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AS gene GO analysis illustration
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#### AS gene GO analysis illustration
## Tang Chao
## Tue Apr 10 2018
library(org.Hs.eg.db)##### data base target to human-geneIDs
library(AnnotationDbi)
library(DOSE)
library(GO.db)
library(topGO)
library(GSEABase)
library(clusterProfiler)
#### Background ===========================================================================================
setwd("/mnt/data5/BGI/UCB/ExpMat_NewID")
exp <- read.table("ReadCount_mat.noERCC.txt",sep = "\t", header=T,row.names=1)
exp_genesum <- rowSums(exp)
summary(exp_genesum)
summary(log10(exp_genesum+1))
pdf("/mnt/data5/BGI/UCB/tangchao/AS_stat/gene_expression_histogram.pdf")
hist(log10(exp_genesum+1), breaks=100)
abline(v = 2.5,col = 2)
dev.off()
sum(log10(exp_genesum+1)<2.5)
#[1] 4307
sum(log10(exp_genesum+1)>=2.5)
#[1] 30274
10^2.5
#[1] 316.2278
write.table(row.names(exp)[log10(exp_genesum+1)>=2.5], "/mnt/data5/BGI/UCB/tangchao/AS_stat/Background_for_GO.txt", col.names = F, row.names = F, quote = F)
#scp -P 1099 [email protected]:/mnt/data5/BGI/UCB/tangchao/AS_stat/Background_for_GO.txt /Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/
#### SE ===================================================================================================
background <- as.character(read.table("/Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/Background_for_GO.txt", header = F, stringsAsFactors = F)[,1])
load("/Users/tangchao/CloudStation/tangchao/project/UCB/all_cells_SE_type_and_PSI.RData")
load("/Users/tangchao/CloudStation/tangchao/project/UCB/all_cells_SE_GTF.RData")
rowSums(!is.na(all_cells_SE_type_and_PSI[,12:ncol(all_cells_SE_type_and_PSI)])) -> se_psi_rowsum
table(all_cells_SE_type_and_PSI[se_psi_rowsum>1,]$AStype)
all_cells_SE_type_and_PSI[se_psi_rowsum > 1 & all_cells_SE_type_and_PSI$AStype == "exon-skipping_exactly",]$loci -> se_loci
test <- as.data.frame(do.call(rbind, strsplit(SE_GTF[SE_GTF$loci %in% se_loci, ]$V9, split = "[; ]")))
valueFind <- function(x, type = "gene_id"){
for(i in 1:length(x)){
if(sum(x == type) == 0){
return(NA)
}else{
return(x[which(x == type)[1]+1])
}
}
}
gtf_infor <- data.frame(gene_id = as.vector(apply(test,1,valueFind)),
transcript_id = as.vector(apply(test,1,function(x){valueFind(x,type = "transcript_id")})),
gene_name = as.vector(apply(test,1,function(x){valueFind(x,type = "gene_name")})),
transcript_name = as.vector(apply(test,1,function(x){valueFind(x,type = "transcript_name")})),
gene_biotype = as.vector(apply(test,1,function(x){valueFind(x,type = "gene_biotype")})),
transcript_biotype = as.vector(apply(test,1,function(x){valueFind(x,type = "transcript_biotype")})), stringsAsFactors=F)
dim(gtf_infor)
gtf_infor <- gtf_infor[!duplicated(gtf_infor),]
dim(gtf_infor)
# [1] 7877 6
length(unique(gtf_infor$gene_id))
# [1] 3004
length(unique(gtf_infor$transcript_id))
# [1] 7877
table(gtf_infor$gene_biotype)
gtf_infor -> SE_gene_info
unique(as.character(gtf_infor$gene_id)) -> SE_gene_list
#### GO
SE_BP_ego <- enrichGO(gene = SE_gene_list,
universe = background,
OrgDb = org.Hs.eg.db,
keytype= 'ENSEMBL',
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.05,
qvalueCutoff = 0.05)
pdf("/Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/more than one cell/SE_GO.pdf",height = 10,width = 15)
