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pheatMap.R
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pheatMap.R
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#####show colors
install.packages("colourpicker")
library(colourpicker)
mypal3=c("#003399","#CC0000","#CC6633")
######The figure shows the landscape of the immune cell infiltration levels and glioma phenotypes
library(pheatmap)
Figure1_anno_colors<-list(
Histology=c(astrocytoma="#144E77",glioblastoma="#1D6CA8",oligoastrocytoma="#85AFD0",oligodendroglioma="#C8DDE9"),
Original.Subtype=c(Classical="#482E86",IDHmut="#5D4592",Mesenchymal="#7982B8",Proneural="#B4B6D6"),
CancerType=c(GBM="#6B7CBC",LGG="#206CA6"),
NMF_cluster= c(Cluster1="#003399",Cluster2="#CC0000",Cluster3="#CC6633"),
IDH.status=c(Mutant="#E1A09E",WT="#0066CC"),
MGMT.promoter.status=c(Methylated="#206CA6",Unmethylated="#E1A09E"),
TERT.promoter.status=c(Mutant="#7982B8",WT="#E79F7B"),
G.CIMP.phenotype=c(high="#83B0D4",low="#B4B6D6"),
SampleType=c(Primary="#E26F72",Recurrent="#E5A278"),
tumorgrade=c(G2="#206CA6",G3="#7982B8",G4="skyblue"),
Response=c(CR="#2597CA",
PD="#85C6E0",
PR="#7FAED4",
SD="#C9DEEA"))
Figure1_pheat<-pheatmap(scale(gbm2),
scale="row",
annotation_col =chongzhushi,
annotation_colors = Figure1_anno_colors,
clustering_distance_rows = "euclidean",
clustering_method = "complete",
cluster_cols = F,
cluster_rows = T,
fontface="bold",
fontfamily= "Arail",
fontsize =12.5,
fontsize_row =11,
width=1500,
heigth=1200,
show_colnames = F,
margin=10,
color = c(colorRampPalette(colors = c("#003399","white"))(length(bk)/2),colorRampPalette(colors = c("white","#CC0000"))(length(bk)/2)),
)
######The figure shows the overlap between NMF subtype and other glioma phenotypes
sankedata
library(ggforce)
sangji<-ggplot(sankedata, aes(x, id = id, split = y, value =intersect)) +
geom_parallel_sets(aes(fill = NMF_cluster), alpha = 0.85, axis.width = 0.1) +
geom_parallel_sets_axes(axis.width = 0.45,fill="white",color="black",size=1) +
geom_parallel_sets_labels(colour = 'black',angle = 0,size=4) +
theme_classic()+theme(legend.position = 0)+
theme_bw()+
xlab(NULL)+
scale_fill_manual(values=mypal3)+
scale_x_discrete(labels = c("glioblastoma grade","NMF Cluster","Other Subtype")) +
ggtitle("Overlap of glioblastome NMF Cluster and other Subtype Classification")+
theme(
legend.position="top",
axis.text.x=element_text(colour="black",family="Arail",size=14,face="bold"),
axis.text.y = element_blank(),
axis.title.y=element_text(family="Arail",size=16,face="bold"),
axis.title.x=element_text(family="Arail",size=16,face="bold"),
plot.title = element_text(family="Arail",size=16,face="bold",hjust = 0.5,lineheight=0.2),
panel.border = element_blank(),
axis.ticks = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
######The figure shows DNA methylation level of Cell-type-related CpG sites
library(scales)
show_col(cellyanse)
anno_cell<-list(cell=c(B=mypal3[1],CD4=mypal3[2],CD8=mypal3[3],Gran="#6495ED",Mono=cellyanse[1],Neut=cellyanse[7]),GSE=c(GSE103541=mypal3[1],GSE166844=mypal3[2],GSE118144=mypal3[3]),SiteType=c(Hyper=mypal3[1],Hypo=mypal3[3]))
