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TCGA 数据下载 —— TCGAbiolinks 简单使用

作者：我家小花儿 | 2024-08-01 17:00:36

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tcgabiolinks

前言

前面我们曾介绍过 TCGAbiolinks 的使用，但是由于 TCGA 的改版，该包也做出了相应的更新，所以我再重新介绍一下新版 TCGAbiolinks 的使用。

TCGAbiolinks 是一个利用 GDC API 接口来查询、下载和分析 TCGA 数据库的数据的 R 包

TCGAbiolinks 包的功能主要可以分为三大块：

数据查询和下载
数据的常规分析
可视化

该包可以从 Bioconductor 上安装稳定版本

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("TCGAbiolinks")
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或者从 GitHub 上安装开发版本

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("BioinformaticsFMRP/TCGAbiolinksGUI.data")
BiocManager::install("BioinformaticsFMRP/TCGAbiolinks")
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导入包

library(TCGAbiolinks)  # version 2.30.4
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数据查询

TCGAbiolinks 提供了一些函数用于查询和下载 GDC 中的数据，包括：

Harmonized：这部分数据都比较新，使用的是 GRCh38 (hg38) 基因组版本，使用的是 GDC pipeline 来处理数据
Legacy：这部分的数据应该是较早之前测的，使用的是 GRCh37 (hg19) 基因组版本

使用 GDCquery 函数来查询 GDC 的数据，该函数的参数为：

GDCquery(
  project,
  data.category,
  data.type,
  workflow.type,
  access,
  platform,
  barcode,
  data.format,
  experimental.strategy,
  sample.type
)
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project：该参数的取值非常多，可以使用如下命令来查询所有可用的项目

> TCGAbiolinks:::getGDCprojects()$project_id
[1] "HCMI-CMDC"                 "TCGA-BRCA"                 "TARGET-ALL-P3"            
 [4] "EXCEPTIONAL_RESPONDERS-ER" "CGCI-HTMCP-LC"             "CPTAC-2"                  
 [7] "CMI-MBC"                   "TARGET-ALL-P2"             "OHSU-CNL"                 
[10] "TARGET-ALL-P1"             "MMRF-COMMPASS"             "ORGANOID-PANCREATIC"      
[13] "NCICCR-DLBCL"              "TCGA-SARC"                 "TCGA-ACC"                 
[16] "WCDT-MCRPC"                "TCGA-UCEC"                 "MP2PRT-ALL"               
[19] "TCGA-KIRC"                 "CGCI-HTMCP-CC"             "CMI-ASC"                  
[22] "CGCI-HTMCP-DLBCL"          "BEATAML1.0-CRENOLANIB"     "CDDP_EAGLE-1"             
[25] "APOLLO-LUAD"               "CMI-MPC"                   "FM-AD"                    
[28] "MATCH-Z1D"                 "MATCH-Y"                   "MATCH-N"                  
[31] "MATCH-Q"                   "MP2PRT-WT"                 "TCGA-LAML"                
[34] "VAREPOP-APOLLO"            "TCGA-SKCM"                 "TRIO-CRU"                 
[37] "TCGA-PAAD"                 "TCGA-TGCT"                 "TCGA-CESC"                
[40] "TCGA-ESCA"                 "TCGA-THCA"                 "TCGA-LIHC"                
[43] "TCGA-PRAD"                 "TCGA-READ"                 "MATCH-I"                  
[46] "MATCH-W"                   "MATCH-B"                   "MATCH-H"                  
[49] "TCGA-OV"                   "TCGA-UVM"                  "MATCH-Z1A"                
[52] "MATCH-U"                   "BEATAML1.0-COHORT"         "TCGA-BLCA"                
[55] "CGCI-BLGSP"                "CTSP-DLBCL1"               "MATCH-S1"                 
[58] "MATCH-R"                   "MATCH-Z1I"                 "CPTAC-3"                  
[61] "TCGA-CHOL"                 "TCGA-GBM"                  "MATCH-S2"                 
[64] "TCGA-UCS"                  "TCGA-PCPG"                 "TCGA-MESO"                
[67] "TARGET-CCSK"               "TARGET-WT"                 "TARGET-RT"                
[70] "TCGA-DLBC"                 "TARGET-OS"                 "TCGA-COAD"                
[73] "REBC-THYR"                 "TCGA-STAD"                 "TCGA-KIRP"                
[76] "TCGA-THYM"                 "TCGA-KICH"                 "TCGA-LGG"                 
[79] "TARGET-AML"                "TCGA-LUSC"                 "TCGA-LUAD"                
[82] "TCGA-HNSC"                 "TARGET-NBL"
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data.category：可以使用如下方式来查询 TCGA-BRCA 项目的可用的分类数据