print(dotplot(SE_BP_ego,showCategory = 10,font.size = 16,title = "BP"))
suppressMessages(plotGOgraph(SE_BP_ego,firstSigNodes = 15, useInfo = "all"))
enrichMap(SE_BP_ego,n = 30, vertex.label.font = 1,cex = 1, font.size = 1)
dev.off()
#### A3SS ===========================================================================================================================
load("/Users/tangchao/CloudStation/tangchao/project/UCB/Parsed_A3SS_PSI_GTF.RData")
dim(A3SS_type_and_PSI)
# [1] 5874 3584
rowSums(!is.na(A3SS_type_and_PSI[,11:ncol(A3SS_type_and_PSI)])) -> a3ss_psi_rowsum
sum(a3ss_psi_rowsum>1)
# [1] 3179
length(a3ss_psi_rowsum)
sum(a3ss_psi_rowsum>1)
A3SS_type_and_PSI$name[a3ss_psi_rowsum>1] -> a3ss_loci
test <- as.data.frame(do.call(rbind, strsplit(na.omit(A3SS_GTF[A3SS_GTF$name %in% a3ss_loci,]$V9), split = "[; ]")))
gtf_infor <- data.frame(gene_id = as.vector(apply(test,1,valueFind)),
transcript_id = as.vector(apply(test,1,function(x){valueFind(x,type = "transcript_id")})),
gene_name = as.vector(apply(test,1,function(x){valueFind(x,type = "gene_name")})),
transcript_name = as.vector(apply(test,1,function(x){valueFind(x,type = "transcript_name")})),
gene_biotype = as.vector(apply(test,1,function(x){valueFind(x,type = "gene_biotype")})),
transcript_biotype = as.vector(apply(test,1,function(x){valueFind(x,type = "transcript_biotype")})), stringsAsFactors=F)
gtf_infor <- gtf_infor[!duplicated(gtf_infor),]
dim(gtf_infor)
# [1] 4812 6
length(unique(gtf_infor$gene_id))
# [1] 2218
length(unique(gtf_infor$transcript_id))
# [1] 4812
unique(gtf_infor$gene_id) -> A3SS_gene_list
#### GO
A3SS_BP_ego <- enrichGO(gene = A3SS_gene_list,
universe = background,
OrgDb = org.Hs.eg.db,
keytype= 'ENSEMBL',
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.05,
qvalueCutoff = 0.05)
pdf("/Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/more than one cell/A3SS_GO.pdf",height = 10,width = 15)
print(dotplot(A3SS_BP_ego,showCategory = 10,font.size = 16,title = "BP"))
suppressMessages(plotGOgraph(A3SS_BP_ego,firstSigNodes = 15, useInfo = "all"))
enrichMap(A3SS_BP_ego,n = 30, vertex.label.font = 1,cex = 1, font.size = 1)
dev.off()
#### A5SS ===========================================================================================================================
load("/Users/tangchao/CloudStation/tangchao/project/UCB/Parsed_A5SS_PSI_GTF.RData")
dim(A5SS_type_and_PSI)
# [1] 3908 3584
rowSums(!is.na(A5SS_type_and_PSI[,11:ncol(A5SS_type_and_PSI)])) -> a5ss_psi_rowsum
sum(a5ss_psi_rowsum>1)
# [1] 2256
length(a5ss_psi_rowsum)
sum(a5ss_psi_rowsum>1)
A5SS_type_and_PSI$name[a5ss_psi_rowsum>1] -> a5ss_loci
test <- as.data.frame(do.call(rbind, strsplit(na.omit(A5SS_GTF[A5SS_GTF$name %in% a5ss_loci,]$V9), split = "[; ]")))
gtf_infor <- data.frame(gene_id = as.vector(apply(test,1,valueFind)),
transcript_id = as.vector(apply(test,1,function(x){valueFind(x,type = "transcript_id")})),
gene_name = as.vector(apply(test,1,function(x){valueFind(x,type = "gene_name")})),
transcript_name = as.vector(apply(test,1,function(x){valueFind(x,type = "transcript_name")})),
gene_biotype = as.vector(apply(test,1,function(x){valueFind(x,type = "gene_biotype")})),
transcript_biotype = as.vector(apply(test,1,function(x){valueFind(x,type = "transcript_biotype")})), stringsAsFactors=F)
gtf_infor <- gtf_infor[!duplicated(gtf_infor),]
dim(gtf_infor)
