时间变量不是maftools中生存分析的数字答案

【问题标题】：Time variable is not numeric for survival analysing in maftools时间变量不是maftools中生存分析的数字
【发布时间】：2020-06-13 00:07:48
【问题描述】：

我正在尝试对肝癌中的“TTN”基因进行生存分析。我使用 TCGAbiolinks 包下载 LIHC 数据。为了执行生存分析，使用了这个命令：

mafSurvival(maf = laml, genes = 'TTN', time = 'days_to_last_followup', Status = 'Overall_Survival_Status', isTCGA = TRUE)

但它导致错误“时间变量不是数字”！

这是我的代码：

library(maftools)
library(TCGAbiolinks)
library(tidyverse)

maf <- GDCquery_Maf("LIHC", pipelines = "muse")


clin <- GDCquery_clinic("TCGA-LIHC","clinical")

my_data <- as_tibble(clin)

# get column names
col <- colnames(my_data)


# Rename column where names is "Sepal.Length"
names(my_data)[names(my_data) == "bcr_patient_barcode"] <- "Tumor_Sample_Barcode"
names(my_data)[names(my_data) == "submitter_id"] <- "Tumor_Sample_Barcode"
names(my_data)[names(my_data) == "vital_status"] <- "Overall_Survival_Status"

names(my_data)[names(my_data) == "Dead"] <- FALSE
names(my_data)[names(my_data) == "Alive"] <- TRUE


laml = read.maf(
  maf,
  clinicalData = my_data,
  removeDuplicatedVariants = TRUE,
  useAll = TRUE,
  gisticAllLesionsFile = NULL,
  gisticAmpGenesFile = NULL,
  gisticDelGenesFile = NULL,
  gisticScoresFile = NULL,
  cnLevel = "all",
  cnTable = NULL,
  isTCGA = FALSE,
  vc_nonSyn = NULL,
  verbose = TRUE
)

plotmafSummary(maf = laml, rmOutlier = TRUE, addStat = 'median', dashboard = TRUE, titvRaw = FALSE)

#oncoplot for top ten mutated genes.
oncoplot(maf = laml, top = 10)


#####################################Function_mafSurvival##################################


#Survival analysis based on grouping of Gene_name mutation status
mafSurvival(maf = laml, genes = 'TTN', time = 'days_to_last_followup', Status = 'Overall_Survival_Status', isTCGA = TRUE)

我将不胜感激任何帮助，使其适用于使用 maftools 和 TCGAbiolinks 软件包进行生存分析。提前致谢。

【问题讨论】：

标签： r survival-analysis

【解决方案1】：

您必须在对read.maf 的调用中设置isTCGA=TRUE（或在maf 中截断它（maf$Tumor_Sample_Barcode <- substr(maf$Tumor_Sample_Barcode, 1, 12)））。

以下代码有效：

invisible(lapply(c("maftools", "TCGAbiolinks"), require, character.only=TRUE))  
maf <- GDCquery_Maf("LIHC", pipelines = "muse")
clin <- GDCquery_clinic("TCGA-LIHC","clinical")
my_data <- data.frame(clin, stringsAsFactors = FALSE, check.names = FALSE)
colnames(my_data)[colnames(my_data)=="submitter_id"] <-  "Tumor_Sample_Barcode"
colnames(my_data)[colnames(my_data)=="vital_status"] <-  "Overall_Survival_Status"

laml <- read.maf(maf, clinicalData = my_data, isTCGA = TRUE)
#> -Validating
#> -Silent variants: 18306 
#> -Summarizing
#> --Possible FLAGS among top ten genes:
#>   TTN
#>   MUC16
#>   OBSCN
#>   FLG
#> -Processing clinical data
#> -Finished in 3.409s elapsed (4.562s cpu)

mafSurvival(maf = laml, genes = 'TTN', time = 'days_to_last_follow_up', Status = 'Overall_Survival_Status', isTCGA = TRUE)
#> Looking for clinical data in annoatation slot of MAF..
#> Number of mutated samples for given genes:
#> TTN 
#>  79
#> Median survival..
#>     Group medianTime   N
#> 1: Mutant        495  79
#> 2:     WT        602 283
#> Removed 83 samples with NA's
#> Warning in survival::Surv(time = Time, event = Status): Invalid status value,
#> converted to NA

#> Warning in survival::Surv(time = Time, event = Status): Invalid status value,
#> converted to NA

#> Warning in survival::Surv(time = Time, event = Status): Invalid status value,
#> converted to NA

【讨论】：