{"id":50847,"date":"2023-03-15T00:00:00","date_gmt":"2023-03-15T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/ingest-and-query-a-gene-expression-dataset-in-r-with-griddb\/"},"modified":"2025-11-14T07:56:02","modified_gmt":"2025-11-14T15:56:02","slug":"ingest-and-query-a-gene-expression-dataset-in-r-with-griddb","status":"publish","type":"post","link":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/ingest-and-query-a-gene-expression-dataset-in-r-with-griddb\/","title":{"rendered":"GridDB\u3092\u7528\u3044\u305fR\u3067\u306e\u907a\u4f1d\u5b50\u767a\u73fe\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u53d6\u308a\u8fbc\u307f\u3068\u30af\u30a8\u30ea\u30fc"},"content":{"rendered":"

\u306f\u3058\u3081\u306b<\/h2>\n

\u3053\u306e\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u3067\u306f\u3001\u307e\u305aGridDB\u306e\u8a2d\u5b9a\u3068R\u304b\u3089\u306e\u63a5\u7d9a\u306b\u95a2\u3059\u308b\u6ce8\u610f\u70b9\u3092\u8aac\u660e\u3057\u307e\u3059\u3002 \u6b21\u306b\u3001The Cancer Genome Atlas (TCGA) \u304b\u3089\u907a\u4f1d\u5b50\u767a\u73fe\u30c7\u30fc\u30bf\u3092\u53d6\u308a\u8fbc\u307f\u3001dplyr<\/code>\u3092\u4f7f\u3063\u3066GridDB\u30d0\u30c3\u30af\u30a8\u30f3\u30c9\u306b\u554f\u3044\u5408\u308f\u305b\u3001\u7c21\u5358\u306a\u8981\u7d04\u7d71\u8a08\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/p>\n

\u524d\u63d0\u6761\u4ef6<\/h2>\n

\u3053\u3053\u304b\u3089\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u3092\u30af\u30ed\u30fc\u30f3\u3059\u308b\u3053\u3068\u3067\u30d5\u30a9\u30ed\u30fc\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3059\u3002 https:\/\/github.com\/griddbnet\/Blogs\/tree\/gene_analysis<\/p>\n

