myofibroblast",
# "Macrophage-3","Endothelial-2","DC","Smooth muscle cells-2","EVT","Plasmablast","Smooth muscle cells-3","Macrophage-4","B-cell","Unciliated Epithelial")
# res$Cell_type<-clust2Names[res$Cell_type]
#
# #ENTREZID
# eg = bitr(res$gene_name, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
# names(eg)[1]="gene_name"
# head(eg)
# e2g <- eg$gene_name
# names(e2g) <- eg$ENTREZID
# res <- res %>% left_join(eg) %>% filter(!is.na(ENTREZID))
#
# geneList <- -log10(res$pvalue)
# names(geneList) <- res$ENTREZID
# geneList = sort(geneList, decreasing = TRUE)
#
#
# res$cname<-paste0(res$Cell_type,"_",res$Origin)
#
# ########################################################
# # Pathway enrichment analysis (ORA)- cell type specific
# # WikiPathways
# #qvalueCutoff = 0.05
# ########################################################
#
# pathway_enrich<-function(res_gene=res,cname_select ,padj_cutoff=0.1,log2FoldChange_cutoff=0)
# {
#
# print(cname_select)
#
# aux <- res %>% filter(cname==cname_select)
#
# if(nrow(aux)>0)
# {
# gene<-genes <- filter(aux,padj<padj_cutoff,abs(log2FoldChange)>log2FoldChange_cutoff) %>% dplyr::select(ENTREZID) %>% unlist
# geneUniv <- aux %>% dplyr::select(ENTREZID) %>% unlist
# ##geneList <- aux$log2FoldChange
# geneList <- -log10(aux$pvalue)
# names(geneList) <- aux$ENTREZID
# geneList = sort(geneList, decreasing = TRUE)
# #length(genes)
#
# wp2gene <- read.gmt("13_sample_investigation_plots/wikipathways-20191210-gmt-Homo_sapiens.gmt")
#
# wp2gene <- wp2gene %>% tidyr::separate(term, c("name","version","wpid","org"), "%")
# wpid2gene <- wp2gene %>% dplyr::select(wpid, gene) #TERM2GENE
# wpid2name <- wp2gene %>% dplyr::select(wpid, name) #TERM2NAME
#
#
# print(length(gene))
#
# if (length(gene)>0)
# {
#
# ewp <- enricher(gene, TERM2GENE = wpid2gene, TERM2NAME = wpid2name)
#
# if (!is.null(ewp))
# {
# res_en<-ewp@result
# res_en<-res_en%>%filter(p.adjust<0.1)
# dim1<-nrow(res_en)
# #print(dim1)
# if(min(dim1,15)>0)
# {
#
#
# res_en<-res_en[1:min(dim1,15),c("ID","Description" ,"GeneRatio","p.adjust","geneID")]
# res_en$Cell_type<-rep(cname_select,min(dim1,15))
# }
# return (res_en)
# }
#
# }
#
# }
#
# }
#
# res_enrichwikilist<-lapply(unique(res$cname), function(x) {pathway_enrich(cname_select=x)})
# res_df_enrichwiki <- do.call(rbind,res_enrichwikilist)
#
# res_df_enrichwiki$GeneRatio<-sapply(res_df_enrichwiki$GeneRatio, function(x){
# numden<-unlist(strsplit(x,"/"))
# return (as.numeric(numden[1])/as.numeric(numden[2]))
# })
#
#
# # WP289: Myometrial Relaxation and Contraction Pathways
# genes_WP289<-unlist(strsplit(res_df_enrichwiki$geneID[which(res_df_enrichwiki$ID=="WP289")],"/"))
#
#
# # ORA
#
# res_df<-res_df_enrichwiki
# pdf(paste0(outFolder,"enrich_wikipathways_cname_DotPlot.pdf"),width=35,height=52)
# ggplot(res_df, # you can replace the numbers to the row number of pathway of your interest
# aes(x = Cell_type, y = Description)) +
# geom_point(aes(size = GeneRatio, color = p.adjust)) +
# theme_bw(base_size = 14) +
# #scale_colour_gradient(limits=c(0, 0.10), low="red") +
# scale_color_gradient(low = "red", high = "blue", space = "Lab")+