bk <- c(seq(-9,-0.1,by=0.01),seq(0,9,by=0.01))
library(pheatmap)
cellsite_ht2<-pheatmap(scale(cellorderEXP),
scale="row",
annotation_col =group,
annotation_row = rowgoup,
annotation_colors = anno_cell,
clustering_distance_rows = "euclidean",
clustering_method = "complete",
legend_breaks = c(-10, -5, 0, 5,10),
cluster_cols = F,
cluster_rows = F,
fontface="bold",
fontfamily= "Arail",
main="cell specifically heterogeneous site",
show_colnames = F,
show_rownames = F,
margin=0.6,
color = c(colorRampPalette(colors = c("blue","white"))(length(bk)/2),colorRampPalette(colors = c("white","red"))(length(bk)/2)))
######The figure shows gene expression of DEG between NMF subtypes
library(gplots)
library(RColorBrewer)
plot_c1<-c(rep("#297BB8",240),rep("#FAAD60",291),rep("#EF9C92",171))
heatmap.2(as.matrix(DEGrexp),density.info=c("none"), ColSideColors=plot_c1,
dendrogram="col",rowsep = c(172,704),key.xlab = "expression-Zscore",
colsep = c(240,531),cexRow = 1.3,cexCol = 0.8,main ="DEG between NMF cluster",labCol =FALSE,key.title="expression",
margins=c(4,17),col=rev(sitecol),colge,scale="row",trace="none",breaks = seq(-0.5,0.5,0.05),Rowv = FALSE,Colv = FALSE)
######The figure shows DNA methylation level of 8 survival-related CpGct for long- and short-survival patients
require(GEOquery)
require(Biobase)
gy34_data_S<-weidian207[,match(as.character(as.matrix(pheno2074%>%filter(therapy=="34Gy",live=="SS")%>%select(sample))),colnames(weidian207))]
gy34_data_L<-weidian207[,match(as.character(as.matrix(pheno2074%>%filter(therapy=="34Gy",live=="LS")%>%select(sample))),colnames(weidian207))]
gy34_data_S<-gy34_data_S[order(apply(gy34_data_S,1,mean),decreasing = T),]
gy34_data_L<-gy34_data_L[order(apply(gy34_data_L,1,mean),decreasing = T),]
gy34_SP<-Heatmap(as.matrix(gy34_data_S),
rect_gp = gpar(col = "white", lwd = 1.2),
show_column_names = FALSE,
row_order = rownames(gy34_data_S),
row_dend_width = unit(2, "cm"),
show_heatmap_legend = FALSE,
column_dend_reorder = FALSE)
gy34_SP
gy34_LP<-Heatmap(as.matrix(gy34_data_L),
rect_gp = gpar(col = "white", lwd = 1.2),
row_names_side = "left",
show_column_names = FALSE,
row_order = rownames(gy34_data_L),
row_dend_width = unit(2, "cm"),
show_heatmap_legend = FALSE,
column_dend_reorder = FALSE)
gy34_LP
gy34_qipao=data.frame(live=c(rep("shortlive",8),rep("longlive",8)),site=c(rownames(gy34_data_S),rownames(gy34_data_L)),mean=c(
as.numeric(apply(gy34_data_S,1,mean),apply(gy34_data_L,1,mean))
))
se8<-c("#0066CC","#663399","#CC3366","#CC9933","#FF9999","#FF9966","#339933","#0099CC")
gy34_qi<-ggplot(gy34_qipao,aes(live,site))+
geom_point(data=gy34_qipao,aes(live,site,size=3,color=mean),
shape=16,show.legend=T)+
scale_color_gradient(low =se8[7] ,high =se8[3])+
labs(size="",color="average β-value")+
xlab(NULL)+
ylab(NULL)+
theme_bw()+
theme(
legend.position = "top",
axis.ticks= element_blank(),
axis.title.y=element_text(family="Arail",size=16,face="bold"),
axis.text.x=element_text(family="Arail",size=16,face="bold"),
plot.title = element_text(family="Arail",size=16,face="bold",hjust = 0.5,lineheight=0.2),
panel.border = element_blank(),
)
gy34_qi