\n
git clone https:\/\/github.com\/griddbnet\/Blogs.git --branch gene_analysis<\/code><\/pre>\n<\/div>\n
GridDB\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3068\u8d77\u52d5<\/h3>\n
GridDB\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u306b\u306f\u3001\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8<\/a>\u306b\u8a18\u8f09\u3055\u308c\u3066\u3044\u308b\u624b\u9806\u306b\u5f93\u3046\u3060\u3051\u3067\u3059\u3002 Ubuntu\u30de\u30b7\u30f3\u3067\u306f\u3001deb<\/code>\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u3001dpkg -i<\/code>\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u306e\u304c\u6700\u3082\u7c21\u5358\u3067\u3059\u3002 \u305d\u306e\u5f8c\u3001sudo systemctl start gridstore<\/code>\u3067GridDB\u30b5\u30fc\u30d3\u30b9\u3092\u8d77\u52d5\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u306e\u5f8c\u3001\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u3093\u3067\u30af\u30a8\u30ea\u3092\u5b9f\u884c\u3059\u308b\u305f\u3081\u306b\u3001R\u304b\u3089\u30b5\u30fc\u30d3\u30b9\u306b\u63a5\u7d9a\u3059\u308b\u6e96\u5099\u304c\u6574\u3044\u307e\u3057\u305f\u3002<\/p>\n
R\u306e\u4f9d\u5b58\u95a2\u4fc2\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b<\/h3>\n
\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3059\u308b\u306b\u306f\u3001\u3044\u304f\u3064\u304b\u306eR\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u5fc5\u8981\u3067\u3059\uff08\u307e\u3060\u5229\u7528\u53ef\u80fd\u3067\u306a\u3044\u5834\u5408\uff09\u3002\u4ee5\u4e0b\u3067\u306f\u3001\u3053\u306eQuarto\u30de\u30fc\u30af\u30c0\u30a6\u30f3\u6587\u66f8\u3067\u4f7f\u7528\u3055\u308c\u308b\u4f9d\u5b58\u95a2\u4fc2\u3092\u691c\u51fa\u3059\u308b\u305f\u3081\u306b\u3001attachment<\/code>\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n
\n
attachment::att_from_rmd(\"01.Rmd\")<\/code><\/pre>\n<\/div>\n
\u3053\u308c\u3089\u306e\u4f9d\u5b58\u95a2\u4fc2\u306f\u901a\u5e38\u306e install.packages<\/code> \u30e1\u30bd\u30c3\u30c9\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3059\u3002\u305f\u3060\u3057\u3001Bioconductor<\/code> \u30d1\u30c3\u30b1\u30fc\u30b8\u306b\u306f BiocManager::install<\/code> \u304c\u5fc5\u8981\u3067\u3059\u3002<\/p>\n
\n
deps <- attachment::att_from_rmd(\"01.Rmd\")\ninstall.packages(deps)<\/code><\/pre>\n<\/div>\n
\u307e\u305f\u306f\u3001Ubuntu<\/code>\u74b0\u5883\u3067\u306f\uff08\u30af\u30e9\u30a6\u30c9\u30bb\u30c3\u30c8\u30a2\u30c3\u30d7\u3067\u306f\u305d\u3046\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u304c\uff09\u3001Ubuntu<\/code>\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u30de\u30cd\u30fc\u30b8\u30e3\u3092\u4f7f\u3063\u3066\u30b3\u30f3\u30d1\u30a4\u30eb\u6e08\u307f\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u3092\u5165\u624b\u3059\u308b\u306e\u304c\u3088\u308a\u65e9\u3044\u65b9\u6cd5\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002<\/p>\n
\n
# replace `PKG1\/2\/3` with the required package names\nsudo apt install r-cran-PKG1 r-cran-PKG2 r-cran-PKG3 ...<\/code><\/pre>\n<\/div>\n
R\u3092\u30ed\u30fc\u30ab\u30ebGridDB\u30af\u30e9\u30b9\u30bf\u306b\u63a5\u7d9a\u3059\u308b<\/h2>\n
\u4ee5\u524d\u306e\u8a18\u4e8b<\/a>\u3067\u7d39\u4ecb\u3057\u307e\u3057\u305f\u304c\u3001GridDB\u30af\u30e9\u30b9\u30bf\u306b\u63a5\u7d9a\u3059\u308b\u305f\u3081\u306bRJDBC<\/code>\u30d1\u30c3\u30b1\u30fc\u30b8\uff08Java\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u63a5\u7d9a\uff09\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u306b\u3057\u307e\u3059\u3002\u30af\u30e9\u30b9\u30bf\u306f\u30ed\u30fc\u30ab\u30eb\u3067\u52d5\u4f5c\u3057\u3066\u3044\u308b\u306e\u3067\uff08\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3068\u30b9\u30bf\u30fc\u30c8 \u30ac\u30a4\u30c9<\/a>\u306b\u5f93\u3048\u3070\u305d\u3046\u306a\u308a\u307e\u3059\uff09\u3001localhost \u306e IP \u30a2\u30c9\u30ec\u30b9 (127.0.0.1<\/code>) \u3068\u30dd\u30fc\u30c8 20001<\/code> \u3092\u5fc5\u305a\u4f7f\u7528\u3059\u308b\u3088\u3046\u306b\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