# [1] 3476 6
length(unique(gtf_infor$gene_id))
# [1] 1735
length(unique(gtf_infor$transcript_id))
# [1] 3476
unique(gtf_infor$gene_id) -> A5SS_gene_list
#### GO
A5SS_BP_ego <- enrichGO(gene = A5SS_gene_list,
universe = background,
OrgDb = org.Hs.eg.db,
keytype= 'ENSEMBL',
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.05,
qvalueCutoff = 0.05)
pdf("/Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/more than one cell/A5SS_GO.pdf",height = 10,width = 15)
print(dotplot(A5SS_BP_ego,showCategory = 10,font.size = 16,title = "BP"))
suppressMessages(plotGOgraph(A5SS_BP_ego,firstSigNodes = 5, useInfo = "all"))
enrichMap(A5SS_BP_ego,n = 30, vertex.label.font = 1,cex = 1, font.size = 1)
dev.off()
#### AFE ===========================================================================================================================
load("/Users/tangchao/CloudStation/tangchao/project/UCB/all_cells_AFE_PSI_GTF.RData")
dim(all_cells_AFE_PSI)
# [1] 2289 3584
rowSums(!is.na(all_cells_AFE_PSI[,11:ncol(all_cells_AFE_PSI)])) -> afe_psi_rowsum
sum(afe_psi_rowsum>1)
# [1] 1361
all_cells_AFE_PSI$name[afe_psi_rowsum>1] -> afe_loci
length(afe_loci)
# [1] 1361
test <- as.data.frame(do.call(rbind, strsplit(na.omit(AFE_GTF[AFE_GTF$name %in% afe_loci,]$V9), split = "[; ]")))
gtf_infor <- data.frame(gene_id = as.vector(apply(test,1,valueFind)),
transcript_id = as.vector(apply(test,1,function(x){valueFind(x,type = "transcript_id")})),
gene_name = as.vector(apply(test,1,function(x){valueFind(x,type = "gene_name")})),
transcript_name = as.vector(apply(test,1,function(x){valueFind(x,type = "transcript_name")})),
gene_biotype = as.vector(apply(test,1,function(x){valueFind(x,type = "gene_biotype")})),
transcript_biotype = as.vector(apply(test,1,function(x){valueFind(x,type = "transcript_biotype")})), stringsAsFactors=F)
gtf_infor <- gtf_infor[!duplicated(gtf_infor),]
dim(gtf_infor)
# [1] 1649 6
length(unique(gtf_infor$gene_id))
# [1] 1204
length(unique(gtf_infor$transcript_id))
# [1] 1649
unique(gtf_infor$gene_id) -> AFE_gene_list
#### GO
AFE_BP_ego <- enrichGO(gene = AFE_gene_list,
universe = background,
OrgDb = org.Hs.eg.db,
keytype= 'ENSEMBL',
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.05,
qvalueCutoff = 0.05)
pdf("/Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/more than one cell/AFE_GO.pdf",height = 10,width = 15)
print(dotplot(AFE_BP_ego,showCategory = 10,font.size = 16,title = "BP"))
suppressMessages(plotGOgraph(AFE_BP_ego,firstSigNodes = 15, useInfo = "all"))
enrichMap(AFE_BP_ego,n = 30, vertex.label.font = 1,cex = .1, font.size = .1)
dev.off()
#### ALE ===========================================================================================================================
load("/Users/tangchao/CloudStation/tangchao/project/UCB/all_cells_ALE_PSI_GTF.RData")
dim(all_cells_ALE_PSI)
# [1] 1211 3584
rowSums(!is.na(all_cells_ALE_PSI[,11:ncol(all_cells_ALE_PSI)])) -> ale_psi_rowsum
sum(ale_psi_rowsum>1)
# [1] 624
all_cells_ALE_PSI$name[ale_psi_rowsum>1] -> ale_loci
length(ale_loci)
# [1] 624
test <- as.data.frame(do.call(rbind, strsplit(na.omit(ALE_GTF[ALE_GTF$name %in% ale_loci,]$V9), split = "[; ]")))
gtf_infor <- data.frame(gene_id = as.vector(apply(test,1,valueFind)),
transcript_id = as.vector(apply(test,1,function(x){valueFind(x,type = "transcript_id")})),
gene_name = as.vector(apply(test,1,function(x){valueFind(x,type = "gene_name")})),