> TCGAbiolinks:::getProjectSummary("TCGA-BRCA")
$file_count
[1] 61173

$data_categories
   file_count case_count                data_category
1       17337       1098  Simple Nucleotide Variation
2        9281       1098             Sequencing Reads
3        5316       1098                  Biospecimen
4        2288       1098                     Clinical
5       12292       1098        Copy Number Variation
6        4876       1097      Transcriptome Profiling
7        3714       1097              DNA Methylation
8         919        881           Proteome Profiling
9         226        101 Somatic Structural Variation
10       4924       1095         Structural Variation

$case_count
[1] 1098

$file_size
[1] 6.245362e+14
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新版本只能获取 harmonized 类型的数据，主要包含 7 个分类：

- Biospecimen
- Clinical
- Copy Number Variation
- DNA Methylation
- Sequencing Reads
- Simple Nucleotide Variation
- Transcriptome Profiling
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注意

legacy 类型的数据在这个版本中已经不支持了，因为 GDC 很块将会把该类型数据关闭。

sample.type：样本类型，取值可以是三列中的任意一个值

至于其他参数，对于 harmonized 数据，其参数取值有

参数列表的详细取值，可以从下面的网址中下载得到：https://docs.google.com/spreadsheets/d/1f98kFdj9mxVDc1dv4xTZdx8iWgUiDYO-qiFJINvmTZs/export?format=csv&gid=2046985454

harmonized 数据库

1. 甲基化数据：复发肿瘤样本

在这个例子中，我们在 harmonized 数据库中搜索复发性 GBM 样本的甲基化数据

query <- GDCquery(
  project = c("TCGA-GBM"),
  data.category = "DNA Methylation",
  platform = c("Illumina Human Methylation 450"),
  sample.type = "Recurrent Tumor"
)

data <- getResults(query)
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getResults() 用于获取查询结果，可以设置 rows 和 cols 参数来设置返回的行和列

如果是使用 RStudio，可以使用 View(data) 来查看数据，也可以使用 DT 包的 datatable 来展示数据

返回结果如下

2. 查询甲基化和表达数据

从数据库中分别获取所有包含甲基化数据（450k）和表达数据的乳腺癌患者信息

query.met <- GDCquery(
  project = "TCGA-BRCA",
  data.category = "DNA Methylation",
  platform = c("Illumina Human Methylation 450")
)
query.exp <- GDCquery(
  project = "TCGA-BRCA",
  data.category = "Transcriptome Profiling",
  data.type = "Gene Expression Quantification", 
  workflow.type = "STAR - Counts"
)
# 获取既检测甲基化又检测表达的样本
common.patients <- intersect(
  substr(getResults(query.met, cols = "cases"), 1, 12),
  substr(getResults(query.exp, cols = "cases"), 1, 12)
)
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> length(common.patients)
[1] 789
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共有 789 个样本，可以设置 barcode 参数，只选择部分样本的数据

selected.met <- GDCquery(
    project = "TCGA-BRCA",
    data.category = "DNA Methylation",
    platform = c("Illumina Human Methylation 450"),
    barcode = common.patients[1:5]
)
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或者直接使用 matchedMetExp 获取同时检测两种类型数据的样本编号

> samples <- matchedMetExp("TCGA-BRCA", n = 10)
> samples
 [1] "TCGA-AQ-A0Y5-01" "TCGA-C8-A274-01" "TCGA-B6-A1KC-01" "TCGA-AC-A62V-01" "TCGA-A2-A0YM-01"
 [6] "TCGA-AO-A03N-01" "TCGA-AO-A1KQ-01" "TCGA-E2-A1LI-01" "TCGA-B6-A0WV-01" "TCGA-E2-A1LE-01"
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3. 查询原始数据

