使用R语言的RTCGA包获取TCGA数据

• TCGA数据源
• R包RTCGA的简单介绍
• 首先安装及加载包
• 指定任意基因从任意癌症里面获取芯片表达数据
• 绘制指定基因在不同癌症的表达量区别boxplot
• 更多boxplot参数
• 指定任意基因从任意癌症里面获取测序表达数据
• 用全部的rnaseq的表达数据来做主成分分析
• 用5个基因在3个癌症的表达量做主成分分析
• 用突变数据做生存分析
• 多个基因在多种癌症的表达量热图

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TCGA数据源

众所周知,TCGA数据库是目前最综合全面的癌症病人相关组学数据库，包括的测序数据有：

• DNA Sequencing
• miRNA Sequencing
• Protein Expression
• mRNA Sequencing
• Total RNA Sequencing
• Array-based Expression
• DNA Methylation
• Copy Number

知名的肿瘤研究机构都有着自己的TCGA数据库探索工具，比如：

• Broad Institute FireBrowse portal, The Broad Institute
• cBioPortal for Cancer Genomics, Memorial Sloan-Kettering Cancer Center
• TCGA Batch Effects, MD Anderson Cancer Center
• Regulome Explorer, Institute for Systems Biology
• Next-Generation Clustered Heat Maps, MD Anderson Cancer Center

R包RTCGA的简单介绍

而RTCGA这个包是 Marcin Marcin Kosinski et al. 等人开发的，工作流程如下：

1.png

这不是一个简单的包，而是一系列根据数据类型分离的包，相当于要先下载这些离线数据R包之后再直接从离线数据包里面获取TCGA的所有数据。

作者写了详细的文档： https://rtcga.github.io/RTCGA/index.html

最新的数据版本是2016-01-28，可以加载以下的包：

• RTCGA.mutations.20160128
• RTCGA.rnaseq.20160128
• RTCGA.clinical.20160128
• RTCGA.mRNA.20160128
• RTCGA.miRNASeq.20160128
• RTCGA.RPPA.20160128
• RTCGA.CNV.20160128
• RTCGA.methylation.20160128

旧版本已经可以考虑弃用了，下面是基于 2015-11-01 版本的 TCGA 数据

• RTCGA.mutations
• RTCGA.rnaseq
• RTCGA.clinical
• RTCGA.PANCAN12
• RTCGA.mRNA
• RTCGA.miRNASeq
• RTCGA.RPPA
• RTCGA.CNV
• RTCGA.methylation

这里就介绍如何使用R语言的RTCGA包来获取任意TCGA数据吧。

首先安装及加载包

这里仅仅是测序mRNA表达量数据以及临床信息，所以只需要下载及安装下面的包:

# Load the bioconductor installer. 
source("https://bioconductor.org/biocLite.R")
# Install the main RTCGA package
biocLite("RTCGA")
# Install the clinical and mRNA gene expression data packages
biocLite("RTCGA.clinical") ## 14Mb
biocLite('RTCGA.rnaseq') ##  (612.6 MB)
biocLite("RTCGA.mRNA") ##  (85.0 MB)
biocLite('RTCGA.mutations')  ## (103.8 MB)

安装成功之后就可以加载，可以看到，有些数据包非常大，如果网速不好，下载会很可怕。也可以自己想办法独立下载。

https://bioconductor.org/packages/3.6/data/experiment/src/contrib/RTCGA.rnaseq_20151101.8.0.tar.gz
https://bioconductor.org/packages/3.6/data/experiment/src/contrib/RTCGA.mRNA_1.6.0.tar.gz
https://bioconductor.org/packages/3.6/data/experiment/src/contrib/RTCGA.clinical_20151101.8.0.tar.gz
https://bioconductor.org/packages/3.6/data/experiment/src/contrib/RTCGA.mutations_20151101.8.0.tar.gz

library(RTCGA)

## Welcome to the RTCGA (version: 1.8.0).

all_TCGA_cancers=infoTCGA()
DT::datatable(all_TCGA_cancers)

library(RTCGA.clinical) 
library(RTCGA.mRNA)
 ## ?mRNA
## ?clinical

指定任意基因从任意癌症里面获取芯片表达数据

这里我们拿下面3种癌症做示范：

• Breast invasive carcinoma (BRCA)
• Ovarian serous cystadenocarcinoma (OV)
• Lung squamous cell carcinoma (LUSC)

library(RTCGA)
library(RTCGA.mRNA)
expr <- expressionsTCGA(BRCA.mRNA, OV.mRNA, LUSC.mRNA,
                        extract.cols = c("GATA3", "PTEN", "XBP1","ESR1", "MUC1"))