transcript_name = as.vector(apply(test,1,function(x){valueFind(x,type = "transcript_name")})),
gene_biotype = as.vector(apply(test,1,function(x){valueFind(x,type = "gene_biotype")})),
transcript_biotype = as.vector(apply(test,1,function(x){valueFind(x,type = "transcript_biotype")})), stringsAsFactors=F)
gtf_infor <- gtf_infor[!duplicated(gtf_infor),]
dim(gtf_infor)
# [1] 857 6
length(unique(gtf_infor$gene_id))
# [1] 566
length(unique(gtf_infor$transcript_id))
# [1] 857
unique(gtf_infor$gene_id) -> ALE_gene_list
#### GO
ALE_BP_ego <- enrichGO(gene = ALE_gene_list,
universe = background,
OrgDb = org.Hs.eg.db,
keytype= 'ENSEMBL',
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.05,
qvalueCutoff = 0.05)
pdf("/Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/more than one cell/ALE_GO.pdf", height = 10,width = 15)
print(dotplot(ALE_BP_ego,showCategory = 10,font.size = 16,title = "BP"))
suppressMessages(plotGOgraph(ALE_BP_ego,firstSigNodes = 15, useInfo = "all"))
enrichMap(ALE_BP_ego,n = 30, vertex.label.font = 1, cex = .1, font.size = .1)
dev.off()
#### circro plot ===========================================================================================================
library(circlize)
library(RColorBrewer)
SE_BP_ego@result -> SE
A3SS_BP_ego@result -> A3SS
A5SS_BP_ego@result -> A5SS
AFE_BP_ego@result -> AFE
ALE_BP_ego@result -> ALE
lapply(list(SE,A3SS,A5SS,AFE,ALE), dim)
lapply(list(SE_gene_list,A3SS_gene_list,A5SS_gene_list,AFE_gene_list,ALE_gene_list), length)
Reduce(union,list(SE_gene_list,A3SS_gene_list,A5SS_gene_list,AFE_gene_list,ALE_gene_list))
length(unique(c(SE$ID, A3SS$ID, A5SS$ID, AFE$ID, ALE$ID)))
mat <- matrix(0, nrow = 5, ncol = length(unique(c(SE$ID, A3SS$ID, A5SS$ID, AFE$ID, ALE$ID))))
row.names(mat) <- c("SE","A3SS","A5SS","AFE","ALE")
colnames(mat) <- unique(c(SE$ID, A3SS$ID, A5SS$ID, AFE$ID, ALE$ID))
for(i in 1:nrow(SE)){
mat[1, SE[i,"ID"]] <- SE[i,"Count"]
}
for(i in 1:nrow(A3SS)){
mat[2, A3SS[i,"ID"]] <- A3SS[i,"Count"]
}
for(i in 1:nrow(A5SS)){
mat[3, A5SS[i,"ID"]] <- A5SS[i,"Count"]
}
for(i in 1:nrow(AFE)){
mat[4, AFE[i,"ID"]] <- AFE[i,"Count"]
}
for(i in 1:nrow(ALE)){
mat[5, ALE[i,"ID"]] <- ALE[i,"Count"]
}
circos.par("canvas.xlim" = c(-2, 2), "canvas.ylim" = c(-2, 2), start.degree = 270, gap.after = c(rep(2, nrow(mat)-1), 10, rep(0, ncol(mat)-1), 10))# 块儿与块儿之间的空格
circos.par(start.degree = 270, gap.after = c(rep(2, nrow(mat)-1), 10, rep(0, ncol(mat)-1), 10))# 块儿与块儿之间的空格
chordDiagram(mat, annotationTrack = "grid", transparency = 0.5,
grid.col = c("#FF0000","#00FF00","#0000FF","#FFFF00","#00FFFF",colorRampPalette(brewer.pal(7, "Paired"))(ncol(mat))),
row.col = c("#FF0000","#00FF00","#0000FF","#FFFF00","#00FFFF"),
preAllocateTracks = list(track.height = 0.1))
circos.trackPlotRegion(track.index = 1, panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
if(sector.name %in% get.all.sector.index()[1:5]){
circos.text(mean(xlim), 0.3*nchar(sector.name), sector.name, niceFacing = T, facing = "clockwise")
}
}, bg.border = NA)
circos.clear()
library(pheatmap)
pdf("/Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/more than one cell/AS_GO_Count_Heatmap.pdf")
pheatmap(mat, show_colnames = F)
dev.off()
pheatmap(mat, silent = T) -> cluster_tab
mat_sort <- mat[,colnames(mat)[cluster_tab$tree_col$order]]
