如何将MAF文件与临床数据相结合来进行临床丰富分析？

Question

我想阅读一份MAF文件列表，并将其与临床数据相结合，然后进行临床丰富分析。

library(maftools)

# Load MAF files (By default, silent mutations are discarded using removeSilent=TRUE; Hence, silent mutations do not need to be subsetted in Question 2)
d <- merge_mafs(lapply(Sys.glob("mafs/Patient*.maf"), read.maf))

# Load sample information
c <- read.table(file="sample-information.tsv", sep="\t", header=T)  

# Combine MAF and sample info
d <- read.maf(maf=d, clinicalData=c)

回溯：

-Reading错误在file.info(文件)中:无效的文件名参数

# Clinical enrichment

response.ce = clinicalEnrichment(maf=d, clinicalFeature="Response")

[.data.table中的错误(getClinicalData(x= maf)、c("Tumor_Sample_Barcode“)、：列(S)未找到:响应)

数据集：https://drive.google.com/file/d/1pX78BUsh__VIVg4tJNChCA5b8h4tkjj-/view

Answer 1

也许先尝试读取示例信息，然后每次在单个maf文件中(即在lapply中)读取时，将clinicalData参数表示为sample_info。如下所示：

# Get the sample information
sample_info = read.table(file="sample-information.tsv", sep="\t", header=T)

# lapply over all the maf file, each indicating clinicalData=sample_info
d = merge_mafs(lapply(dir("mafs/",full.names = T), read.maf, verbose=F, clinicalData=sample_info))

# Get the response.ce
response.ce = clinicalEnrichment(maf = d,clinicalFeature = "Response")