从数据库中查询乳腺癌原始测序数据（bam），这部分样本访问受限，一般人用不了。

query <- GDCquery(
    project = "TCGA-BRCA", 
    data.category = "Sequencing Reads",
    data.type = "Aligned Reads", 
    data.format = "bam",
    workflow.type = "STAR 2-Pass Transcriptome"
    # workflow.type = "STAR 2-Pass Genome"
    # workflow.type = "STAR 2-Pass Chimeric"
    # workflow.type = "BWA with Mark Duplicates and BQSR"
    # workflow.type = "BWA-aln"
)
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> getResults(query, rows = 1:6, cols = c("file_name","cases"))
                                                                  file_name
1  0361ec21-e83f-40a9-b825-d92487b1239a.rna_seq.transcriptome.gdc_realn.bam
2  0a9e33db-2527-4cc3-8669-a7c10fed7a7f.rna_seq.transcriptome.gdc_realn.bam
3  65c868ba-94ed-43f2-bfd1-814979ee9486.rna_seq.transcriptome.gdc_realn.bam
4  09f906ba-fa4b-487f-9631-fbd97d9db8f9.rna_seq.transcriptome.gdc_realn.bam
5  8c92ecf0-5d81-48c3-ad9f-9adf51860962.rna_seq.transcriptome.gdc_realn.bam
6  a8e0466b-d106-44b7-b846-c4fb666715f5.rna_seq.transcriptome.gdc_realn.bam
                          cases
1  TCGA-C8-A1HO-01A-11R-A13Q-07
2  TCGA-A7-A0CD-01A-11R-A00Z-07
3  TCGA-AO-A0J5-01A-11R-A034-07
4  TCGA-B6-A0IK-01A-12R-A056-07
5  TCGA-A7-A4SB-01A-21R-A266-07
6  TCGA-E9-A1R4-01A-21R-A14D-07
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获取 manifest 文件

我们可以获取查询到数据的 manifest 文件列表，设置 save = TRUE 可以将结果输出到文件中，用其他软件下载对应的数据

getManifest(query, save = FALSE) 
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获取 ATAC-seq 数据

getResults(TCGAbiolinks:::GDCquery_ATAC_seq())[,c("file_name","file_size")]
# A tibble: 47 × 2
   file_name                                                          file_size
   <chr>                                                                  <dbl>
 1 TCGA-ATAC_PanCancer_PeakSet.txt                                     37522221
 2 TCGA-ATAC_DataS1_DonorsAndStats_v4.xlsx                               251795
 3 MESO_bigWigs.tgz                                                  1507969705
 4 TCGA-ATAC_DataS5_GWAS_v2.xlsx                                         999661
 5 COAD_bigWigs.tgz                                                  8939070313
 6 TCGA-ATAC_PanCan_Raw_Counts.txt                                   1241263873
 7 TGCT_bigWigs.tgz                                                  2058854449
 8 TCGA-ATAC_DataS6_Footprinting_v1.xlsx                               19544445
 9 TCGA-ATAC_DataS10_WGS-ATAC_v2_Open.xlsx                               893110
10 TCGA-ATAC_Cancer_Type-specific_Count_Matrices_log2norm_counts.zip  632769396
# ℹ 37 more rows
# ℹ Use `print(n = ...)` to see more rows
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数据下载与处理

TCGAbiolinks 提供了一些函数，用于下载和处理来自 GDC 数据库的数据，并用于后续的分析

数据下载 GDCdownload

下载 GDC 数据的方法有两种：

client：该方法会创建 MANIFEST 文件并使用 GDC Data Transfer Tool 来下载数据，但是速度上会比使用 API 更慢
api：该方法使用 GDC API 接口来下载数据，也会创建 MANIFEST 文件并将数据保存为 tar.gz 文件，如果数据过大，会将数据拆分为小块分别下载，files.per.chunk 参数可以控制同时下载的文件个数，减少下载错误

数据处理 GDCprepare：

使用 GDCprepare 处理数据后，会返回 SummarizedExperiment 或 data.frame 对象，主要包含三个主要的矩阵，可以使用 SummarizedExperiment 包来获取对应的信息：

样本信息矩阵，使用 colData(data) 来获取样本信息
检测矩阵信息，使用 assay(data) 来获取分子数据
特征矩阵信息，使用 rowRanges(data) 来获取特征元数据信息，如基因的信息

