众所周知，TCGA数据库改版了！！改的比之前更好用了！
对于常规转录组数据，主要是以下几点改变：
下载一次即可获得counts、TPM、FPKM三种类型的表达矩阵，再也不用单独下载了
自带gene symbol，不用自己找各种方法转换了
自带基因类型，可以直接区分mRNA和lncRNA了
TCGAbiolinks不仅是数据下载，它能访问、下载全部的TCGA数据（除了受限制的），用它下载的数据是最新最全的！这和直接去GDC官网，使用网页下载的方式是一样的。
除了常规的转录组数据，还包括甲基化数据、SNP数据、突变数据、临床数据等多种数据类型，还能进行数据分析，比如差异分析、生存分析、聚类等，除此之外，它也具有强大的绘图功能，可以直接绘制突变瀑布图等多种图形，是一个全面的TCGA包。
作为官方唯一推荐的专用下载及分析可视化一体的R包：TCGAbiolinks，也进行了相应的更新。
而xena的数据并不会及时更新，最新的数据还停留在2019年。
因为网络问题一直没怎么学习过这个强大的R包，最近数据更新了，学习下。
安装
需要安装版本在2.25.1以上的版本！
# 经典2选1
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("TCGAbiolinks")
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("BioinformaticsFMRP/TCGAbiolinksGUI.data")
BiocManager::install("BioinformaticsFMRP/TCGAbiolinks")
注意：目前
bioconductor上面的TCGAbiolinks还停留在2.24.3版本，你需要安装开发版本哦~
如果你安装不成功，可以下载到本地安装，如果你不会本地安装，请翻看b站视频：可能是最适合初学者的R包安装教程
使用
对网络有要求！
如果这一步都不成功，建议下面的就别运行了，因为很可能也不会成功。
# 查看TCGA中33种癌症的简称
library(TCGAbiolinks)
projects <- TCGAbiolinks::getGDCprojects()$project_id ##获取癌症名字
projects <- projects[grepl('^TCGA', projects, perl=TRUE)]
projects
## [1] "TCGA-READ" "TCGA-UCS" "TCGA-COAD" "TCGA-CESC" "TCGA-PAAD" "TCGA-ESCA"
## [7] "TCGA-KIRP" "TCGA-PCPG" "TCGA-HNSC" "TCGA-BLCA" "TCGA-STAD" "TCGA-SARC"
## [13] "TCGA-CHOL" "TCGA-LAML" "TCGA-THYM" "TCGA-ACC" "TCGA-SKCM" "TCGA-LUAD"
## [19] "TCGA-LIHC" "TCGA-KIRC" "TCGA-KICH" "TCGA-DLBC" "TCGA-PRAD" "TCGA-OV"
## [25] "TCGA-MESO" "TCGA-LUSC" "TCGA-GBM" "TCGA-UVM" "TCGA-LGG" "TCGA-BRCA"
## [31] "TCGA-TGCT" "TCGA-THCA" "TCGA-UCEC"
批量下载mRNA和lncRNA的数据
需要良好的网络环境，网络不好就别试了。全部数据40+G。
sapply(projects, function(project){
# 查询
query <- GDCquery(project = project,
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "STAR - Counts"
)
# 下载
GDCdownload(query, method = "api", files.per.chunk = 100) #每次下载100个文件
# 整理
GDCprepare(query,save = T,save.filename = paste0(project,"_mRNA.Rdata"))
}
)
如果query能成功，但是下载成功，可以通过网页下载后，放在指定目录中，然后再运行GDCprepare函数也是可以成功的！
批量下载临床数据
也可以使用GDCquery_clinic()直接下载。
sapply(projects, function(project){
query <- GDCquery(project = project,
data.category = "Clinical",
file.type = "xml")
GDCdownload(query)
clinical <- GDCprepare_clinic(query, clinical.info = "patient")
saveRDS(clinical,file = paste0(project,"_clinical.rds"))
})
使用方法做个小记录，可以通过不同的参数快速获取不同的临床数据：
clinical <- GDCprepare_clinic(query, clinical.info = "patient")
To get the following information please change the clinical.info argument
=> new_tumor_events: new_tumor_event
=> drugs: drug
=> follow_ups: follow_up
=> radiations: radiation
批量下载miRNA
sapply(projects, function(project){
query <- GDCquery(project = project,
data.category = "Transcriptome Profiling",
data.type = "miRNA Expression Quantification"
)
GDCdownload(query)
GDCprepare(query, save = T,save.filename = paste0(project,"_miRNA.Rdata"))
})
批量下载SNP
sapply(projects, function(project){
query <- GDCquery(
project = project,
data.category = "Simple Nucleotide Variation",
data.type = "Masked Somatic Mutation",
access = "open"
)
GDCdownload(query)
GDCprepare(query, save = T,save.filename = paste0(project,"_SNP.Rdata"))
})
批量下载CNV
sapply(projects, function(project){
query <- GDCquery(
project = project,
data.category = "Copy Number Variation",
data.type = "Masked Copy Number Segment",
access = "open"
)
GDCdownload(query)
GDCprepare(query, save = T,save.filename = paste0(project,"_CNV.Rdata"))
})
批量下载甲基化
数据太大了，只下载一个COAD的演示一下~
β值矩阵：
coad_methy <- GDCquery(
project = "TCGA-COAD",
data.category = "DNA Methylation",
data.type = "Methylation Beta Value",
platform = "Illumina Human Methylation 27" # Illumina Human Methylation 450
)
GDCdownload(coad_methy)
GDCprepare(coad_methy,save = T,save.filename="COAD_METHY_beta.Rdata")
IDAT：
coad_methy <- GDCquery(
project = "TCGA-COAD",
data.category = "DNA Methylation",
data.type = "Masked Intensities",
platform = "Illumina Human Methylation 27", # Illumina Human Methylation 450
legacy = FALSE
)
GDCdownload(coad_methy)
GDCprepare(coad_methy,save = T,save.filename="COAD_METHY_idat.Rdata")
批量下载蛋白质数据
sapply(projects, function(project){
query <- GDCquery(
project = project,
data.category = "Proteome Profiling",
data.type = "Protein Expression Quantification"
)
GDCdownload(query)
GDCprepare(query, save = T,save.filename = paste0(project,"_protein.Rdata"))
})
亲测可用，我下载了2天1夜......
除此之外，还有其他数据可用，大家可以去官网学习~