pdf("/Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/more than one cell/AS_GO_Count_circular.pdf")
circos.par(start.degree = 270, gap.after = c(rep(2, nrow(mat)-1), 10, rep(0, ncol(mat)-1), 10))# 块儿与块儿之间的空格
chordDiagram(mat_sort, annotationTrack = "grid", transparency = 0.5,
grid.col = c("#FF0000","#00FF00","#0000FF","#FFFF00","#00FFFF",colorRampPalette(brewer.pal(7, "Paired"))(ncol(mat))),
row.col = c("#FF0000","#00FF00","#0000FF","#FFFF00","#00FFFF"),
preAllocateTracks = list(track.height = 0.1))
circos.trackPlotRegion(track.index = 1, panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
if(sector.name %in% get.all.sector.index()[1:5]){
circos.text(mean(xlim), 0.3*nchar(sector.name), sector.name, niceFacing = T, facing = "clockwise")
}
}, bg.border = NA)
circos.clear()
dev.off()
####
library(ggsci)
library(scales)
rev(colorRampPalette(pal_material("deep-purple")(10))(max(mat_sort)))
colorRampPalette(pal_material("deep-purple")(10))(max(mat_sort))
show_col(colorRampPalette(pal_material("deep-purple")(10))(max(mat_sort)))
colorRampPalette(c("#FFFFFF","#FF0000"))(100)
show_col(rev(colorRampPalette(c("#FFFFFF","#FF0000"))(max(mat_sort))))
rev(colorRampPalette(pal_material("deep-purple")(10))(max(mat_sort)))
rev(colorRampPalette(c("#FFFFFF","#FF0000"))(max(mat_sort)))
pdf("/Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/more than one cell/AS_GO_circos.pdf")
circos.par(start.degree = 270, gap.after = c(rep(2, nrow(mat)-1), 10, rep(0, ncol(mat)-1), 10))# 块儿与块儿之间的空格
chordDiagram(mat_sort, annotationTrack = "grid", transparency = 0.5,
grid.col = c("#FF0000","#00FF00","#0000FF","#FFFF00","#00FFFF",rep("#FFFFFF", ncol(mat_sort))),
row.col = c("#FF0000","#00FF00","#0000FF","#FFFF00","#00FFFF"),
preAllocateTracks = list(track.height = 0.1))
circos.trackPlotRegion(track.index = 1, panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
if(sector.name %in% get.all.sector.index()[1:5]){
circos.text(mean(xlim), 0.3*nchar(sector.name), sector.name, niceFacing = T, facing = "clockwise")
}
}, bg.border = NA)
circos.track(track.index = 2, panel.fun = function(x, y) {
sector.name = get.cell.meta.data("sector.index")
if(sector.name %in% colnames(mat_sort)) {
circos.rect(get.cell.meta.data("xlim")[1],
get.cell.meta.data("ylim")[1],
get.cell.meta.data("xlim")[2],
get.cell.meta.data("ylim")[2],
col = rev(colorRampPalette(c("#FFFFFF","#FF0000"))(max(mat_sort)))[as.numeric(mat_sort[1,sector.name])],
border = NA)
}
}, bg.border = NA)
circos.track(track.index = 2, panel.fun = function(x, y) {
sector.name = get.cell.meta.data("sector.index")
if(sector.name %in% colnames(mat_sort)) {
circos.rect(get.cell.meta.data("xlim")[1],
get.cell.meta.data("ylim")[2],
get.cell.meta.data("xlim")[2],
get.cell.meta.data("ylim")[2]*2,
col = rev(colorRampPalette(c("#FFFFFF","#00FF00"))(max(mat_sort)))[as.numeric(mat_sort[2,sector.name])],
border = NA)
}
}, bg.border = NA)
circos.track(track.index = 2, panel.fun = function(x, y) {
sector.name = get.cell.meta.data("sector.index")
if(sector.name %in% colnames(mat_sort)) {
circos.rect(get.cell.meta.data("xlim")[1],
get.cell.meta.data("ylim")[2]*2,
get.cell.meta.data("xlim")[2],
get.cell.meta.data("ylim")[2]*3,
col = rev(colorRampPalette(c("#FFFFFF","#0000FF"))(max(mat_sort)))[as.numeric(mat_sort[3,sector.name])],
border = NA)
}
}, bg.border = NA)
circos.track(track.index = 2, panel.fun = function(x, y) {