参数列表

查询和下载数据

我们从 harmonized（参考基因组为 hg38）数据库中下载基因表达数据

# Gene expression aligned against hg38
query <- GDCquery(
    project = "TCGA-GBM",
    data.category = "Transcriptome Profiling",
    data.type = "Gene Expression Quantification", 
    workflow.type = "STAR - Counts",
    barcode = c("TCGA-14-0736-02A-01R-2005-01", "TCGA-06-0211-02A-02R-2005-01")
)
GDCdownload(query = query)
data <- GDCprepare(query = query)
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返回的对象为 SummarizedExperiment 类型

class(data)
# [1] "RangedSummarizedExperiment"
# attr(,"package")
# [1] "SummarizedExperiment"
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获取样本信息

# 先导入包
library(SummarizedExperiment)
colData(data)[,1:4]
# DataFrame with 2 rows and 4 columns
#                                             barcode      patient           sample shortLetterCode
#                                         <character>  <character>      <character>     <character>
# TCGA-14-0736-02A-01R-2005-01 TCGA-14-0736-02A-01R.. TCGA-14-0736 TCGA-14-0736-02A              TR
# TCGA-06-0211-02A-02R-2005-01 TCGA-06-0211-02A-02R.. TCGA-06-0211 TCGA-06-0211-02A              TR
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获取表达数据

assay(data)[1:6,]
# TCGA-14-0736-02A-01R-2005-01 TCGA-06-0211-02A-02R-2005-01
# ENSG00000000003.15                         4239                         3825
# ENSG00000000005.6                            34                            7
# ENSG00000000419.13                          891                         1775
# ENSG00000000457.14                          251                          455
# ENSG00000000460.17                          123                          333
# ENSG00000000938.13                          284                          677
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获取基因信息

rowRanges(data)[1:3, 1:3]
# GRanges object with 3 ranges and 3 metadata columns:
#     seqnames              ranges strand |   source     type     score
#     <Rle>              <IRanges>  <Rle> | <factor> <factor> <numeric>
# ENSG00000000003.15     chrX 100627108-100639991      - |   HAVANA     gene        NA
# ENSG00000000005.6     chrX 100584936-100599885      + |   HAVANA     gene        NA
# ENSG00000000419.13    chr20   50934867-50958555      - |   HAVANA     gene        NA
# -------
#     seqinfo: 25 sequences from an unspecified genome; no seqlengths
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GDCprepare

GDCprepare 函数的功能还在开发中，并不是所有情况下都能正常工作，目前支持的数据处理包括下表中列出的情况

下载 CNV 数据

下载 Copy Number Segment 类型的数据

query <- GDCquery(
    project = "TCGA-ACC", 
    data.category = "Copy Number Variation",
    data.type = "Copy Number Segment",
    barcode = c( "TCGA-OR-A5KU-01A-11D-A29H-01", "TCGA-OR-A5JK-01A-11D-A29H-01")
)
GDCdownload(query)
data <- GDCprepare(query)
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下载 Gene Level Copy Number 类型的数据

query <- GDCquery(
    project = "TCGA-ACC",
    data.category = "Copy Number Variation",
    data.type = "Gene Level Copy Number",              
    access = "open"
)
GDCdownload(query)
data <- GDCprepare(query)
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下载 Allele-specific Copy Number Segment 类型的数据

query <- GDCquery(
    project = "TCGA-ACC",
    data.category = "Copy Number Variation",
    data.type = "Allele-specific Copy Number Segment",              
    access = "open"
)
GDCdownload(query)
data <- GDCprepare(query)
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下载 Masked Copy Number Segment 类型的数据

query <- GDCquery(
    project = "TCGA-ACC",
    data.category = "Copy Number Variation",
    data.type = "Masked Copy Number Segment",              
    access = "open"
)
GDCdownload(query)
data <- GDCprepare(query)

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下载转录组数据

下载 mRNA 表达数据

# mRNA pipeline: https://gdc-docs.nci.nih.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/
query.exp.hg38 <- GDCquery(
    project = "TCGA-GBM", 
    data.category = "Transcriptome Profiling", 
    data.type = "Gene Expression Quantification", 
    workflow.type = "STAR - Counts",
    barcode =  c("TCGA-14-0736-02A-01R-2005-01", "TCGA-06-0211-02A-02R-2005-01")
)
GDCdownload(query.exp.hg38)
expdat <- GDCprepare(
    query = query.exp.hg38,
    save = TRUE, 
    save.filename = "exp.rda"
)
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下载 miRNA 表达数据

