GTEx（Genotype-Tissue Expression，基因型-组织表达）数据库，研究从来自449名生前健康的人类捐献者的7000多份尸检样本，涵盖44个组织（42种不同的组织类型），包括31个实体器官组织、10个闹分区、全血、2个来自捐献者血液和皮肤的细胞系，作者利用这些样本研究基因表达在不同组织和个体中有何差异。

GTEx对几乎所有转录基因的基因表达模式进行了观察，从而能够确定基因组中影响基因表达的特定区域。

此外，合并GTEx与TCGA数据库数据能够有效解决TCGA数据库中正常组织样本量不足的缺陷，从而提高比较的准确性。

1. 数据来源

• tcga_RSEM_gene_tpm.gz 
• TCGA_phenotype_denseDataOnlyDownload.tsv.gz
• gtex_RSEM_gene_tpm.gz 
• GTEX_phenotpye.gz 
• gencode.v23.annotation.gene.probemap
• samplepair.txt(TCGA和GTeX sample信息)

（数据比较大，如果下载困难可以留言）

2. 注释来自 TCGA 和 GTEx 的样本

library(stringr)
library(dplyr)
library(ggplot2)
library(RColorBrewer)
library(data.table)


#################======= step1: clean GTEx pheno data   =======#################
gtex <- read.table("samplepair.txt",header=T,sep='\t')

tcga_ref <- gtex[,1:2]

gtex$type <- paste0(gtex$TCGA,"_normal_GTEx")
gtex$sample_type <-"normal"
gtex <- gtex[,c("TCGA","GTEx","type","sample_type")]
names(gtex)[1:2] <- c("tissue","X_primary_site")


gp <- read.delim(file="GTEX_phenotype.gz",header=T,as.is = T)
gtex2tcga <- merge(gtex,gp,by="X_primary_site")
gtex_data <- gtex2tcga[,c(5,2:4)]
names(gtex_data)[1] <- "sample"
#write.table(gtex_data,"GTEx_pheno.txt",row.names=F,quote=F,sep='\t')

#################======= step2: clean a TCGA pheno data   =======#################
tcga <- read.delim(file="TCGA_phenotype_denseDataOnlyDownload.tsv.gz",header=T,as.is = T)
tcga <- merge(tcga_ref,tcga,by.y="X_primary_disease",by.x="Detail",all.y = T)
tcga <- tcga[tcga$sample_type %in% c("Primary Tumor","Solid Tissue Normal"),]

tcga$type <- ifelse(tcga$sample_type=='Solid Tissue Normal',
                    paste(tcga$TCGA,"normal_TCGA",sep="_"),paste(tcga$TCGA,"tumor_TCGA",sep="_"))
tcga$sample_type <- ifelse(tcga$sample_type=='Solid Tissue Normal',"normal","tumor")
tcga<-tcga[,c(3,2,6,5)]
names(tcga)[2] <- "tissue"
#write.table(tcga,"tcga_pheno.txt",row.names = F,quote=F,sep='\t')


#################======= step3: remove samples without tpm data =======############
gtex_exp <-  fread("gtex_RSEM_gene_tpm.gz",data.table = F)
gtexS <- gtex_data[ gtex_data$sample%in%colnames(gtex_exp)[-1],]

tcga_exp <- fread("tcga_RSEM_gene_tpm.gz",data.table = F)
tcgaS <- tcga[tcga$sample %in%colnames(tcga_exp)[-1],]
tcga_gtex <- rbind(tcgaS,gtexS)
write.table(tcga_gtex,"tcga_gtex_sample.txt",row.names = F,quote=F,sep='\t')

3. 提取感兴趣的基因

library(stringr)
library(dplyr)
library(ggplot2)
library(RColorBrewer)
library(data.table)
library(tibble)

rm(list = ls())
options(stringsAsFactors = FALSE) 

target <- "YTHDC2"

idmap <- read.delim("gencode.v23.annotation.gene.probemap",as.is=T)
tcga_exp <- fread("tcga_RSEM_gene_tpm.gz",data.table = F)
gtex_exp <- fread("gtex_RSEM_gene_tpm.gz",data.table=F)
tcga_gtex <- read.table("tcga_gtex_sample.txt",sep='\t',header = T)

id <- idmap$id[which(idmap$gene==target)]
tcga_data <- t(tcga_exp[tcga_exp$sample==id,colnames(tcga_exp)%in%c("sample",tcga_gtex$sample)])
tcga_data <- data.frame(tcga_data[-1,])
tcga_data <- rownames_to_column(tcga_data,"sample")
names(tcga_data)[2] <- "tpm"

gtex_data <- t(gtex_exp[gtex_exp$sample==id,colnames(gtex_exp)%in%c("sample",tcga_gtex$sample)])
gtex_data <- data.frame(gtex_data[-1,])
gtex_data <- rownames_to_column(gtex_data,"sample")
names(gtex_data)[2] <- "tpm"

tmp  <- rbind(tcga_data,gtex_data)
exp <- merge(tmp,tcga_gtex,by="sample",all.x=T)
exp <- exp[,c("tissue","sample_type","tpm")]
exp <- arrange(exp,tissue)
write.table(exp,"Merge gene expression/YTHDC2 expression.txt",row.names = F,quote=F,sep='\t')

4. 可视化基因表达

library(ggplot2)
library(ggpubr)
library(RColorBrewer)

rm(list = ls())
options(stringsAsFactors = FALSE) 

exp <- read.table("Merge gene expression/YTHDC2 expression.txt",header=T,sep='\t')

ylabname <- paste("YTHDC2", "expression")
colnames(exp) <- c("Tissue", "Group", "Gene")

p1 <- ggboxplot(exp, x = "Tissue", y = "Gene", fill = 'Group',
                ylab = ylabname,
                color = "Group", 
                palette = c("#00AFBB",  "#FC4E07"),
                ggtheme = theme_minimal())

##计算每种肿瘤正常和肿瘤组织的样本量
count_N<-exp %>% group_by(Tissue, Group) %>% tally
count_N$n <- paste("n =",count_N$n)
##添加N = 到图中
p1 <-p1+geom_text(data=count_N, aes(label=n, y=-9,color=Group), position=position_dodge2(0.9),size = 3,angle=90, hjust = 0)+
  theme(axis.text.x = element_text(angle = 45,hjust = 1.2))

#计算t检验显著性
comp<- compare_means(Gene ~ Group, group.by = "Tissue", data = exp,
                         method = "t.test", symnum.args = list(cutpoints = c(0,0.001, 0.01, 0.05, 1), symbols = c( "***", "**", "*", "ns")),
                         p.adjust.method = "holm")
#添加显著性标记
p2 <- p1 + stat_pvalue_manual(comp, x = "Tissue", y.position = 7.5,
                     label = "p.signif", position = position_dodge(0.8))
p2
#dev.off()

##保存图片
### pdf version
ggsave("figure/pancancer_Plot.pdf", width = 14, height = 5)

### png version
#png("figure/pancancer_Plot.png", width = 465, height = 225, units='mm', res = 300)

代码参考GitHub：https://github.com/cmutd/TCGA_GTEx

---

B站视频更新代码（从Xena下载的数据），其余代码一样：

exp <- read.table("YTHDC2.tsv",header=T,sep='\t')
exp <- exp[c(1,3)]
exp <- merge(exp, tcga_gtex,by="sample")
colnames(exp)[c(2,3,5)] <- c("Gene","Tissue","Group")
  
ylabname <- paste("YTHDC2", "expression")
exp <- exp %>% plotly::filter(Gene != -9.966)