TCGA的28篇教程- 使用R语言的RTCGA包获取TCGA数据

前些天被TCGA的终结新闻刷屏，但是一直比较忙，还没来得及仔细研读，但是笔记本躺着的一些TCGA教程快发霉了，借此契机好好整理一下吧，预计二十篇左右的笔记

——jimmy

往期目录如下：

使用R语言的cgdsr包获取TCGA数据

第二篇目录

- 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正文

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：

img

这不是简单的一个包，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boxplotlibrary(ggpubr)

## Loading required package: ggplot2

## Loading required package: magrittr

# GATA3

ggboxplot(expr, x = "dataset", y = "GATA3",

title = "GATA3", ylab = "Expression",

color = "dataset", palette = "jco")

img# PTEN

ggboxplot(expr, x = "dataset", y = "PTEN",

title = "PTEN", ylab = "Expression",

color = "dataset", palette = "jco")

img## 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)

img

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

)

img

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)

img

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")

img

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)

img

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)

img

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")

img

细心的同学可以发现，本教程其实里面含有大量的外链，因为微信自身的限制没办法跳转，大家可以去生信技能树论坛查看，谢谢合作哦。

一个R包不仅仅是提供一个数据下载接口，更重要的是里面封装了一些便于使用的统计分析函数。

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