sector.name = get.cell.meta.data("sector.index")
if(sector.name %in% colnames(mat_sort)) {
circos.rect(get.cell.meta.data("xlim")[1],
get.cell.meta.data("ylim")[2]*3,
get.cell.meta.data("xlim")[2],
get.cell.meta.data("ylim")[2]*4,
col = rev(colorRampPalette(c("#FFFFFF","#FFFF00"))(max(mat_sort)))[as.numeric(mat_sort[4,sector.name])],
border = NA)
}
}, bg.border = NA)
circos.track(track.index = 2, panel.fun = function(x, y) {
sector.name = get.cell.meta.data("sector.index")
if(sector.name %in% colnames(mat_sort)) {
circos.rect(get.cell.meta.data("xlim")[1],
get.cell.meta.data("ylim")[2]*4,
get.cell.meta.data("xlim")[2],
get.cell.meta.data("ylim")[2]*5,
col = rev(colorRampPalette(c("#FFFFFF","#00FFFF"))(max(mat_sort)))[as.numeric(mat_sort[5,sector.name])],
border = NA)
}
}, bg.border = NA)
circos.clear()
dev.off()
#### heatmap of adj.pvalue ============================================================================================================
matp <- matrix(0, nrow = 5, ncol = length(unique(c(SE$ID, A3SS$ID, A5SS$ID, AFE$ID, ALE$ID))))
row.names(matp) <- c("SE","A3SS","A5SS","AFE","ALE")
colnames(matp) <- unique(c(SE$ID, A3SS$ID, A5SS$ID, AFE$ID, ALE$ID))
for(i in 1:nrow(SE)){
matp[1, SE[i,"ID"]] <- SE[i,"p.adjust"]
}
for(i in 1:nrow(A3SS)){
matp[2, A3SS[i,"ID"]] <- A3SS[i,"p.adjust"]
}
for(i in 1:nrow(A5SS)){
matp[3, A5SS[i,"ID"]] <- A5SS[i,"p.adjust"]
}
for(i in 1:nrow(AFE)){
matp[4, AFE[i,"ID"]] <- AFE[i,"p.adjust"]
}
for(i in 1:nrow(ALE)){
matp[5, ALE[i,"ID"]] <- ALE[i,"p.adjust"]
}
-log10(matp) -> logmatp
logmatp[is.infinite(logmatp)] <- -1
pdf("/Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/more than one cell/AS_GO_p.adjust_Heatmap.pdf")
pheatmap(logmatp, show_colnames = F)
dev.off()
pheatmap(logmatp, show_colnames = F, cutree_cols = 2)
test2 <- pheatmap(logmatp, silent = T)
cutree(tree = test2$tree_col, 2)
table(cutree(tree = test2$tree_col, 2))
cluster1_GO <- names(cutree(tree = test2$tree_col, 2)[cutree(tree = test2$tree_col, 2)==1])
sapply(cluster1_GO, function(x) Term(GOTERM[[x]]))
cluster2_GO <- names(cutree(tree = test2$tree_col, 2)[cutree(tree = test2$tree_col, 2)==2])
sapply(cluster2_GO, function(x) Term(GOTERM[[x]]))
Term(GOTERM[["GO:0006413"]])
GOBPANCESTOR[["GO:0006413"]]
tang <- do.call(rbind,lapply(as.list(cluster1_GO), function(x) {select(GO.db, keys = GOBPANCESTOR[[x]], columns = "TERM")}))
table(tang$TERM)[order(table(tang$TERM))]
tail(table(tang$TERM)[order(table(tang$TERM))],11)
tang2 <- lapply(as.list(cluster2_GO), function(x) {select(GO.db, keys = GOBPANCESTOR[[x]], columns = "TERM")})
tang2 <- do.call(rbind,tang2)
table(tang2$TERM)[order(table(tang2$TERM))]
tail(table(tang2$TERM)[order(table(tang2$TERM))],11)
names(tail(table(tang$TERM)[order(table(tang$TERM))],11)) -> n1
names(tail(table(tang2$TERM)[order(table(tang2$TERM))],11)) -> n2
n1[!n1 %in% Reduce(intersect, list(n1,n2))]
n2[!n2 %in% Reduce(intersect, list(n1,n2))]
#### GO slim at high level =============================================================================================
myGOslim <- function(x){
requireNamespace("topGO") || stop("package topGO is required")
groupGOTerms <- rvcheck::get_fun_from_pkg("topGO", "groupGOTerms")
annFUN.gene2GO <- rvcheck::get_fun_from_pkg("topGO", "annFUN.gene2GO")
if (!class(x) %in% c("gseaResult", "enrichResult")) {
stop("x should be output of gseGO or enrichGO...")