gy60_data_S<-weidian207[,match(as.character(as.matrix(pheno2074%>%filter(therapy=="60Gy",live=="SS")%>%select(sample))),colnames(weidian207))]
gy60_data_L<-weidian207[,match(as.character(as.matrix(pheno2074%>%filter(therapy=="60Gy",live=="LS")%>%select(sample))),colnames(weidian207))]
gy60_data_S<-gy60_data_S[order(apply(gy60_data_S,1,mean),decreasing = T),]
gy60_data_L<-gy60_data_L[order(apply(gy60_data_L,1,mean),decreasing = T),]
gy60_SP<-Heatmap(as.matrix(gy60_data_S),
rect_gp = gpar(col = "white", lwd = 1.2),
show_column_names = FALSE,
row_order = rownames(gy60_data_S),
row_dend_width = unit(2, "cm"),
show_heatmap_legend = FALSE,
column_dend_reorder = FALSE)
gy60_SP
gy60_LP<-Heatmap(as.matrix(gy60_data_L),
rect_gp = gpar(col = "white", lwd = 1.2),
row_names_side = "left",
show_column_names = FALSE,
row_order = rownames(gy60_data_L),
row_dend_width = unit(2, "cm"),
show_heatmap_legend =FALSE,
column_dend_reorder = FALSE)
gy60_LP
gy60_qipao=data.frame(live=c(rep("shortlive",8),rep("longlive",8)),site=c(rownames(gy60_data_S),rownames(gy60_data_L)),mean=c(
as.numeric(apply(gy60_data_S,1,mean),apply(gy60_data_L,1,mean))
))
se8<-c("#0066CC","#663399","#CC3366","#CC9933","#FF9999","#FF9966","#339933","#0099CC")
gy60_qi<-ggplot(gy60_qipao,aes(live,site))+
geom_point(data=gy60_qipao,aes(live,site,size=3,color=mean),
shape=16,show.legend=T)+
scale_color_gradient(low =se8[7] ,high =se8[3])+
labs(size="",color="average β-value")+
xlab(NULL)+
ylab(NULL)+
theme_bw()+
theme(
legend.position = "top",
axis.ticks= element_blank(),
axis.title.y=element_text(family="Arail",size=16,face="bold"),
axis.text.x=element_text(family="Arail",size=16,face="bold"),
plot.title = element_text(family="Arail",size=16,face="bold",hjust = 0.5,lineheight=0.2),
panel.border = element_blank(),
)
gy60_qi
TCGA_noTMZSS<-yaowu1 %>% filter(treat=="no TMZ") %>% filter(fenzu=="1") #22 10
TCGA_noTMZLS<-yaowu1 %>% filter(treat=="no TMZ") %>% filter(fenzu=="2") #29 10
noTMZss<-methjisuan[match(weidian8[order(apply(methjisuan[match(weidian8,rownames(methjisuan)),match(TCGA_noTMZSS$sample,colnames(methjisuan))],1,mean),decreasing = T)],rownames(methjisuan))
,match(TCGA_noTMZSS$sample,colnames(methjisuan))]
noTMZls<-methjisuan[match(weidian8[order(apply(methjisuan[match(weidian8,rownames(methjisuan)),match(TCGA_noTMZSS$sample,colnames(methjisuan))],1,mean),decreasing = T)],rownames(methjisuan))
,match(TCGA_noTMZSS$sample,colnames(methjisuan))]
TCGA_noRTSS<-FANG %>% filter(RT=="NO") %>% filter(fenzu2=="1") #55 14
TCGA_noRTLS<-FANG %>% filter(RT=="NO") %>% filter(fenzu2=="2") #53 14
noRTss<-methjisuan[match(weidian8[order(apply(methjisuan[match(weidian8,rownames(methjisuan)),match(TCGA_noRTSS$sample,colnames(methjisuan))],1,mean),decreasing = T)],rownames(methjisuan))
,match(TCGA_noRTSS$sample,colnames(methjisuan))]
noRTls<-methjisuan[match(weidian8[order(apply(methjisuan[match(weidian8,rownames(methjisuan)),match(TCGA_noRTLS$sample,colnames(methjisuan))],1,mean),decreasing = T)],rownames(methjisuan))
,match(TCGA_noRTLS$sample,colnames(methjisuan))]
TCGAnoTMZ_SP<-Heatmap(as.matrix(noTMZss),
rect_gp = gpar(col = "white", lwd = 1.2),