## Warning in flatten_bindable(dots_values(...)): '.Random.seed' is not an
## integer vector but of type 'NULL', so ignored

expr

## # A tibble: 1,305 x 7
##             bcr_patient_barcode   dataset     GATA3       PTEN      XBP1
##                                                
##  1 TCGA-A1-A0SD-01A-11R-A115-07 BRCA.mRNA  2.870500  1.3613571  2.983333
##  2 TCGA-A1-A0SE-01A-11R-A084-07 BRCA.mRNA  2.166250  0.4283571  2.550833
##  3 TCGA-A1-A0SH-01A-11R-A084-07 BRCA.mRNA  1.323500  1.3056429  3.020417
##  4 TCGA-A1-A0SJ-01A-11R-A084-07 BRCA.mRNA  1.841625  0.8096429  3.131333
##  5 TCGA-A1-A0SK-01A-12R-A084-07 BRCA.mRNA -6.025250  0.2508571 -1.451750
##  6 TCGA-A1-A0SM-01A-11R-A084-07 BRCA.mRNA  1.804500  1.3107857  4.041083
##  7 TCGA-A1-A0SO-01A-22R-A084-07 BRCA.mRNA -4.879250 -0.2369286 -0.724750
##  8 TCGA-A1-A0SP-01A-11R-A084-07 BRCA.mRNA -3.143250 -1.2432143 -1.193083
##  9 TCGA-A2-A04N-01A-11R-A115-07 BRCA.mRNA  2.034000  1.2074286  2.278833
## 10 TCGA-A2-A04P-01A-31R-A034-07 BRCA.mRNA -0.293125  0.2883571 -1.605083
## # ... with 1,295 more rows, and 2 more variables: ESR1 , MUC1 

可以看到我们感兴趣的5个基因在这3种癌症的表达量数据都获取了，但是样本量并不一定是最新的TCGA样本量，如下：

nb_samples <- table(expr$dataset)
nb_samples

## 
## BRCA.mRNA LUSC.mRNA   OV.mRNA 
##       590       154       561

其中要注意的是mRNA并不是rnaseq，两者不太一样，具体样本数量，可以看最前面的表格。

下面简化一下标识，方便可视化展现

expr$dataset <- gsub(pattern = ".mRNA", replacement = "",  expr$dataset)
expr$bcr_patient_barcode <- paste0(expr$dataset, c(1:590, 1:561, 1:154))
expr

## # A tibble: 1,305 x 7
##    bcr_patient_barcode dataset     GATA3       PTEN      XBP1       ESR1
##                                           
##  1               BRCA1    BRCA  2.870500  1.3613571  2.983333  3.0842500
##  2               BRCA2    BRCA  2.166250  0.4283571  2.550833  2.3860000
##  3               BRCA3    BRCA  1.323500  1.3056429  3.020417  0.7912500
##  4               BRCA4    BRCA  1.841625  0.8096429  3.131333  2.4954167
##  5               BRCA5    BRCA -6.025250  0.2508571 -1.451750 -4.8606667
##  6               BRCA6    BRCA  1.804500  1.3107857  4.041083  2.7970000
##  7               BRCA7    BRCA -4.879250 -0.2369286 -0.724750 -4.4860833
##  8               BRCA8    BRCA -3.143250 -1.2432143 -1.193083 -1.6274167
##  9               BRCA9    BRCA  2.034000  1.2074286  2.278833  4.1155833
## 10              BRCA10    BRCA -0.293125  0.2883571 -1.605083  0.4731667
## # ... with 1,295 more rows, and 1 more variables: MUC1 

绘制指定基因在不同癌症的表达量区别boxplot

library(ggpubr)

## Loading required package: ggplot2

## Loading required package: magrittr

# GATA3
ggboxplot(expr, x = "dataset", y = "GATA3",
          title = "GATA3", ylab = "Expression",
          color = "dataset", palette = "jco")

2.png

# PTEN
ggboxplot(expr, x = "dataset", y = "PTEN",
          title = "PTEN", ylab = "Expression",
          color = "dataset", palette = "jco")

3.png

## 注意这个配色可以自选的： RColorBrewer::display.brewer.all()  

这里选择的是 ggsci 包的配色方案，包括： “npg”, “aaas”, “lancet”, “jco”, “ucscgb”, “uchicago”, “simpsons” and “rickandmorty”，针对常见的SCI杂志的需求开发的。

还可以加上P值信息

my_comparisons <- list(c("BRCA", "OV"), c("OV", "LUSC"))
ggboxplot(expr, x = "dataset", y = "GATA3",
          title = "GATA3", ylab = "Expression",
          color = "dataset", palette = "jco")+
  stat_compare_means(comparisons = my_comparisons)

