TCGA在去年更新之后提供了Count、TPM、FPKM三种格式的mRNA表达量数据，同时提供了ensembl gene ID、基因名、基因类型，因此有必要更新一下数据了。
安装需要的R包
install.packages("tidyverse")
install.packages("arrow")
install.packages("data.table")
install.packages("magrittr")
install.packages("pacman")
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("BioinformaticsFMRP/TCGAbiolinksGUI.data")
BiocManager::install("BioinformaticsFMRP/TCGAbiolinks")
TCGA数据版本信息
rm(list = ls())
library(pacman)
p_load(magrittr, tidyverse, TCGAbiolinks, data.table, arrow)
TCGAbiolinks::getGDCInfo()
# $commit
# [1] "4dd3680528a19ed33cfc83c7d049426c97bb903b"
# $data_release
# [1] "Data Release 36.0 - December 12, 2022"
# $status
# [1] "OK"
# $tag
# [1] "3.0.0"
# $version
# [1] 1
建几个文件夹
mkdir mRNA miRNA SNV CNV Protein
需要下载的数据
gdc_projects <- TCGAbiolinks::getGDCprojects() %>%
pull(id) %>%
grep(pattern = "^TCGA", x = ., value = T) %>%
str_remove("TCGA-")
gdc_projects
# [1] "CHOL" "LIHC" "DLBC" "BLCA" "ACC" "CESC" "PCPG" "PAAD" "MESO" "TGCT"
# [11] "KIRP" "UVM" "UCS" "THYM" "COAD" "ESCA" "GBM" "KICH" "HNSC" "PRAD"
# [21] "OV" "LUSC" "LAML" "LGG" "SARC" "BRCA" "READ" "LUAD" "STAD" "THCA"
# [31] "KIRC" "SKCM" "UCEC"
下载mRNA表达量数据
downRNA <- function(cancer) {
query <- TCGAbiolinks::GDCquery(
project = paste0("TCGA-", cancer),
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "STAR - Counts",
legacy = FALSE
)
TCGAbiolinks::GDCdownload(query, files.per.chunk = 50)
data <- TCGAbiolinks::GDCprepare(query, summarizedExperiment = F)
data %<>% dplyr::filter(str_detect(gene_id, "^EN"))
dt <- data %>% dplyr::select(gene_id, gene_name, gene_type, starts_with("unstranded"), starts_with("tpm"), starts_with("fpkm_unstranded"))
colnames(dt) %<>% str_remove("_unstranded") %>% str_replace("unstranded", "count")
arrow::write_ipc_file(dt, str_glue("mRNA/TCGA_{cancer}_mRNA.arrow", compression = "zstd", compression_level = 1))
return(NULL)
}
walk(gdc_projects, downRNA)
下载其他几种数据的函数
download <- function(
cancer,
folder_name,
data_category = FALSE,
data_type = FALSE,
workflow_type = FALSE,
experimental_strategy = FALSE,
legacy = FALSE) {
query <- TCGAbiolinks::GDCquery(
project = paste0("TCGA-", cancer),
data.category = data_category,
data.type = data_type,
experimental.strategy = experimental_strategy,
workflow.type = workflow_type,
legacy = legacy
)
TCGAbiolinks::GDCdownload(query, files.per.chunk = 50)
TCGAbiolinks::GDCprepare(query = query, summarizedExperiment = FALSE) %>%
arrow::write_ipc_file(., str_glue("{folder_name}/TCGA_{cancer}_{folder_name}.arrow", compression = "zstd", compression_level = 1))
}
下载microRNA表达量数据
walk(gdc_projects, download, folder_name = "miRNA", data_category = "Transcriptome Profiling", data_type = "miRNA Expression Quantification", experimental_strategy = "miRNA-Seq")
下载SNV数据
walk(gdc_projects, download, folder_name = "SNV", data_category = "Simple Nucleotide Variation", data_type = "Masked Somatic Mutation")
下载CNV 数据
walk(gdc_projects, download, folder_name = "CNV", data_category = "Copy Number Variation", data_type = "Masked Copy Number Segment")
下载蛋白表达量数据
walk(gdc_projects, download, folder_name = "Protein", data_category = "Proteome Profiling", data_type = "Protein Expression Quantification”）
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