}
gene2GO <- inverseList(x@geneSets)
if (is(x, "gseaResult")) {
ont <- x@setType
allgenes <- x@geneList
core_genes <- unique(unlist(geneInCategory(x)))
allgenes[!names(allgenes) %in% core_genes] <- -1
allgenes[core_genes] <- 1
}
else {
ont <- x@ontology
universe <- x@universe
allgenes <- numeric(length(universe))
names(allgenes) <- universe
allgenes[x@gene] <- 1
}
selector <- function(scores) return(scores == 1)
if (!ont %in% c("BP", "MF", "CC")) {
stop("ontology should be one of 'BP', 'MF' or 'CC'...")
}
pvalue <- x@result$p.adjust
names(pvalue) <- x@result$ID
groupGOTerms()
GOdata <- new("topGOdata", description = "clusterProfiler enrichment results",
ontology = ont,
allGenes = allgenes,
geneSel = selector,
annot = annFUN.gene2GO,
gene2GO = gene2GO)
result <- buildLevels(graph(GOdata))
return(result)
}
SE_DAG <- myGOslim(SE_BP_ego)
A3SS_DAG <- myGOslim(A3SS_BP_ego)
A5SS_DAG <- myGOslim(A5SS_BP_ego)
AFE_DAG <- myGOslim(AFE_BP_ego)
ALE_DAG <- myGOslim(ALE_BP_ego)
getNoOfLevels(SE_DAG)
getNoOfLevels(A3SS_DAG)
getNoOfLevels(A5SS_DAG)
getNoOfLevels(AFE_DAG)
getNoOfLevels(ALE_DAG)
SE_DAG$level2nodes$`5`
A3SS_DAG$level2nodes$`5`
A5SS_DAG$level2nodes$`5`
AFE_DAG$level2nodes$`5`
ALE_DAG$level2nodes$`5`
lapply(FUN = length, list(SE_DAG$level2nodes$`2`,
A3SS_DAG$level2nodes$`2`,
A5SS_DAG$level2nodes$`2`,
AFE_DAG$level2nodes$`2`,
ALE_DAG$level2nodes$`2`))
length(Reduce(intersect, list(SE_DAG$level2nodes$`2`,
A3SS_DAG$level2nodes$`2`,
A5SS_DAG$level2nodes$`2`,
AFE_DAG$level2nodes$`2`,
ALE_DAG$level2nodes$`2`)))
select(GO.db, keys = SE_DAG$level2nodes$`2`, columns = "TERM")
SE$Description[1]
Term(GOTERM[[SE$ID[1]]])
select(GO.db, keys = GOBPANCESTOR[[SE$ID[1]]][GOBPANCESTOR[[SE$ID[1]]] %in% SE_DAG$level2nodes$`3`], columns = "TERM")
select(GO.db, keys = GOBPANCESTOR[[SE$ID[2]]][GOBPANCESTOR[[SE$ID[2]]] %in% SE_DAG$level2nodes$`3`], columns = "TERM")
select(GO.db, keys = GOBPANCESTOR[[SE$ID[12]]][GOBPANCESTOR[[SE$ID[12]]] %in% SE_DAG$level2nodes$`3`], columns = "TERM")
#### enrichment at level 3
SE[SE$ID %in% SE_DAG$level2nodes$`3`,]$Description
# [1] "multi-organism metabolic process" "antigen processing and presentation" "protein folding"
# [4] "methylation"
A3SS[A3SS$ID %in% A3SS_DAG$level2nodes$`3`,]$Description
# [1] "multi-organism metabolic process" "antigen processing and presentation"
A5SS[A5SS$ID %in% A5SS_DAG$level2nodes$`3`,]$Description
# [1] "multi-organism metabolic process" "antigen processing and presentation" "multi-organism localization"
AFE[AFE$ID %in% AFE_DAG$level2nodes$`3`,]$Description
# [1] "multi-organism metabolic process" "protein folding"
ALE[ALE$ID %in% ALE_DAG$level2nodes$`3`,]$Description
# [1] "multi-organism metabolic process" "antigen processing and presentation"
SE_L3 <-SE[SE$ID %in% SE_DAG$level2nodes$`3`,]
A3SS_L3 <- A3SS[A3SS$ID %in% A3SS_DAG$level2nodes$`3`,]
A5SS_L3 <- A5SS[A5SS$ID %in% A5SS_DAG$level2nodes$`3`,]
AFE_L3 <- AFE[AFE$ID %in% AFE_DAG$level2nodes$`3`,]
ALE_L3 <- ALE[ALE$ID %in% ALE_DAG$level2nodes$`3`,]
length(unique(c(SE_L3$ID, A3SS_L3$ID, A5SS_L3$ID, AFE_L3$ID, ALE_L3$ID)))