# #theme(axis.text.x = element_text(angle = 45,hjust=1),text = element_text(size=30)) +
# labs(size="GeneRatio",color="p.adjust") + #x="",y="GO term"
# ylab(NULL)+
# xlab(NULL)+
# coord_fixed(ratio = 1)+
# #theme_black()+
# theme_bw()+
# #theme(axis.text.x = element_text(angle = 45))+
# theme(axis.text.y = element_text(hjust = 1))+
# theme(axis.text=element_text(size=30),axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),text = element_text(size=30))
#
# #ggtitle("GO pathway enrichment")
# dev.off()
#
#
# ########################################################
# # ORA -all DEGs combined
# # WikiPathways
# ########################################################
#
#
# gene<-genes <- filter(res,padj<0.1,abs(log2FoldChange)>0) %>% dplyr::select(ENTREZID) %>% unlist
# geneUniv <- res %>% dplyr::select(ENTREZID) %>% unlist
# ##geneList <- aux$log2FoldChange
# geneList <- -log10(res$pvalue)
# names(geneList) <- res$ENTREZID
# geneList = sort(geneList, decreasing = TRUE)
# #length(genes)
#
# wp2gene <- read.gmt("13_sample_investigation_plots/wikipathways-20191210-gmt-Homo_sapiens.gmt")
#
# wp2gene <- wp2gene %>% tidyr::separate(term, c("name","version","wpid","org"), "%")
# wpid2gene <- wp2gene %>% dplyr::select(wpid, gene) #TERM2GENE
# wpid2name <- wp2gene %>% dplyr::select(wpid, name) #TERM2NAME
#
# ewp <- enricher(gene, TERM2GENE = wpid2gene, TERM2NAME = wpid2name)
# res_df<-ewp@result
# res_df<-res_df%>%filter(p.adjust<0.1)
#
# res_df$GeneRatio<-sapply(res_df$GeneRatio, function(x){
# numden<-unlist(strsplit(x,"/"))
# return (as.numeric(numden[1])/as.numeric(numden[2]))
# })
#
#
# res_df<-res_df[1:15,]
#
# pdf(paste0(outFolder,"enrich_wikipathways.combined_DotPlot.pdf"),width=12,height=10)
# ggplot(res_df, # you can replace the numbers to the row number of pathway of your interest
# aes(x = GeneRatio, y = Description)) +
# geom_point(aes(size = GeneRatio, color = p.adjust)) +
# theme_bw(base_size = 11) +
# scale_color_gradient(low = "red", high = "blue", space = "Lab")+
# theme(axis.text.x = element_text(angle = 45,hjust=1),text = element_text(size=20)) +
# labs(size="GeneRatio",color="p.adjust") + #x="",y="GO term"
# ylab(NULL)+
# xlab(NULL)+
# theme_bw()+
# theme(axis.text=element_text(size=30), axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),text = element_text(size=30))
# dev.off()
#
#
# ###########################################################
# # Pathway enrichment analysis on subtypes
# ###########################################################
#
# # load DE genes
# res <- read_tsv("./7_outputs_DESeq_ConditionsByCluster_bath_library/ALL.combined.2021-08-30.tsv")
#
# # Adding location, cell type, and origin columns
# res <- res %>% separate(cname,c("Cluster","Origin"),sep="_",remove=FALSE) #Cell_type
#
#
# clust2Names<-c("Stromal-1","Macrophage-2","Macrophage-1","Endothelial-1","Monocyte","CD4_T-cell","Decidual","CD8_T-cell","LED","Stromal-2","ILC","NK-cell","Smooth muscle cells-1","Stromal Fibroblast","Macrophage-3","Endothelial-2","DC","Smooth muscle cells-2","EVT","Plasmablast","Smooth muscle cells","Macrophage-4","B-cell","Unciliated Epithelial")
