大家好，我是生信技能树学徒，前面我们带来了大量的表达数据挖掘实战演练，但是TCGA数据库之丰富程度，值得我们花费多年时间继续探索，现在带来的是突变全景图，如果你对之前的教程感兴趣，可以点击学习菜鸟团（周一数据挖掘专栏）成果展
就是上面这张全景，我重复出来的是下面这个样子。
文章
标题: Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma
链接: https://www.cell.com/cell/fulltext/S0092-8674(17)30639-6
数据准备
绘制全景图需要maf格式的突变信息文件以及临床信息文件。
还是从XENA上进行下载
需要注意，这里储存突变信息的文件需要是maf格式，和我们之前根据是否存在该基因的突变对样本进行分类的文件不同。
处理数据-R-maftools
1. 读取临床信息
tumor_type <-"LIHC"
Rdata_file <- paste('./data/', tumor_type,'.phenoData.Rdata', sep ='')
if(!file.exists( Rdata_file )) {
phenoData <- read.table( destfile,
header =T,
sep ='\t',
quote ='')
rownames( phenoData ) <- phenoData[ ,1]
colnames( phenoData )[1] <-"Tumor_Sample_Barcode"
phenoData[1:5,1:5]
save( phenoData, file = Rdata_file )
}else{
load( Rdata_file )
}
这里是遇到的第一个坑：我们看一下临床信息的“Tumor_Sample_Barcode”，是16位的短ID，但是后来在使用read.maf读取maf文件时，发现下载的maf文件的“Tumor_Sample_Barcode”是长ID，就存在了两个ID不匹配，从而导致临床信息被直接略过了。我去github上翻看了一下作者的代码，read.maf也可以接受数据框。所以就把maf文件先读取进来，处理一下ID。
2. 读取maf文件
maf <-data.table::as.data.table(read.csv(file ="./raw_data/TCGA.LIHC.mutect.DR-10.0.somatic.maf.gz",
header =TRUE, sep ='\t',
stringsAsFactors =FALSE, comment.char ="#"))
maf$Tumor_Sample_Barcode <- substr(maf$Tumor_Sample_Barcode,1,16)
require(maftools)
## 作者用到了HBV和HCV的临床信息
phenoData$HBV <- ifelse(phenoData$hist_hepato_carc_fact =='Hepatitis B','HBV','others')
phenoData$HCV <- ifelse(phenoData$hist_hepato_carc_fact =='Hepatitis C','HCV','others')
phenoData[phenoData$neoplasm_histologic_grade ==""] <-'no_reported'
## 这个函数不强求直接读取文本文件，也可以读取数据变量
laml <-read.maf(maf, clinicalData =phenoData)
laml
laml@data <- laml@data[grepl('PASS', laml@data$FILTER), ]
接下来绘图遇到了第二个坑，关于factor的问题，以及颜色的对应关系的列表如何制作，绘图的函数怎么调用颜色信息。
3. 绘图
library(RColorBrewer)
png(paste0('oncoplot_top26_phone', tumor_type,'.png'), res =150,
width =1500, height =1080)
## 文章中这些driver gene是Mutsig挑选出来的，文章里面提供了，就直接使用了这个数据
genes = c("TP53","CTNNB1","ALB","AXIN1","BAP1","KEAP1","NFE2L2","LZTR1","RB1","PIK3CA","RPS6KA3","AZIN1","KRAS","IL6ST","RP1L1","CDKN2A","EEF1A1","ARID2","ARID1A","GPATCH4","ACVR2A","APOB","CREB3L3","NRAS","AHCTF1","HIST1H1C")
## 为突变类型的分类数据设置颜色
variantClass <- names(table(laml@data$Variant_Classification))
col = c(RColorBrewer::brewer.pal(n =4, name ='Set1'),
RColorBrewer::brewer.pal(n =5, name ='Set2'))
names(col) = variantClass
col
## 绘图的时候我们使用的数据是laml，临床信息在clinical.data里面
## 绘图函数要求这些设置颜色的数据是factor，所以我们要把加到图上的
## 临床信息转变为因子
laml@clinical.data$neoplasm_histologic_grade <-
as.factor(laml@clinical.data$neoplasm_histologic_grade)
gradecolors = RColorBrewer::brewer.pal(n =4,name ='Spectral')
names(gradecolors) = levels(laml@clinical.data$neoplasm_histologic_grade)
laml@clinical.data$race.demographic <-
as.factor(laml@clinical.data$race.demographic)
Racecolors = RColorBrewer::brewer.pal(n =5,name ='Spectral')
names(Racecolors) = levels(laml@clinical.data$race.demographic)
laml@clinical.data$gender.demographic <-
as.factor(laml@clinical.data$gender.demographic)
Gendercolors = c("#b3e2cd","#fb9a99")
names(Gendercolors) = levels(laml@clinical.data$gender.demographic)
laml@clinical.data$HBV <-
as.factor(laml@clinical.data$HBV)
HBVcolors = c("#ffffb3","#e31a1c")
names(HBVcolors) = levels(laml@clinical.data$HBV)
laml@clinical.data$HCV <-
as.factor(laml@clinical.data$HCV)
HCVcolors = c("#1b9e77","#fc8d62")
names(HCVcolors) = levels(laml@clinical.data$HCV)
## 绘图函数需要一个list
phecolors = list(neoplasm_histologic_grade = gradecolors,
race.demographic = Racecolors,
gender.demographic = Gendercolors,
HBV = HBVcolors,
HCV = HCVcolors)
## clinicalFeatures是从laml@clinical.data里面挑取数据，所以
## 一定要是laml@clinical.data里面的列名
oncoplot(maf = laml,
colors = col,
bgCol ="#ebebeb", borderCol ="#ebebeb",
genes = genes, GeneOrderSort =F, keepGeneOrder =T,
fontSize =7, legendFontSize =7,
annotationFontSize =7,
annotationTitleFontSize =7,
sortByMutation =T,
showTumorSampleBarcodes =F,
annotationColor = phecolors,
clinicalFeatures = c("neoplasm_histologic_grade",
"race.demographic",
"gender.demographic",
"HBV",
"HCV"))
dev.off()