show_column_names = FALSE,
row_order = rownames(noTMZss),
row_dend_width = unit(2, "cm"),
show_heatmap_legend = FALSE,
column_dend_reorder = FALSE)
TCGAnoTMZ_SP
TCGAnoTMZ_LP<-Heatmap(as.matrix(noTMZls),
rect_gp = gpar(col = "white", lwd = 1.2),
row_names_side = "left",
show_column_names = FALSE,
row_order = rownames(noTMZls),
row_dend_width = unit(2, "cm"),
show_heatmap_legend = FALSE,
column_dend_reorder = FALSE)
TCGAnoTMZ_LP
TCGAnoRT_SP<-Heatmap(as.matrix(noRTss),
rect_gp = gpar(col = "white", lwd = 1.2),
show_column_names = FALSE,
row_order = rownames(noRTss),
row_dend_width = unit(2, "cm"),
show_heatmap_legend = FALSE,
column_dend_reorder = FALSE)
TCGAnoRT_SP
TCGAnoRT_LP<-Heatmap(as.matrix(noRTls),
rect_gp = gpar(col = "white", lwd = 1.2),
row_names_side = "left",
show_column_names = FALSE,
row_order = rownames(noRTls),
row_dend_width = unit(2, "cm"),
show_heatmap_legend = FALSE,
column_dend_reorder = FALSE)
TCGAnoRT_LP
library(ggplot2)
library(reshape)
TCGAnoTMZ_qipao=data.frame(live=c(rep("shortlive",8),rep("longlive",8)),site=c(rownames(noTMZss),rownames(noTMZls)),mean=c(
as.numeric(apply(noTMZss,1,mean),apply(noTMZls,1,mean))
))
se8<-c("#0066CC","#663399","#CC3366","#CC9933","#FF9999","#FF9966","#339933","#0099CC")
TCGAnoTMZ_qi<-ggplot(TCGAnoTMZ_qipao,aes(live,site))+
geom_point(data=TCGAnoTMZ_qipao,aes(live,site,size=3,color=mean),
shape=16,show.legend=T)+
scale_color_gradient(low =se8[7] ,high =se8[3])+
labs(size="",color="average β-value")+
xlab(NULL)+
ylab(NULL)+
theme_bw()+
theme(
legend.position = "top",
axis.ticks= element_blank(),
axis.title.y=element_text(family="Arail",size=16,face="bold"),
axis.text.x=element_text(family="Arail",size=16,face="bold"),
plot.title = element_text(family="Arail",size=16,face="bold",hjust = 0.5,lineheight=0.2),
panel.border = element_blank(),
)
TCGAnoTMZ_qi
TCGAnoRT_qipao=data.frame(live=c(rep("shortlive",8),rep("longlive",8)),site=c(rownames(noRTss),rownames(noRTls)),mean=c(
as.numeric(apply(noRTss,1,mean),apply(noRTls,1,mean))
))
se8<-c("#0066CC","#663399","#CC3366","#CC9933","#FF9999","#FF9966","#339933","#0099CC")
TCGAnoRT_qi<-ggplot(TCGAnoRT_qipao,aes(live,site))+
geom_point(data=TCGAnoRT_qipao,aes(live,site,size=3,color=mean),
shape=16,show.legend=T)+
scale_color_gradient(low =se8[7] ,high =se8[3])+
labs(size="",color="average β-value")+
xlab(NULL)+
ylab(NULL)+
theme_bw()+
theme(
legend.position = "top",
axis.ticks= element_blank(),
axis.title.y=element_text(family="Arail",size=16,face="bold"),
axis.text.x=element_text(family="Arail",size=16,face="bold"),
plot.title = element_text(family="Arail",size=16,face="bold",hjust = 0.5,lineheight=0.2),
panel.border = element_blank(),
)
TCGAnoRT_qi
library(cowplot)
library(patchwork)
library(ggplotify)
plot_grid(as.ggplot(TCGAnoTMZ_SP),
as.ggplot(TCGAnoTMZ_LP),
as.ggplot(TCGAnoRT_SP),
as.ggplot(TCGAnoRT_LP),
as.ggplot(TMZ_SP),
as.ggplot(TMZ_LP),
as.ggplot(gy34_SP),
as.ggplot(gy34_LP),
as.ggplot(gy60_SP),
as.ggplot(gy60_LP),
ncol=2
)
#######The figure shows proportition of CpGct with different methylation changes
TMZdui<-data.frame(zhiliao=c(rep("no",3),rep("yes",3)),leibie=rep(c("down","incre","no"),2),value=c(2,3,3,4,2,2))
