TCGA数据库

TCGA数据库

前面我们已经给大家介绍过TCGA数据库中样本barcode的详细组成：TCGA样本barcode详细介绍，现在我们来看看如何将基因表达矩阵与样本临床信息进行合并，方便后续做 比如生存分析，基因在不同样本分期、性别、年龄分组等中的差异表达情况。

首先我们去TGCA下载如腺癌的基因表达矩阵

这里使用R包 TCGAbiolinks 去TCGA下载数据。

1、加载包：代码语言：javascript代码运行次数：0运行复制

## download tcga data
## update: 2024-02-22
## Author: zhang juan

rm(list=ls())
# 当然，需要先去安装这个包，如果已安装就可以跳过：
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("TCGAbiolinks")

## load packages
library(TCGAbiolinks)
library(SummarizedExperiment)
suppressPackageStartupMessages(library(tidyverse))

2、癌症类型选择：代码语言：javascript代码运行次数：0运行复制

# 癌症类型，用 getGDCprojects()$project_id 查看所有
getGDCprojects()$project_id

# [1] "TARGET-AML"                "MATCH-Z1I"                 "HCMI-CMDC"                 "MATCH-W"         
# [5] "MATCH-Z1D"                 "MATCH-Z1A"                 "MATCH-U"                   "MATCH-Q"         
# [9] "TCGA-PCPG"                 "MATCH-H"                   "MATCH-C1"                  "TCGA-THYM"       
# [1] "MATCH-I"                   "MATCH-S1"                  "MATCH-P"                   "MATCH-R"      
# [17] "MATCH-Z1B"                 "TCGA-PAAD"                 "TCGA-STAD"                 "TCGA-TGCT"   
# [21] "MATCH-S2"                  "TCGA-SARC"                 "TCGA-PRAD"                 "TCGA-READ"     
# [25] "TCGA-UCS"                  "TCGA-UVM"                  "TRIO-CRU"                  "VAREPOP-APOLLO"
# [29] "WCDT-MCRPC"                "TARGET-ALL-P1"             "REBC-THYR"                 "TARGET-ALL-P2"   
# ...           

不同缩写代表的含义可取这个地址查看：

本次腺癌缩写为：BRCA

、下载：代码语言：javascript代码运行次数：0运行复制

# 设置query 
query <- GDCquery(
  project = "TCGA-BRCA",  # 癌症类型，用 getGDCprojects()$project_id 查看所有
  ="Transcriptome Profiling",     # 数据类别, 用getProjectSummary(project)查看所有类别
   ="Gene Expression Quantification", # 数据类型
  ="STAR - Counts"                # 工作流类型
  )

## 下载数据
GDCdownload(query=query, files.= 50, directory = "./")

下来后的数据为一个样本一个tsv文件：如 8d1641ea-7552-4d2-9298-094e005686a.rna_seq.augmented_star_gene_

4、整合成一个表达矩阵：代码语言：javascript代码运行次数：0运行复制

## 整理数据并存储为 R对象
GDCprepare(query,save=T,save.filename="Rdata", directory = "./")

## 再次加载
load("Rdata")
ls()
names(assays(data))
rowdata <- rowData(data)

5、提取mRA的SummarizedExperiment对象，根据gene_type取子集，太简单了！代码语言：javascript代码运行次数：0运行复制

table(rowdata$gene_type)

tcga_mrna <- data[rowdata$gene_type == "protein_coding",]

tcga_mrna_count <- assay(tcga_mrna,"unstranded")   # mRA的counts矩阵
tcga_mrna_tpm <- assay(tcga_mrna, "tpm_unstrand")  # mRA的tpm矩阵
tcga_mrna_fpkm <- assay(tcga_mrna,"fpkm_unstrand") # mRA的fpkm矩阵


# 添加gene_symbol, 先提取gene_name
symbol_mrna <- rowData(tcga_mrna)$gene_name
head(symbol_mrna)

####################################################### count值
tcga_mrna_count_symbol <- cbind(data.frame(symbol_mrna), as.data.frame(tcga_mrna_count))

# 去重复保留最大的那个
tcga_mrna_count_symbol1 <- tcga_mrna_count_symbol %>% 
  as_tibble() %>% # tibble不支持row name，我竟然才发现！
  mutate(meanrow = rowMeans(.[,-1]), .before=2) %>% 
  arrange(desc(meanrow)) %>% 
  distinct(symbol_mrna,.keep_all=T) %>% 
  select(-meanrow)

saveRDS(tcga_mrna_count_symbol1, file = "tcga_mrna_count_symbol.rds")
(tcga_mrna_count_symbol1, file ="tcga_mrna_count_symbol.xls", = F,sep = "\t",quote = F)

####################################################### tpm值
tcga_mrna_tpm_symbol <- cbind(data.frame(symbol_mrna), as.data.frame(tcga_mrna_tpm))

