请写出TCGA中LUSC临床数据的下载与处理的完整R语言代码，提取生存数据，TNM分期数据等，越详细越好

好的，以下是TCGA中LUSC临床数据的下载与处理的完整R语言代码，包括了提取生存数据和TNM分期数据的步骤：

1. 下载TCGA数据

library(TCGAbiolinks)
query <- GDCquery(project = "TCGA-LUSC", 
                   data.category = "Clinical",
                   barcode = NULL)
GDCdownload(query)

2. 读取临床数据

clinical_data <- GDCprepare_clinic(query)

3. 数据清洗

# 删除NA值
clinical_data <- clinical_data[, colSums(is.na(clinical_data)) == 0]

# 删除重复列
clinical_data <- clinical_data[, !duplicated(colnames(clinical_data))]

# 选择需要的列
clinical_data <- clinical_data[, c("bcr_patient_barcode", "age_at_initial_pathologic_diagnosis", "gender", "race", "vital_status", "days_to_death", "days_to_last_followup", "pathologic_T", "pathologic_N", "pathologic_M")]

# 调整列名
colnames(clinical_data) <- c("patient_id", "age", "sex", "race", "vital_status", "days_to_death", "days_to_last_followup", "T", "N", "M")

# 转换变量类型
clinical_data$age <- as.numeric(clinical_data$age)
clinical_data$sex <- as.factor(clinical_data$sex)
clinical_data$race <- as.factor(clinical_data$race)

4. 提取生存数据

# 删除vital_status为NA的行
clinical_data <- clinical_data[!is.na(clinical_data$vital_status),]

# 转换vital_status为生存状态
clinical_data$vital_status <- ifelse(clinical_data$vital_status == "Dead", 1, 0)

# 计算生存时间
clinical_data$survival_time <- ifelse(clinical_data$vital_status == 0, clinical_data$days_to_last_followup, clinical_data$days_to_death)

# 删除days_to_death和days_to_last_followup列
clinical_data <- clinical_data[, !grepl("^days_to_", colnames(clinical_data))]

5. 提取TNM分期数据

# 合并T、N、M列为一个新列TNM
clinical_data$TNM <- paste(clinical_data$T, clinical_data$N, clinical_data$M, sep = "")

# 删除T、N、M列
clinical_data <- clinical_data[, !grepl("^pathologic_[T|N|M]", colnames(clinical_data))]

以上就是TCGA中LUSC临床数据的下载与处理的完整R语言代码，其中包括了数据下载、读取、清洗、生存数据和TNM分期数据的提取等步骤，代码中也包含了注释，希望对你有所帮助。