下载TCGA数据的方法有很多，上一篇介绍了如何用gdc-client批量下载数据，基于网上有很多用TCGAbiolinks包下载数据的教程，所以也想学习一下这个方法。TCGAbiolinks的优点在于具备一体化的下载整合，无需再使用复杂的方法对下载的单个数据重新进行整合，换句话说，就是TCGAbiolinks包下载的数据是合并了的，不需要整理（TCGAbiolinks数据下载）。上一篇里我下载了20多个病人的RNA-seq数据，但是下载后发现这些文件是独立的，你还要对它们进行整合。所以我看到了TCGAbiolinks这个优点之后就决定要学习它了。（好吧，可能是因为懒。。。）
TCGAbiolinks的官方网站是：http://www.bioconductor.org/packages/devel/bioc/vignettes/TCGAbiolinks/inst/doc/index.html
（一）安装TCGAbiolinks
> BiocManager::install("TCGAbiolinks")
> library(TCGAbiolinks)
（二）选定要下载的cancer类型
> TCGAbiolinks::getGDCprojects()$project_id
[1] "TCGA-SARC" "TARGET-CCSK"
[3] "TARGET-NBL" "TARGET-AML"
[5] "TCGA-MESO" "TCGA-ACC"
[7] "TCGA-READ" "TCGA-LGG"
[9] "BEATAML1.0-CRENOLANIB" "TCGA-THCA"
[11] "VAREPOP-APOLLO" "HCMI-CMDC"
[13] "TCGA-CHOL" "TCGA-KIRC"
[15] "ORGANOID-PANCREATIC" "TCGA-BRCA"
[17] "TCGA-OV" "TCGA-GBM"
[19] "TCGA-SKCM" "GENIE-VICC"
[21] "TCGA-DLBC" "CGCI-BLGSP"
[23] "OHSU-CNL" "CPTAC-3"
[25] "BEATAML1.0-COHORT" "TCGA-KICH"
[27] "TCGA-UVM" "TCGA-THYM"
[29] "TCGA-TGCT" "TCGA-LUSC"
[31] "TCGA-PRAD" "FM-AD"
[33] "TCGA-UCEC" "TCGA-LAML"
[35] "TARGET-ALL-P2" "TCGA-STAD"
[37] "TARGET-ALL-P3" "GENIE-DFCI"
[39] "GENIE-NKI" "GENIE-MDA"
[41] "GENIE-JHU" "GENIE-MSK"
[43] "TCGA-ESCA" "TCGA-HNSC"
[45] "TARGET-OS" "TARGET-RT"
[47] "TCGA-LIHC" "CTSP-DLBCL1"
[49] "TCGA-COAD" "TCGA-LUAD"
[51] "TCGA-CESC" "TARGET-WT"
[53] "NCICCR-DLBCL" "TCGA-PAAD"
[55] "MMRF-COMMPASS" "TARGET-ALL-P1"
[57] "CPTAC-2" "TCGA-UCS"
[59] "TCGA-KIRP" "TCGA-PCPG"
[61] "TCGA-BLCA" "GENIE-UHN"
[63] "GENIE-GRCC"
缩写代表的癌症种类见链接：TCGA癌症缩写、癌症中英文对照
#因为我做头颈癌，所以选择HNSC，这个跟教程里的不一样
> cancer_type="TCGA-HNSC"
（三）选择下载你想要的数据类型
这里教程里下载的是临床数据，我也先按流程走一遍：
> clinical <- GDCquery_clinic(project= cancer_type,type = "clinical")
查看下载的数据：
> clinical[1:4,1:4]
submitter_id year_of_diagnosis classification_of_tumor last_known_disease_status
1 TCGA-4P-AA8J 2013 not reported not reported
2 TCGA-BA-4074 2003 not reported not reported
3 TCGA-BA-4075 2004 not reported not reported
4 TCGA-BA-4076 2003 not reported not reported
> dim(clinical)
[1] 528 78 #是个528行，78列的一个表
那么这个表里都有些什么，可以view一下看看：
> View(clinical)
可以看出这个表，一行是一个病例，列是根据这个病人的各项信息。
然后可以保存一下你下载的这个表了：
> save(clinical,file="BRCA_clinical.Rdata")
> write.csv(clinical, file="TCGAbiolinks-BRCA-clinical.csv")
（四）如果不想下载临床样品，我只想下载实验相关的数据，怎么办？
好说~网上也能搜到各种数据类型的下载方法：
（1）RNA-seq的count数据
代码参考：R包：TCGAbiolinks
> library(dplyr)
> library(DT)
> library(SummarizedExperiment)
> data_type <- "Gene Expression Quantification"#选择数据类型为“基因定量表达”
> data_category <- "Transcriptome Profiling" #选择数据类别为转录组
> workflow_type <- "HTSeq - Counts"
> query_TranscriptomeCounts <- GDCquery(project = cancer_type,
data.category = data_category,
data.type = data_type,
workflow.type = workflow_type)
# GDCquery函数参数详解官网网址：
http://www.bioconductor.org/packages/release/bioc/vignettes/TCGAbiolinks/inst/doc/query.html#useful_information
#然后会弹出一大串下面这些。。。
--------------------------------------
o GDCquery: Searching in GDC database
--------------------------------------
Genome of reference: hg38
--------------------------------------------
oo Accessing GDC. This might take a while...
