TCGAbiolinks：搜索，下载和可视化突变数据

发表时间：2019年3月20日

在运行程序前，我们需要导入必要的包：

library(TCGAbiolinks)

library(SummarizedExperiment)

1. 搜索和下载

TCGAbiolinks提供了一些从GDC下载突变数据（mutation data）的功能。下载数据有两种选择：

1. GDCquery_Maf：下载与hg38基因组对齐的MAF
2. GDCquery，GDCdownload并GDCpreprare：下载与hg19基因组对齐的MAF

1.1. 突变数据（hg38）

这个例子将下载的MAF（突变注释文件）用于variant calling pipeline muse(变异的探索流程muse)。变异的探索流程的选项有：muse，varscan2，somaticsniper，mutect。有关更多信息，请访问GDC文档。

maf <- GDCquery_Maf("CHOL", pipelines = "muse")

# Only first 50 to make render faster
datatable(maf[1:2,],
          filter = 'top',
          options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
          rownames = FALSE)

1.2. 突变数据（hg19）

这个例子将下载与hg19（旧TCGA maf文件）对齐的MAF（突变注释文件）

query.maf.hg19 <- GDCquery(project = "TCGA-CHOL", 
                           data.category = "Simple nucleotide variation", 
                           data.type = "Simple somatic mutation",
                           access = "open", 
                           legacy = TRUE)
# Check maf availables
datatable(dplyr::select(getResults(query.maf.hg19),-contains("cases")),
          filter = 'top',
          options = list(scrollX = TRUE, keys = TRUE, pageLength = 10), 
          rownames = FALSE)

query.maf.hg19 <- GDCquery(project = "TCGA-CHOL", 
                           data.category = "Simple nucleotide variation", 
                           data.type = "Simple somatic mutation",
                           access = "open", 
                           file.type = "bcgsc.ca_CHOL.IlluminaHiSeq_DNASeq.1.somatic.maf",
                           legacy = TRUE)
GDCdownload(query.maf.hg19)
maf <- GDCprepare(query.maf.hg19)
# Only first 50 to make render faster
datatable(maf[1:2,],
          filter = 'top',
          options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
          rownames = FALSE)

2. 可视化数据

要显示数据，您可以使用Bioconductor包maftools。有关详细信息，请查看其插图。

library(maftools)
library(dplyr)
maf <- GDCquery_Maf("CHOL", pipelines = "muse") %>% read.maf

datatable(getSampleSummary(maf),
          filter = 'top',
          options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
          rownames = FALSE)
plotmafSummary(maf = maf, rmOutlier = TRUE, addStat = 'median', dashboard = TRUE)

oncoplot(maf = maf, top = 10, removeNonMutated = TRUE)
titv = titv(maf = maf, plot = FALSE, useSyn = TRUE)
#plot titv summary
plotTiTv(res = titv)

3. 参考

• WGS-variant calling pipeline (变异的探索流程)