数据挖掘：是时候更新一下TCGA的数据了

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")