TMZduiP<-ggplot(TMZdui, aes(x = zhiliao, y = value, group = leibie)) +
geom_col(aes(fill=leibie)) +
scale_x_discrete(labels=c("no Temozolomide","Temozolomide"))+
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.25)) +
geom_text(aes(label = value), position = position_stack(vjust = .5), size = 3)+
scale_y_continuous(expand = expansion(mult = c(0,0.1)))+
#coord_flip()+
theme_bw()+
theme_classic()+theme(legend.position = 0)+
theme_bw()+
theme(
legend.title = element_blank(),
legend.position="none",
axis.ticks = element_blank(),
axis.text.y = element_blank(),
axis.text.x=element_text(colour="black",family="Arail",size=14,face="bold"),
axis.title.y=element_blank(),
axis.title.x = element_blank(),
plot.title = element_text(family="Arail",size=16,face="bold",hjust = 0.5,lineheight=0.2),
panel.border = element_blank(),
)
RTdui1<-data.frame(zhiliao=c(rep("no",3),rep("yes",3)),leibie=rep(c("down","incre","no"),2),value=c(3,2,3,4,3,1))
RTduiP1<-ggplot(RTdui1, aes(x = zhiliao, y = value, group = leibie)) +
geom_col(aes(fill=leibie)) +
scale_fill_manual(values=c("#CC9933","#CC3366","black"))+
scale_x_discrete(labels=c("no radiation","radiation 34Gy"))+
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.25)) +
geom_text(aes(label = value), position = position_stack(vjust = .5), size = 3)+
scale_y_continuous(expand = expansion(mult = c(0,0.1)))+
#coord_flip()+
theme_bw()+
theme_classic()+theme(legend.position = 0)+
theme_bw()+
theme(
legend.title = element_blank(),
legend.position="none",
axis.ticks = element_blank(),
axis.text.y = element_blank(),
axis.text.x=element_text(colour="black",family="Arail",size=14,face="bold"),
axis.title.y=element_blank(),
axis.title.x = element_blank(),
plot.title = element_text(family="Arail",size=16,face="bold",hjust = 0.5,lineheight=0.2),
panel.border = element_blank(),
)
RTdui2<-data.frame(zhiliao=c(rep("no",3),rep("yes",3)),leibie=rep(c("down","incre","no"),2),value=c(3,2,3,3,3,2))
RTduiP2<-ggplot(RTdui2, aes(x = zhiliao, y = value, group = leibie)) +
geom_col(aes(fill=leibie)) +
scale_fill_manual(values=c("#CC9933","#CC3366","black"))+
scale_x_discrete(labels=c("no radiation","radiation 34Gy"))+
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.25)) +
geom_text(aes(label = value), position = position_stack(vjust = .5), size = 3)+
scale_y_continuous(expand = expansion(mult = c(0,0.1)))+
#coord_flip()+
theme_bw()+
theme_classic()+theme(legend.position = 0)+
theme_bw()+
theme(
legend.title = element_blank(),
legend.position="none",
axis.ticks = element_blank(),
axis.text.y = element_blank(),
axis.text.x=element_text(colour="black",family="Arail",size=14,face="bold"),
axis.title.y=element_blank(),
axis.title.x = element_blank(),
plot.title = element_text(family="Arail",size=16,face="bold",hjust = 0.5,lineheight=0.2),
panel.border = element_blank(),
)
plot_grid(TMZduiP,RTduiP1,RTduiP2,ncol=3,align = "hv",rel_widths = c(1,1,1))
save(
TCGAnoTMZ_SP,TCGAnoTMZ_LP,TCGAnoRT_SP,
TCGAnoRT_LP,TMZ_SP,TMZ_LP,gy34_SP,
gy34_LP,gy60_SP,gy60_LP,TMZduiP,cellsite_ht2,DEGrexp,
RTduiP1,RTduiP2,file="pheatmap.Rdata")