> merge_maf <- function(metadata, path){
#通过合并path,还有sample sheet前两列得到每一个文件的完整路径
filenames <- file.path(path, metadatafile_id, metadatafile_name,
fsep = .Platform$file.sep)
message ('############### Merging maf data ################\n',
'### This step may take a few minutes ###\n')
#通过lapply循环去读每一个样本的maf，然后通过rbind合并成矩阵，按行来合并
#colClasses指定所有列为字符串
mafMatrix <- do.call("rbind", lapply(filenames, function(fl)
read.table(gzfile(fl),header=T,sep="\t",quote="",fill=T,colClasses="character")))
return (mafMatrix)
}
#定义去除重复样本的函数FilterDuplicate
> FilterDuplicate <- function(metadata) {
filter <- which(duplicated(metadata[,'sample']))
if (length(filter) != 0) {
metadata <- metadata[-filter,]
}
message (paste('Removed', length(filter), 'samples', sep=' '))
return (metadata)
}
#读入maf的sample sheet文件
> metaMatrix.maf=read.table("maf_sample_sheet.tsv",sep="\t",header=T)
#替换.为下划线，转换成小写，sample_id替换成sample
>names(metaMatrix.maf)=gsub("sample_id","sample",gsub("\\.","_",tolower(names(metaMatrix.maf))))
#删掉最后一列sample_type中的空格
> metaMatrix.mafsample_type=gsub(" ","",metaMatrix.mafsample_type)
#删掉重复的样本
> metaMatrix.maf <- FilterDuplicate(metaMatrix.maf)
#调用merge_maf函数合并maf的矩阵
> maf_value=merge_maf(metadata=metaMatrix.maf,
path="maf_data"
)
|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| > #查看前三行前十列 > maf_value[1:3,1:10] Hugo_Symbol Entrez_Gene_Id Center NCBI_Build Chromosome Start_Position End_Position Strand Variant_Classification Variant_Type 1 PLOD1 5351 WUGSC GRCh38 chr1 11957858 11957858 + Missense_Mutation SNP 2 IVL 3713 WUGSC GRCh38 chr1 152910652 152910652 + Nonsense_Mutation SNP 3 OBSCN 84033 WUGSC GRCh38 chr1 228256740 228256740 + Missense_Mutation SNP |
#保存合并后的maf文件
> write.table(file="combined_maf_value.txt",maf_value,row.names=F,quote=F,sep="\t")
#TMB打分
> BiocManager::install("maftools")
> library(maftools)
> laml <- read.maf(maf = "combined_maf_value.txt")
> tmb_table_wt_log = tmb(maf = laml)
#tmb_table_wt_log = tmb(maf = laml)： 这行代码调用了 tmb() 函数，计算了基于 MAF 数据的肿瘤突变负荷（TMB）。参数 maf 接受了之前读取的 laml 数据框作为输入，然后将结果赋值给了 tmb_table_wt_log 变量。
write.table(tmb_table_wt_log,file="TMB_log.txt",sep="\t",row.names=F)
#突变负荷分析
> library(limma)
> library(ggplot2)
#install.packages("ggpubr")
> library(ggpubr)
#install.packages("ggExtra")
> library(ggExtra)
> expFile="geneExp.txt"
> tmbFile="TMB.txt"
> rt=read.table(expFile, header=T, sep="\t", check.names=F, row.names=1)
> gene=colnames(rt)[1]
> tumorData=rt[rt$Type=="Tumor",1,drop=F]
> tumorData=as.matrix(tumorData)
> rownames(tumorData)=gsub("(.*?)\\-(.*?)\\-(.*?)\\-.*", "\\1\\-\\2\\-\\3\\", rownames(tumorData))
> data=avereps(tumorData)
> tmb=read.table(tmbFile, header=T, sep="\t", check.names=F, row.names=1)
> rownames(tmb)=gsub("(.*?)\\-(.*?)\\-(.*?)\\-.*", "\\1\\-\\2\\-\\3\\", rownames(tmb))
> tmb=avereps(tmb)
> sameSample=intersect(row.names(data), row.names(tmb))
> data=data[sameSample,,drop=F]
> tmb=tmb[sameSample,,drop=F]
> rt=cbind(data, tmb)
> x=as.numeric(rt[,gene])
> y=log2(as.numeric(rt[,"total_perMB_log"])+1)
> df1=as.data.frame(cbind(x,y))
> corT=cor.test(x, y, method="spearman")
> p1=ggplot(df1, aes(x, y)) +
xlab(paste0(gene, " expression"))+ylab("Tumor mutation burden")+
geom_point()+ geom_smooth(method="lm",formula = y ~ x) + theme_bw()+
stat_cor(method = 'spearman', aes(x =x, y =y))
p1
> p2=ggMarginal(p1, type = "density", xparams = list(fill = "orange"),yparams = list(fill = "blue"))
> p2
学习交流