# 去重复保留最大的那个
tcga_mrna_tpm_symbol1 <- tcga_mrna_tpm_symbol %>% 
  as_tibble() %>% # tibble不支持row name，我竟然才发现！
  mutate(meanrow = rowMeans(.[,-1]), .before=2) %>% 
  arrange(desc(meanrow)) %>% 
  distinct(symbol_mrna,.keep_all=T) %>% 
  select(-meanrow)

saveRDS(tcga_mrna_tpm_symbol1, file = "tcga_mrna_tpm_symbol.rds")
(tcga_mrna_tpm_symbol1, file = "tcga_mrna_tpm_symbol.xls", = F,sep = "\t",quote = F)

####################################################### fpkm值
tcga_mrna_fpkm_symbol <- cbind(data.frame(symbol_mrna), as.data.frame(tcga_mrna_fpkm))

# 去重复保留最大的那个
tcga_mrna_fpkm_symbol1 <- tcga_mrna_fpkm_symbol %>% 
  as_tibble() %>% # tibble不支持row name，我竟然才发现！
  mutate(meanrow = rowMeans(.[,-1]), .before=2) %>% 
  arrange(desc(meanrow)) %>% 
  distinct(symbol_mrna,.keep_all=T) %>% 
  select(-meanrow)

saveRDS(tcga_mrna_fpkm_symbol1, file = "tcga_mrna_fpkm_symbol.rds")
(tcga_mrna_fpkm_symbol1, file = "tcga_mrna_fpkm_symbol.xls", = F,sep = "\t",quote = F)

接着下载样本临床信息

1、同样首先需要联网 进行 query：代码语言：javascript代码运行次数：0运行复制

##############################################################################
########################## .批量下载临床数据 ###################################
##############################################################################
# ref: .html

query <- GDCquery(
  project = "TCGA-BRCA", 
   = "Clinical",
  data.format = "bcr xml"
  )
save(query, file = "query.rdata")

# 下载到当前目录
GDCdownload(query, files.= 50, directory = "./")

2、对下载的数据进行整理：代码语言：javascript代码运行次数：0运行复制

clinical <- GDCprepare_clinic(query, clinical.info = "patient", directory = "./")
clinical.follow_up <- GDCprepare_clinic(query, clinical.info = "follow_up", directory = "./")
clinical.stage_event <- GDCprepare_clinic(query, clinical.info = "stage_event", directory = "./")
clinical.drug <- GDCprepare_clinic(query, clinical.info = "drug", directory = "./")
clinical.radiation <- GDCprepare_clinic(query, clinical.info = "radiation", directory = "./")
clinical.admin <- GDCprepare_clinic(query, clinical.info = "admin", directory = "./")

# 保存
saveRDS(clinical, file = "_patient.rds")
saveRDS(clinical.admin, file = "_admin.rds")
saveRDS(clinical.drug, file = "_drug.rds")
saveRDS(clinical.follow_up, file = "_follow_up.rds")
saveRDS(clinical.radiation, file = "_radiation.rds")
saveRDS(clinical.stage_event, file = "_stage_event.rds")

现在将基因表达矩阵与临床信息整合在一起

先看看各自的样本ID名，根据前面的介绍《TCGA样本barcode详细介绍》，可以看到 表达矩阵里面的是样本ID，临床信息中是patient ID，一个病人可能会取多个样本，比如同时存在正常样本与肿瘤样本，也可能同时具有好几个肿瘤样本：

代码语言：javascript代码运行次数：0运行复制

# 表达矩阵 样本名
mrna_fpkm <- readRDS("tcga_mrna_fpkm_symbol.rds")
head(colnames(mrna_fpkm))

# [1] "symbol_mrna"                  "TCGA-5L-AAT0-01A-12R-A41B-07" "TCGA-A2-A04U-01A-11R-A115-07" "TCGA-A-A04A-01A-21R-A04-07"
# [5] "TCGA-A7-A1D-01A-1R-A12P-07" "TCGA-BH-A201-01A-11R-A14M-07"

# 临床信息
clinical <- readRDS(file = "_patient.rds")
colnames(clinical)
head(clinical[,1:6])
# 我们后面相比较不同病理分期间某个基因表达差异，这里过滤一下样本
clinical <- clinical[,c("bcr_patient_barcode", "stage_event_pathologic_stage")]
colnames(clinical) <- c("bcr_patient_barcode", "pathologic_stage")
str(clinical)
table(clinical$pathologic_stage)
clinical$pathologic_stage <- (clinical$pathologic_stage)
clinical <- clinical[clinical$pathologic_stage!="",]
clinical <- (clinical)
head(clinical)