query.mirna <- GDCquery(
    project = "TARGET-AML", 
    experimental.strategy = "miRNA-Seq",
    data.category = "Transcriptome Profiling", 
    barcode = c("TARGET-20-PATDNN","TARGET-20-PAPUNR"),
    data.type = "miRNA Expression Quantification"
)
GDCdownload(query.mirna)
mirna <- GDCprepare(
    query = query.mirna,
    save = TRUE, 
    save.filename = "mirna.rda"
)
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下载 isoform（剪切异构体）表达数据

query.isoform <- GDCquery(
    project = "TARGET-AML", 
    experimental.strategy = "miRNA-Seq",
    data.category = "Transcriptome Profiling", 
    barcode = c("TARGET-20-PATDNN","TARGET-20-PAPUNR"),
    data.type = "Isoform Expression Quantification"
)
GDCdownload(query.isoform)

isoform <- GDCprepare(
    query = query.isoform,
    save = TRUE, 
    save.filename = "mirna-isoform.rda"
)
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下载 DNA 甲基化数据

下载甲基化 Beta 值数据

# 27K 数据
query_met.hg38 <- GDCquery(
    project = "TCGA-BRCA", 
    data.category = "DNA Methylation", 
    data.type = "Methylation Beta Value",
    platform = "Illumina Human Methylation 27", 
    barcode = c("TCGA-B6-A0IM")
)
GDCdownload(query_met.hg38)
data.hg38 <- GDCprepare(query_met.hg38)
# 450K 数据
query_met.hg38 <- GDCquery(
    project= "TCGA-LGG", 
    data.category = "DNA Methylation", 
    data.type = "Methylation Beta Value",
    platform = "Illumina Human Methylation 450", 
    barcode = c("TCGA-HT-8111-01A-11D-2399-05","TCGA-HT-A5R5-01A-11D-A28N-05")
)
GDCdownload(query_met.hg38)
data.hg38 <- GDCprepare(query_met.hg38)

# Epic
query_met.hg38 <- GDCquery(
    project= "HCMI-CMDC", 
    data.category = "DNA Methylation", 
    data.type = "Methylation Beta Value",
    platform = "Illumina Methylation Epic", 
    barcode = c("HCM-BROD-0045")
)
GDCdownload(query_met.hg38)
data.hg38 <- GDCprepare(query_met.hg38)
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下载 IDAT 文件

query <- GDCquery(
    project = "TCGA-BRCA",
    data.category = "DNA Methylation",
    data.type = "Masked Intensities",
    platform = "Illumina Human Methylation 27"
)
GDCdownload(query, files.per.chunk=10)
betas <- GDCprepare(query)

query <- GDCquery(
    project = "HCMI-CMDC",
    data.category = "DNA Methylation",
    data.type = "Masked Intensities",
    platform = "Illumina Methylation Epic"
)
GDCdownload(query, files.per.chunk = 10)
betas <- GDCprepare(query)
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下载蛋白质组数据

下载蛋白质芯片数据

query.rppa <- GDCquery(
    project = "TCGA-ESCA", 
    data.category = "Proteome Profiling",
    data.type = "Protein Expression Quantification"
)
GDCdownload(query.rppa) 
rppa <- GDCprepare(query.rppa) 
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下载临床数据

下载样本的临床信息

# XML 格式
query <- GDCquery(
    project = "TCGA-COAD",
    data.category = "Clinical",
    data.type = "Clinical Supplement",
    data.format = "BCR XML",
    barcode = "TCGA-A6-5664"
)
GDCdownload(query)
drug <- GDCprepare_clinic(query,"drug")

query <- GDCquery(
    project = "TCGA-COAD",
    data.category = "Clinical",
    data.type = "Clinical Supplement",
    data.format = "BCR OMF XML",
    barcode = "TCGA-AD-6964"
)
GDCdownload(query)


query <- GDCquery(
    project = "TCGA-ACC", 
    data.category = "Clinical",
    data.type = "Clinical Supplement", 
    data.format = "BCR Biotab"
)
GDCdownload(query)
clinical.BCRtab.all <- GDCprepare(query)
names(clinical.BCRtab.all)
# 放疗信息
query <- GDCquery(
    project = "TCGA-ACC", 
    data.category = "Clinical",
    data.type = "Clinical Supplement", 
    data.format = "BCR Biotab",
    file.type = "radiation"
)
GDCdownload(query)
clinical.BCRtab.radiation <- GDCprepare(query)
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下载 SNV 数据