mat_l3 <- matrix(0, nrow = 5, ncol = length(unique(c(SE_L3$ID, A3SS_L3$ID, A5SS_L3$ID, AFE_L3$ID, ALE_L3$ID))))
row.names(mat_l3) <- c("SE","A3SS","A5SS","AFE","ALE")
colnames(mat_l3) <- unique(c(SE_L3$Description, A3SS_L3$Description, A5SS_L3$Description, AFE_L3$Description, ALE_L3$Description))
for(i in 1:nrow(SE_L3)){
mat_l3[1, SE_L3[i,"Description"]] <- SE_L3[i,"Count"]
}
for(i in 1:nrow(A3SS_L3)){
mat_l3[2, A3SS_L3[i,"Description"]] <- A3SS_L3[i,"Count"]
}
for(i in 1:nrow(A5SS_L3)){
mat_l3[3, A5SS_L3[i,"Description"]] <- A5SS_L3[i,"Count"]
}
for(i in 1:nrow(AFE_L3)){
mat_l3[4, AFE_L3[i,"Description"]] <- AFE_L3[i,"Count"]
}
for(i in 1:nrow(ALE_L3)){
mat_l3[5, ALE_L3[i,"Description"]] <- ALE_L3[i,"Count"]
}
pdf("/Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/more than one cell/AS_GO_Count_circos_at_levels3.pdf")
#circos.par("canvas.xlim" = c(-2, 2), "canvas.ylim" = c(-2, 2), start.degree = 230, gap.after = c(rep(2, nrow(mat_l3)-1), 10, rep(2, ncol(mat_l3)-1), 10))#
circos.par(start.degree = 240, gap.after = c(rep(2, nrow(mat_l3)-1), 60, rep(2, ncol(mat_l3)-1), 60))# 块儿与块儿之间的空格
chordDiagram(mat_l3, annotationTrack = "grid", transparency = 0.5,
grid.col = c("#FF0000","#00FF00","#0000FF","#FFFF00","#00FFFF",colorRampPalette(brewer.pal(7, "Paired"))(ncol(mat_l3))),
row.col = c("#FF0000","#00FF00","#0000FF","#FFFF00","#00FFFF"),
preAllocateTracks = list(track.height = 0.1))
circos.trackPlotRegion(track.index = 1, panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
if(sector.name %in% get.all.sector.index()[1:5]){
circos.text(mean(xlim), 0.3*nchar(sector.name), sector.name, niceFacing = T, facing = "clockwise")
}
}, bg.border = NA)
circos.clear()
library(ComplexHeatmap)
lgd_points = Legend(at = colnames(mat_l3), type = "points",
legend_gp = gpar(col = colorRampPalette(brewer.pal(7, "Paired"))(ncol(mat_l3))),
background = colorRampPalette(brewer.pal(7, "Paired"))(ncol(mat_l3)),
title_position = "topleft",
title = "GO Term",
nrow = 5)
lgd_list_vertical = packLegend(lgd_points, direction = "vertical")
pushViewport(viewport(x = unit(90, "mm"), y = unit(20, "mm"),
width = grobWidth(lgd_list_vertical),
height = grobHeight(lgd_list_vertical)))
grid.draw(lgd_list_vertical)
upViewport()
dev.off()
write.table(SE_gene_list, "/Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/more than one cell/SE_gene_list.txt", col.names = F, row.names = F, quote = F)
write.table(A3SS_gene_list, "/Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/more than one cell/A3SS_gene_list.txt", col.names = F, row.names = F, quote = F)
write.table(A5SS_gene_list, "/Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/more than one cell/A5SS_gene_list.txt", col.names = F, row.names = F, quote = F)
write.table(AFE_gene_list, "/Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/more than one cell/AFE_gene_list.txt", col.names = F, row.names = F, quote = F)
write.table(ALE_gene_list, "/Users/tangchao/CloudStation/tangchao/project/UCB/AS_GO/more than one cell/ALE_gene_list.txt", col.names = F, row.names = F, quote = F)