4.png

这些统计学检验，也是被包装成了函数：

compare_means(c(GATA3, PTEN, XBP1) ~ dataset, data = expr)

## # A tibble: 9 x 8
##      .y. group1 group2             p         p.adj p.format p.signif
##                                 
## 1  GATA3   BRCA     OV 1.111768e-177 3.335304e-177  < 2e-16     ****
## 2  GATA3   BRCA   LUSC  6.684016e-73  1.336803e-72  < 2e-16     ****
## 3  GATA3     OV   LUSC  2.965702e-08  2.965702e-08  3.0e-08     ****
## 4   PTEN   BRCA     OV  6.791940e-05  6.791940e-05  6.8e-05     ****
## 5   PTEN   BRCA   LUSC  1.042830e-16  3.128489e-16  < 2e-16     ****
## 6   PTEN     OV   LUSC  1.280576e-07  2.561153e-07  1.3e-07     ****
## 7   XBP1   BRCA     OV 2.551228e-123 7.653685e-123  < 2e-16     ****
## 8   XBP1   BRCA   LUSC  1.950162e-42  3.900324e-42  < 2e-16     ****
## 9   XBP1     OV   LUSC  4.239570e-11  4.239570e-11  4.2e-11     ****
## # ... with 1 more variables: method 

更多boxplot参数

label.select.criteria <- list(criteria = "`y` > 3.9 & `x` %in% c('BRCA', 'OV')")
ggboxplot(expr, x = "dataset",
          y = c("GATA3", "PTEN", "XBP1"),
          combine = TRUE,
          color = "dataset", palette = "jco",
          ylab = "Expression", 
          label = "bcr_patient_barcode",              # column containing point labels
          label.select = label.select.criteria,       # Select some labels to display
          font.label = list(size = 9, face = "italic"), # label font
          repel = TRUE                                # Avoid label text overplotting
          )

5.png

其中 combine = TRUE 会把多个boxplot并排画在一起，其实没有ggplot自带的分面好用。

还可以使用 merge = TRUE or merge = “asis” or merge = "flip" 来把多个boxplot 合并，效果不一样。

还有翻转，如下：

ggboxplot(expr, x = "dataset", y = "GATA3",
          title = "GATA3", ylab = "Expression",
          color = "dataset", palette = "jco",
          rotate = TRUE)

6.png

更多可视化详见： http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/77-facilitating-exploratory-data-visualization-application-to-tcga-genomic-data/

指定任意基因从任意癌症里面获取测序表达数据

还是同样的3种癌症和5个基因做示范，这个时候的基因ID稍微有点麻烦，不仅仅是要symbol还要entrez的ID，具体需要看 https://wiki.nci.nih.gov/display/TCGA/RNASeq+Version+2 的解释

如下：

library(RTCGA)
library(RTCGA.rnaseq)
expr <- expressionsTCGA(BRCA.rnaseq, OV.rnaseq, LUSC.rnaseq,
                        extract.cols = c("GATA3|2625", "PTEN|5728", "XBP1|7494","ESR1|2099", "MUC1|4582"))
expr

## # A tibble: 2,071 x 7
##             bcr_patient_barcode     dataset `GATA3|2625` `PTEN|5728`
##                                                 
##  1 TCGA-3C-AAAU-01A-11R-A41B-07 BRCA.rnaseq   14337.4623    1724.328
##  2 TCGA-3C-AALI-01A-11R-A41B-07 BRCA.rnaseq    7437.7379    1106.580
##  3 TCGA-3C-AALJ-01A-31R-A41B-07 BRCA.rnaseq   10252.9465    1478.695
##  4 TCGA-3C-AALK-01A-11R-A41B-07 BRCA.rnaseq    8761.6880    1877.120
##  5 TCGA-4H-AAAK-01A-12R-A41B-07 BRCA.rnaseq   14068.5106    1739.574
##  6 TCGA-5L-AAT0-01A-12R-A41B-07 BRCA.rnaseq   16511.5120    1596.715
##  7 TCGA-5L-AAT1-01A-12R-A41B-07 BRCA.rnaseq    6721.2714    1374.083
##  8 TCGA-5T-A9QA-01A-11R-A41B-07 BRCA.rnaseq   13485.3556    2181.485
##  9 TCGA-A1-A0SB-01A-11R-A144-07 BRCA.rnaseq     601.4191    2529.114
## 10 TCGA-A1-A0SD-01A-11R-A115-07 BRCA.rnaseq   12982.8798    1875.775
## # ... with 2,061 more rows, and 3 more variables: `XBP1|7494` ,
## #   `ESR1|2099` , `MUC1|4582` 

nb_samples <- table(expr$dataset)
nb_samples

## 
## BRCA.rnaseq LUSC.rnaseq   OV.rnaseq 
##        1212         552         307

library(ggpubr)
# ESR1|2099
ggboxplot(expr, x = "dataset", y = "`PTEN|5728`",
          title = "ESR1|2099", ylab = "Expression",
          color = "dataset", palette = "jco")