# 变成 stage I 、II、III、IV、
clinical$stage <- clinical$pathologic_stage
clinical$stage[grepl("Stage I$|Stage IA$|Stage IB$",clinical$pathologic_stage)] <- "Stage I"
clinical$stage[grepl("Stage II$|Stage IIA$|Stage IIB$",clinical$pathologic_stage)] <- "Stage II"
clinical$stage[grepl("Stage III$|Stage IIIA$|Stage IIIB$|Stage IIIC$",clinical$pathologic_stage)] <- "Stage III"
table(clinical$stage)
table(clinical$pathologic_stage,clinical$stage)
clinical$stage <- factor(clinical$stage, levels = c("Stage I","Stage II","Stage III","Stage IV"))

那么，这里对应的时候，一般可以先将样本分为肿瘤样本与正常样本，看看肿瘤样本中 某个基因表达的高低分组 生存曲线KM差异：

肿瘤样本的编号一般为样本barcode中的第14-15位编码字符：

01-09为肿瘤样本，10以及10以上的为对照样本。肿瘤样本里面又有很多细小的分类：

我们这里直接提取 01A类的实体瘤样本：

代码语言：javascript代码运行次数：0运行复制

# 提取 01A类的实体瘤样本
table(str_sub(colnames(mrna_fpkm),14,16))
mrna_fpkm_tumor <- as.data.frame(mrna_fpkm[, str_sub(colnames(mrna_fpkm),14,16)=="01A"])
rownames(mrna_fpkm_tumor) <- mrna_fpkm$symbol_mrna
mrna_fpkm_tumor[1:6,1:6]

# 截取样本名字前面12个字符，与临床信息中的样本ID保持一致
colnames(mrna_fpkm_tumor) <- str_sub(colnames(mrna_fpkm_tumor), 1,12)
head(colnames(mrna_fpkm_tumor))

#[1] "TCGA-5L-AAT0" "TCGA-A2-A04U" "TCGA-A-A04A" "TCGA-A7-A1D" "TCGA-BH-A201" "TCGA-BH-A0H6"

具有临床信息的病人ID与肿瘤样本表达矩阵取交集：

代码语言：javascript代码运行次数：0运行复制

clinical_com <- clinical[match(comid, clinical$bcr_patient_barcode) ,]
mrna_fpkm_tumor_com <- mrna_fpkm_tumor[, comid]
dim(clinical_com)
# [1] 1056  114

dim(mrna_fpkm_tumor_com)
# [1] 1998  1056

查看腺癌中的明星基因 BRCA1 基因在不同分组中的差异吧：

代码语言：javascript代码运行次数：0运行复制

# 查看 brca1基因在不同分组中的差异吧
data <- data.frame(clinical_com, BRCA1=t(mrna_fpkm_tumor_com["BRCA1",]))
head(data)

刚好使用我们前面给大家介绍的绘图小技巧《带有疾病进展的多分组差异结果如何展示？》中的代码绘制：

代码语言：javascript代码运行次数：0运行复制

# 绘制小提琴图和显著性标记
library(ggplot2)
library(ggstatsplot)
library(patchwork)
library(reshape2)
library(stringr)
library(ggsignif)
library(ggsci)

max_pos <- max(data$BRCA1)
max_pos

B <- ggplot(data=data,aes(x=stage,y=BRCA1,colour = stage)) +  
  geom_boxplot(mapping=aes(x=stage,y=BRCA1,colour = stage), size=0.6, width = 0.5) + # 箱线图 
  geom_jitter(mapping=aes(x=stage,y=BRCA1,colour = stage),size=1.2) +  # 散点  
  scale_color_npg() + 
  # #scale_color_manual(limits=c("Stage I","Stage II","Stage III","Stage IV"), 
  #                    values =c( "#ed1a22","#00a651","#652b90","") ) + # 颜  
  geom_signif(mapping=aes(x=stage,y=BRCA1), # 不同组别的显著性              
              comparis = list(c("Stage I","Stage II"),c("Stage III","Stage IV"),c("Stage I","Stage III")),              
              map_signif_level=T, # T显示显著性，F显示p value              
              tip_length=c(0,0,0,0,0,0,0,0,0,0,0,0), # 修改显著性线两端的长短 
              y_position = c(max_pos,max_pos*1.04,max_pos*1.05), # 设置显著性线的位置高度              
              size=0.8, # 修改线的粗细              
              textsize = 4, # 修改显著性标记的大小              
              test = "") + # 检验的类型,可以更改  
  theme_classic() + #设置白背景
  labs(x="",y="")  + # 添加标题，x轴，y轴标签 
  ggtitle(label = "BRCA1") +
  theme( = element_text(hjust = 0.5),
        axis.line=element_line(linetype=1,color="black",size=0.9),
        x = element_text(size = 12))

B

结果如下：

学会了这个，后面就可以随意绘制任意基因在任意临床表型分组间的差异了！

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