原文链接：https://mp.weixin.qq.com/s?__biz=MzAxMDkxODM1Ng==&mid=2247486585&idx=1&sn=3035f6420904aad2c8161b362cdeb472&chksm=9b484cc2ac3fc5d479fc5bce3d68d4666b763652a21a55b281aad8c0c4df9b56b4d3b353cc4c&scene=21#wechat_redirect

1.RTCGA相关包的下载及使用？

（1）相关包的下载：（原文中的代码在我的电脑上执行的有些问题，故包的下载代码修改如下：）

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install("RTCGA")
BiocManager::install("RTCGA.clinical")
BiocManager::install("RTCGA.rnaseq")
BiocManager::install("RTCGA.mRNA")
BiocManager::install("RTCGA.mutations")

（2）包的使用代码为：library(包的名称)

2.RTCGA包的工作流程？（图片是从原文中截取的）

3.指定任意基因从任意癌症里面获取芯片表达数据

library(RTCGA)
library(RTCGA.mRNA)
#收集TCGA数据集的表达式(表达量数据)(Gather Expressions for TCGA Datasets)
expr <- expressionsTCGA(BRCA.mRNA, OV.mRNA, LUSC.mRNA,extract.cols = c("GATA3", "PTEN", "XBP1","ESR1", "MUC1"))
#print(expr)
#统计表达式出现的次数
nb_samples <- table(expr$dataset)
print(nb_samples)

结果如下：

观察可得，上述结果标识太过复杂，为了更方便地展示结果，添加代码如下：

#简化一下标识，方便可视化展现
#gsub函数为字符串替换函数
expr$dataset <- gsub(pattern = ".mRNA", replacement = "",  expr$dataset)
#paste0函数为字符串连接函数
expr$bcr_patient_barcode <- paste0(expr$dataset, c(1:590, 1:561, 1:154))
print(expr)

结果如下：

4.绘制指定基因在不同癌症的表达量区别boxplot

library(ggplot2)
library(ggpubr)# load the packages need load ggplot2 packages first
# GATA3,为expr的一列值
ggboxplot(expr, x = "dataset", y = "GATA3",title = "GATA3", ylab = "Expression",color = "dataset", palette = "jco")
# PTEN,为expr的一列值
ggboxplot(expr, x = "dataset", y = "PTEN",title = "PTEN", ylab = "Expression",color = "dataset", palette = "jco")

图形如下：

加入P值（显著性差异）信息：

为了便于观察信息，可以加入其它的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 labelslabel.select = label.select.criteria,       # Select some labels to displayfont.label = list(size = 9, face = "italic"), # label fontrepel = TRUE                                # Avoid label text overplotting
)

效果如下：

有的情况下为了便于观察，可以进行翻转：

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

效果图如下：

5.指定任意基因从任意癌症里面获取测序表达数据

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"))
print(expr)
#
nb_samples <- table(expr$dataset)
#绘图
library(ggpubr)
ggboxplot(expr, x = "dataset", y = "PTEN|5728",title = "ESR1|2099", ylab = "Expression",color = "dataset", palette = "jco")

效果图：

6.用全部的rnaseq的表达数据来做主成分分析

library(RTCGA)
library(RTCGA.rnaseq)#加载此包需先加载RTCGA包
library(dplyr)
expressionsTCGA(BRCA.rnaseq, OV.rnaseq, HNSC.rnaseq) %>%dplyr::rename(cohort = dataset) %>%  filter(substr(bcr_patient_barcode, 14, 15) == "01") -> BRCA.OV.HNSC.rnaseq.cancer
#主成分分析函数
pcaTCGA(BRCA.OV.HNSC.rnaseq.cancer, "cohort") -> pca_plot
plot(pca_plot)

效果图：

7.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)
#主成分分析
#pcaTCGA(rnaseq.5genes.3cancers, "dataset") -> pca_plot
#plot(pca_plot)

8.用突变数据做生存分析

library(RTCGA.mutations)
library(dplyr)
library(survminer)
mutationsTCGA(BRCA.mutations, OV.mutations) %>%filter(Hugo_Symbol == 'TP53') %>%filter(substr(bcr_patient_barcode, 14, 15) =="01") %>% # cancer tissuemutate(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
print(km_plot)

效果图如下：

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

效果图：

10.知识点总结

（1）R语言中的table函数：主要有两个作用：

1°统计每个数据出现的次数。aq：

2°实现混淆矩阵。（ps：暂未理解）

（2）R语言中的gsub函数：R语言中的sub()函数和gsub()函数均为字符串替换函数，两者的区别在于sub()函数只会替换最初匹配到的字符串，且只匹配一次，而gsub()函数会替换掉所有匹配的字符串。gsub()函数的具体用法为gsub(“替换的目标字符串”，"被替换的字符串"，父串)。

（3）R语言中的paste0函数：paste()函数和paste0()函数均为字符串连接函数。区别在于连接的两个字符串之间的符号是否为空。

（4）R语言的ggpubr包中的重要函数。

1°ggboxplot函数：该函数绘制的是箱型图。参数如下：

ggboxplot(data, x, y, combine = FALSE, merge = FALSE, color = "black",fill = "white", palette = NULL, title = NULL, xlab = NULL,ylab = NULL, facet.by = NULL, panel.labs = NULL,short.panel.labs = TRUE, linetype = "solid", size = NULL, width = 0.7,notch = FALSE, select = NULL, remove = NULL, order = NULL,add = "none", add.params = list(), error.plot = "pointrange",label = NULL, font.label = list(size = 11, color = "black"),label.select = NULL, repel = FALSE, label.rectangle = FALSE,ggtheme = theme_pubr(), ...)

2°ggpubr中的两个关键函数：compare_means():做统计分析；stat_compare_means()：自行完成统计分析，并在图中加入显著性信息。

（5）R语言中的dplyr包：https://blog.csdn.net/wltom1985/article/details/54973811（该篇文章中的函数介绍的比较详细）