7.png

更多可视化见：http://rtcga.github.io/RTCGA/articles/Visualizations.html

用全部的rnaseq的表达数据来做主成分分析

## RNASeq expressions
library(RTCGA.rnaseq)
library(dplyr)  

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

expressionsTCGA(BRCA.rnaseq, OV.rnaseq, HNSC.rnaseq) %>%
   dplyr::rename(cohort = dataset) %>%  
   filter(substr(bcr_patient_barcode, 14, 15) == "01") -> BRCA.OV.HNSC.rnaseq.cancer

pcaTCGA(BRCA.OV.HNSC.rnaseq.cancer, "cohort") -> pca_plot

plot(pca_plot)

8.png

因为是全部的表达数据，所以非常耗时，但是可以很明显看到乳腺癌和卵巢癌关系要近一点，头颈癌症就要远一点。

用5个基因在3个癌症的表达量做主成分分析

expr %>%  
   filter(substr(bcr_patient_barcode, 14, 15) == "01") -> rnaseq.5genes.3cancers
DT::datatable(rnaseq.5genes.3cancers)

#pcaTCGA(rnaseq.5genes.3cancers, "dataset") -> pca_plot

#plot(pca_plot)

该包里面的pcaTCGA函数不好用，其实可以自己做PCA分析。

用突变数据做生存分析

library(RTCGA.mutations)
# library(dplyr) if did not load at start
library(survminer)
mutationsTCGA(BRCA.mutations, OV.mutations) %>%
   filter(Hugo_Symbol == 'TP53') %>%
   filter(substr(bcr_patient_barcode, 14, 15) ==
   "01") %>% # cancer tissue
   mutate(bcr_patient_barcode =
   substr(bcr_patient_barcode, 1, 12)) ->
  BRCA_OV.mutations
 
library(RTCGA.clinical)
survivalTCGA(
  BRCA.clinical,
  OV.clinical,
  extract.cols = "admin.disease_code"
  ) %>%
   dplyr::rename(disease = admin.disease_code) ->
  BRCA_OV.clinical

BRCA_OV.clinical %>%
   left_join(
     BRCA_OV.mutations,
     by = "bcr_patient_barcode"
     ) %>%
   mutate(TP53 =
   ifelse(!is.na(Variant_Classification), "Mut","WILDorNOINFO")) ->
  BRCA_OV.clinical_mutations
 
BRCA_OV.clinical_mutations %>%
select(times, patient.vital_status, disease, TP53) -> BRCA_OV.2plot

kmTCGA(
  BRCA_OV.2plot,
  explanatory.names = c("TP53", "disease"),
  break.time.by = 400,
  xlim = c(0,2000),
  pval = TRUE) -> km_plot

## Scale for 'colour' is already present. Adding another scale for
## 'colour', which will replace the existing scale.

## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.

print(km_plot)

9.png

多个基因在多种癌症的表达量热图

library(RTCGA.rnaseq)
# perfrom plot
# library(dplyr) if did not load at start
expressionsTCGA(
  ACC.rnaseq,
  BLCA.rnaseq,
  BRCA.rnaseq,
  OV.rnaseq,
  extract.cols = 
    c("MET|4233",
    "ZNF500|26048",
    "ZNF501|115560")
  ) %>%
  dplyr::rename(cohort = dataset,
  MET = `MET|4233`) %>%
  #cancer samples
  filter(substr(bcr_patient_barcode, 14, 15) ==
  "01") %>%
  mutate(MET = cut(MET,
   round(quantile(MET, probs = seq(0,1,0.25)), -2),
   include.lowest = TRUE,
   dig.lab = 5)) -> ACC_BLCA_BRCA_OV.rnaseq

ACC_BLCA_BRCA_OV.rnaseq %>%
  select(-bcr_patient_barcode) %>%
  group_by(cohort, MET) %>%
  summarise_each(funs(median)) %>%
  mutate(ZNF500 = round(`ZNF500|26048`),
  ZNF501 = round(`ZNF501|115560`)) ->
  ACC_BLCA_BRCA_OV.rnaseq.medians

## `summarise_each()` is deprecated.
## Use `summarise_all()`, `summarise_at()` or `summarise_if()` instead.
## To map `funs` over all variables, use `summarise_all()`

heatmapTCGA(ACC_BLCA_BRCA_OV.rnaseq.medians,
  "cohort", "MET", "ZNF500",
  title = "Heatmap of ZNF500 expression")

10.png