下载单核苷酸变异信息

query <- GDCquery(
    project = "TCGA-HNSC", 
    data.category = "Simple Nucleotide Variation",
    data.type = "Masked Somatic Mutation",
    access = "open"
)
GDCdownload(query)
maf <- GDCprepare(query)
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也可以下载 MC3 MAF 文件

maf <- getMC3MAF()
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然后搭配 maftools 来可视化突变数据。例如

library(maftools)
library(dplyr)

query <- GDCquery(
    project = "TCGA-CHOL", 
    data.category = "Simple Nucleotide Variation", 
    access = "open",
    data.type = "Masked Somatic Mutation", 
    workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking"
)
GDCdownload(query)
maf <- GDCprepare(query)

maf <- read.maf(maf)
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绘制简单的汇总结果图

plotmafSummary(maf = maf, rmOutlier = TRUE, addStat = 'median', dashboard = TRUE)
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绘制突变瀑布图

oncoplot(maf = maf, top = 10, removeNonMutated = TRUE)
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绘制 ti（transitions）和 tv（transversions）的比例，以及样本中 6 个碱基替换的占比

titv <- titv(maf = maf, plot = FALSE, useSyn = TRUE)
plotTiTv(res = titv)
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获取分子亚型

PanCancerAtlas_subtypes 函数可访问从 synapse 检索到的信息（可能包含最新的分子亚型）

subtypes <- PanCancerAtlas_subtypes()
subtypes[1:4, 1:4]
# # A tibble: 4 × 4
#   pan.samplesID cancer.type Subtype_mRNA                         Subtype_DNAmeth  
#   <chr>         <chr>       <chr>                                <chr>            
# 1 TCGA-OR-A5J1  ACC         steroid-phenotype-high+proliferation CIMP-high        
# 2 TCGA-OR-A5J2  ACC         steroid-phenotype-high+proliferation CIMP-low         
# 3 TCGA-OR-A5J3  ACC         steroid-phenotype-high               CIMP-intermediate
# 4 TCGA-OR-A5J4  ACC         NA                                   CIMP-high 
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而 TCGAquery_subtype 函数则包含从 TCGA 论文中检索到的样本信息的完整表。

TCGAquery_subtype(tumor = "acc")[1:3, 1:4]
# acc subtype information from:doi:10.1016/j.ccell.2016.04.002
#        patient  Histology C1A.C1B                              mRNA_K4
# 1 TCGA-OR-A5J1 Usual Type     C1A steroid-phenotype-high+proliferation
# 2 TCGA-OR-A5J2 Usual Type     C1A steroid-phenotype-high+proliferation
# 3 TCGA-OR-A5J3 Usual Type     C1A               steroid-phenotype-high
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下载单细胞数据

获取单细胞转录组数据

query.sc.analysis <- GDCquery(
    project = "CPTAC-3", 
    data.category = "Transcriptome Profiling",
    access = "open",
    data.type = "Single Cell Analysis",
    data.format =  "TSV"
)  
GDCdownload(query.sc.analysis)
Single.Cell.Analysis.list <- GDCprepare(query.sc.analysis)
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10x Raw Counts 数据

query.raw.counts <- GDCquery(
    project = "CPTAC-3", 
    data.category = "Transcriptome Profiling",
    access = "open",
    data.type = "Gene Expression Quantification",
    barcode = c("CPT0167860015","CPT0206880004"),
    workflow.type = "CellRanger - 10x Raw Counts"
)  
GDCdownload(query.raw.counts)
raw.counts.list <- GDCprepare(query.raw.counts)
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过滤后的 10x Raw Counts 数据

query.filtered.counts <- GDCquery(
    project = "CPTAC-3", 
    data.category = "Transcriptome Profiling",
    access = "open",
    data.type = "Gene Expression Quantification",
    barcode = c("CPT0167860015","CPT0206880004"),
    workflow.type = "CellRanger - 10x Filtered Counts"
)  
GDCdownload(query.filtered.counts)
filtered.counts.list <- GDCprepare(query.filtered.counts)
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10x Chromium 差异表达数据

query.sc.dea <- GDCquery(
    project = "CPTAC-3", 
    data.category = "Transcriptome Profiling",
    access = "open",
    data.type = "Differential Gene Expression",
    barcode = c("CPT0167860015","CPT0206880004"),
    workflow.type = "Seurat - 10x Chromium"
)  
GDCdownload(query.sc.dea)
sc.dea.list <- GDCprepare(query.sc.dea)
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