# names(clust2Names)<-c(0:23)
# res$Cell_type<-clust2Names[res$Cluster]
# res$cname<-paste0(res$Cell_type,"_",res$Origin)
#
#
#
# #ENTREZID id
# eg = bitr(res$gene_name, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
# names(eg)[1]="gene_name"
# head(eg)
#
# e2g <- eg$gene_name
# names(e2g) <- eg$ENTREZID
#
# # non-na ENTREZID were included
# res <- res %>% left_join(eg) %>% filter(!is.na(ENTREZID))
#
# ######################################################################################################################
# # Pathway analysis based on shared DEGs and DEGs specific to each cell type
# # macrophage-1 and macrophage-2 and macrophage-3
# ######################################################################################################################
#
# res3<-res %>% filter(padj<0.1)
# res_intersect_macrophages<-res3 %>% filter(Cell_type %in% c("Macrophage-3","Macrophage-2","Macrophage-1"))
#
# Macrophage1<-unique(res3$gene_name[which(res3$Cell_type=="Macrophage-1")])
# Macrophage2<-unique(res3$gene_name[which(res3$Cell_type=="Macrophage-2")])
# Macrophage3<-unique(res3$gene_name[which(res3$Cell_type=="Macrophage-3")])
# x<-list(Macrophage1,Macrophage2,Macrophage3)
# library(VennDiagram)
#
# library(VennDiagram)
# venn.plot <- draw.triple.venn(
# area1 = length(Macrophage1),
# area2 = length(Macrophage2),
# area3 = length(Macrophage3),
# n12 = length(intersect(Macrophage1,Macrophage2)),
# n23 = length(intersect(Macrophage2,Macrophage3)),
# n13 = length(intersect(Macrophage1,Macrophage3)),
# n123 = length(intersect(Macrophage1,intersect(Macrophage2,Macrophage3))),
# category = c("Macrophage-1","Macrophage-2","Macrophage-3"),
# fill = c(cluster.Colors["Macrophage-1"], cluster.Colors["Macrophage-2"], cluster.Colors["Macrophage-3"]),
# scaled=FALSE)
#
# png(filename = paste0(outFolder,"venn_cluster.Macrophages.png"))
# grid.draw(venn.plot)
# dev.off()
#
#
# ###########################################################
# ###### shared DEGs (macrophage-1 and macrophage-2 and macrophage-3)
# ###########################################################
#
# genes <- filter(res_intersect_macrophages,padj<0.1) %>% dplyr::select(ENTREZID) %>% unlist %>% unique
# geneUniv <- res %>% dplyr::select(ENTREZID) %>% unlist %>% unique
#
#
# system(paste0("mkdir -p ",outFolder,"Shared_macrophages1-3/"))
#
#
# message(".................................")
# message("enrichGO")
# ego <- enrichGO(gene=genes,universe=geneUniv, OrgDb=org.Hs.eg.db,ont="BP",minGSSize = 5)
# print(head(ego))
#
#
# res_df_enrichGO<-ego@result %>% filter(qvalue<=0.1)
# res_df_enrichGO$GeneRatio<-sapply(res_df_enrichGO$GeneRatio, function(x){
# numden<-unlist(strsplit(x,"/"))
# return (as.numeric(numden[1])/as.numeric(numden[2]))
# })
#
#
# res_df_enrichGO<-res_df_enrichGO %>%filter(p.adjust<0.1)
#
# res_df_enrichGO<-res_df_enrichGO[1:50,]
# pdf(paste0(outFolder,"Shared_macrophages1-3/","enrichGO_commonDEG_DotPlot.pdf"),width=20,height=30)
# ggplot(res_df_enrichGO, # you can replace the numbers to the row number of pathway of your interest
# aes(x = GeneRatio, y = Description)) +
# geom_point(aes(size = GeneRatio, color = p.adjust)) +
# theme_bw(base_size = 14) +
# #scale_colour_gradient(limits=c(0, 0.10), low="red") +
# scale_color_gradient(low = "red", high = "blue", space = "Lab")+
# theme(axis.text.x = element_text(angle = 45,hjust=1),text = element_text(size=30)) +
# labs(size="GeneRatio",color="p.adjust") + #x="",y="GO term"
# ylab(NULL)+
# xlab(NULL)+
# theme_bw()+
# theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),text = element_text(size=30))
# dev.off()
#
#
#
#
# ###########################################################
# print(".................................")
# print("enrichKEGG")
# ekegg <- enrichKEGG(gene=genes,universe=geneUniv,organism="hsa",minGSSize = 5)
# print(head(ekegg))
#
#
# res_df_enrichKEGG<-ekegg@result%>% filter(qvalue<=0.1)
#
# res_df_enrichKEGG$GeneRatio<-sapply(res_df_enrichKEGG$GeneRatio, function(x){
# numden<-unlist(strsplit(x,"/"))
# return (as.numeric(numden[1])/as.numeric(numden[2]))
# })
#
# res_df_enrichKEGG<-res_df_enrichKEGG %>%filter(p.adjust<0.1)
# res_df_enrichKEGG<-res_df_enrichKEGG[1:50,]
# pdf(paste0(outFolder,"Shared_macrophages1-3/","enrichKEGG_commonDEG_DotPlot.pdf"),width=20,height=30)
# ggplot(res_df_enrichKEGG, # you can replace the numbers to the row number of pathway of your interest
# aes(x = GeneRatio, y = Description)) +
# geom_point(aes(size = GeneRatio, color = p.adjust)) +
# theme_bw(base_size = 14) +
# scale_color_gradient(low = "red", high = "blue", space = "Lab")+
# theme(axis.text.x = element_text(angle = 45,hjust=1),text = element_text(size=10)) +
# labs(size="GeneRatio",color="p.adjust") + #x="",y="GO term"
# ylab(NULL)+
# xlab(NULL)+
# theme_bw()+
# theme(axis.text=element_text(size=30), axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),text = element_text(size=30))
# dev.off()
#
#
#
# ###########################################################
# message(".................................")
# message("enrichPathway")
# erpath <- enrichPathway(gene=genes,universe=geneUniv,minGSSize = 5)
# print(head(erpath))
#
# res_df_enrichPathway<-erpath@result%>% filter(qvalue<=0.1)
#
# res_df_enrichPathway$GeneRatio<-sapply(res_df_enrichPathway$GeneRatio, function(x){
# numden<-unlist(strsplit(x,"/"))
# return (as.numeric(numden[1])/as.numeric(numden[2]))
# })
#
#
# res_df_enrichPathway<-res_df_enrichPathway %>%filter(p.adjust<0.1)
# res_df_enrichPathway<-res_df_enrichPathway[1:50,]