--------------------------------------------
ooo Project: TCGA-HNSC
--------------------
oo Filtering results
--------------------
ooo By data.type
ooo By workflow.type
----------------
oo Checking data
----------------
ooo Check if there are duplicated cases
ooo Check if there results for the query
-------------------
o Preparing output
-------------------
#将上一步搜索得到的数据下载下来，自动存储到所设置目录下的文件夹
> GDCdownload(query_TranscriptomeCounts, method = "api")
#method：使用API (POST方法)或gdc客户端工具。选择“api”,“client”。API更快，但是下载过程中数据可能会损坏，可能需要重新执行。
Downloading data for project TCGA-HNSC
GDCdownload will download 546 files. A total of 136.906805 MB
Downloading as: Wed_Jan_08_16_55_43_2020.tar.gz
Downloading: 140 MB
#将搜索得到的数据转换为适用于R语言的形式，返回值为a summarizedExperiment or a data.frame---类似矩阵的容器，行名为基因，列名为样本名
> expdat <- GDCprepare(query = query_TranscriptomeCounts)
|===================================================================================|100% Completed after 1 m
Starting to add information to samples
=> Add clinical information to samples
Add FFPE information. More information at:
=> https://cancergenome.nih.gov/cancersselected/biospeccriteria
=> http://gdac.broadinstitute.org/runs/sampleReports/latest/FPPP_FFPE_Cases.html
=> Adding subtype information to samples
hnsc subtype information from:doi:10.1038/nature14129
Accessing www.ensembl.org to get gene information
Downloading genome information (try:0) Using: Human genes (GRCh38.p13)
From the 60483 genes we couldn't map 3971
这一步我在操作的时候有报错，如果你在操作的时候也出现了类似：internal error -3这样的报错，可以重新启动一下Rstudio。参考文章：lazy-load database 'P' is corrupt #3
> count_matrix=assay(expdat)
> View(count_matrix)#view一下看看矩阵啥样
每一列是一个病人，行是基因名称
这个表就是我们熟悉的count值了，你可以随心所欲的处理它，折磨它。。。
但是在任何操作之前千万别忘了保存：
> write.csv(count_matrix,file = "TCGAbiolinks_HNSC_counts.csv")
(2)下载RNA-seq的FPKM数据
> Expr_df <- GDCquery(project = cancer_type,
data.category = data_category,
data.type = data_type,
workflow.type = "HTSeq - FPKM")
> GDCdownload(Expr_df, method = "api", files.per.chunk = 100)
#files.per.chunk：这将使API方法一次只下载n个(files.per.chunk)文件。当数据量过大时，可能会下载出错，可设置files.per.chunk参数减少下载问题。值为整数，即可将文件拆分为几个文件下载，如files.per.chunk = 6。
Downloading data for project TCGA-HNSC
GDCdownload will download 546 files. A total of 278.868796 MB
Downloading chunk 1 of 6 (100 files, size = 51.032322 MB) as Wed_Jan_08_19_22_27_2020_0.tar.gz
Downloading: 51 MB Downloading chunk 2 of 6 (100 files, size = 51.063004 MB) as Wed_Jan_08_19_22_27_2020_1.tar.gz
Downloading: 51 MB Downloading chunk 3 of 6 (100 files, size = 51.028334 MB) as Wed_Jan_08_19_22_27_2020_2.tar.gz
Downloading: 51 MB Downloading chunk 4 of 6 (100 files, size = 51.08847 MB) as Wed_Jan_08_19_22_27_2020_3.tar.gz
Downloading: 51 MB Downloading chunk 5 of 6 (100 files, size = 51.14113 MB) as Wed_Jan_08_19_22_27_2020_4.tar.gz
Downloading: 51 MB Downloading chunk 6 of 6 (46 files, size = 23.515536 MB) as Wed_Jan_08_19_22_27_2020_5.tar.gz
Downloading: 24 MB
> expdat_2 <- GDCprepare(query = Expr_df)
> Expr_matrix=assay(expdat_2)
> write.csv(Expr_matrix,file = "TCGAbiolinks_HNSC_FPKM.csv")
（3）下载其他类型的数据
其他的数据我不经常用，但是也查询了代码，万一以后能用得着呢，参考文章有：
1.用TCGAbiolinks从TCGA数据下载到下游分析的学习笔记
2.R包：TCGAbiolinks
3.TCGA3.R包TCGAbiolinks下载数据
4.TCGA数据下载—TCGAbiolinks包参数详解
5.TCGA数据库下载：多种方法及优缺点介绍
#下载miRNA数据
query <- GDCquery(project = cancer_type,
data.category = "Transcriptome Profiling",
data.type = "miRNA Expression Quantification",
workflow.type = "BCGSC miRNA Profiling")
GDCdownload(query, method = "api", files.per.chunk = 50)
expdat <- GDCprepare(query = query)
count_matrix=assay(expdat)
write.csv(count_matrix,file = paste(cancer_type,"miRNA.csv",sep = "-"))
#下载Copy Number Variation数据
query <- GDCquery(project = cancer_type,
data.category = "Copy Number Variation",
data.type = "Copy Number Segment")
GDCdownload(query, method = "api", files.per.chunk = 50)
expdat <- GDCprepare(query = query)
count_matrix=assay(expdat)
write.csv(count_matrix,file = paste(cancer_type,"Copy-Number-Variation.csv",sep = "-"))
#下载甲基化数据
query.met <- GDCquery(project =cancer_type,
legacy = TRUE,
data.category = "DNA methylation")
GDCdownload(query.met, method = "api", files.per.chunk = 300)
expdat <- GDCprepare(query = query)
count_matrix=assay(expdat)
write.csv(count_matrix,file = paste(cancer_type,"methylation.csv",sep = "-"))