\u3053\u306e2\u3064\u306e\u30b9\u30c6\u30c3\u30d7\u3001\u3064\u307e\u308ajdbc<\/code>\u30c9\u30e9\u30a4\u30d0\u3068GridDB\u63a5\u7d9a\u306e\u4f5c\u6210\u3092\u3001connect_to_griddb<\/code>\u3068\u3044\u3046\u95a2\u6570\u306b\u307e\u3068\u3081\u308b\u3068\u4f7f\u3044\u3084\u3059\u304f\u306a\u308b\u306f\u305a\u3067\u3059\u3002RJDBC\u3068<\/code>DBI\u306e\u540d\u524d\u7a7a\u9593\u5168\u4f53\u3092\u8aad\u307f\u8fbc\u3093\u3067\u3044\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u63a2\u7d22\u7684\u306a\u5bfe\u8a71\u30bb\u30c3\u30b7\u30e7\u30f3\u3067\u3042\u308c\u3070\u554f\u984c\u306a\u3044\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u304c\u3001\u3088\u308a\u6df1\u523b\u306a\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u3067\u306f\u3001\u3082\u3061\u308d\u3093 R \u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f5c\u6210\u3057\u3066\u5fc5\u8981\u306a\u95a2\u6570\u3060\u3051\u3092<\/code>importFrom`\u3067\u660e\u793a\u7684\u306b\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\u304b box<\/a> \u306a\u3069\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u4f7f\u3063\u3066\u7279\u5b9a\u306e\u30a4\u30f3\u30dd\u30fc\u30c8\u304c\u3042\u308b\u30e2\u30b8\u30e5\u30fc\u30eb\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/p>\n
\n
suppressPackageStartupMessages({\n  library(RJDBC)\n  library(DBI)\n  library(dplyr)\n  library(tidyr)\n  library(purrr)\n  library(qs)\n})\nconnect_to_griddb <- function() {\n  drv <- JDBC(driverClass = \"com.toshiba.mwcloud.gs.sql.Driver\",\n              # Point this to your gridstore jar\n              classPath = \"~\/src\/jdbc\/bin\/gridstore-jdbc.jar\")\n  \n  conn <-\n    dbConnect(drv,\n              \"jdbc:gs:\/\/127.0.0.1:20001\/myCluster\/public\",\n              \"admin\",\n              \"admin\")\n  \n  return(conn)\n}<\/code><\/pre>\n<\/div>\n
\u30c7\u30fc\u30bf<\/h2>\n
\u3053\u3053\u3067\u306f\u3001The Cancer Genome Atlas Program (TCGA)<\/a> \u3067\u516c\u958b\u3055\u308c\u3066\u3044\u308b\u30d2\u30c8\u306e\u304c\u3093\u7814\u7a76\u304b\u3089\u5f97\u3089\u308c\u305f\u3044\u304f\u3064\u304b\u306e\u907a\u4f1d\u5b50\u767a\u73fe\u30c7\u30fc\u30bf\u3092\u6271\u3044\u307e\u3059\u3002\u7279\u306b\u3001Bioconductor\u30d1\u30c3\u30b1\u30fc\u30b8GSEABenchmarkeR<\/code><\/a>\u306b\u3088\u3063\u3066\u30d1\u30c3\u30b1\u30fc\u30b8\u5316\u3055\u308c\u305f\u3001\u907a\u4f1d\u5b50\u767a\u73fe\u7814\u7a76\u306e\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u306b\u4f7f\u7528\u3059\u308b\u3053\u3068\u3092\u76ee\u7684\u3068\u3057\u305f\u30c7\u30fc\u30bf\u306b\u7126\u70b9\u3092\u5f53\u3066\u307e\u3059\u3002\u30c7\u30fc\u30bf\u306f\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3055\u308c\u3001\u3042\u3089\u304b\u3058\u3081\u77e9\u5f62\u96c6\u5408\u306b\u30d5\u30a9\u30fc\u30de\u30c3\u30c8\u3055\u308c\u3001\u4fdd\u5b58\u3068\u8aad\u307f\u8fbc\u307f\u3092\u9ad8\u901f\u5316\u3059\u308b\u305f\u3081\u306b qs<\/code><\/a> \u30a2\u30fc\u30ab\u30a4\u30d6\u3068\u3057\u3066\u4fdd\u5b58\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u3061\u3087\u3063\u3068\u8997\u3044\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
\n
TCGA <- qs::qread(\"data\/expressions.qs\")\nsapply(TCGA, nrow) |> summary()<\/code><\/pre>\n<\/div>\n
10\u500b\u306e\u764c\u306e\u767a\u73fe\u30c7\u30fc\u30bf\u304c\u3042\u308a\u3001\u5e73\u57471.2M\u306e\u30aa\u30d6\u30b6\u30d9\u30fc\u30b7\u30e7\u30f3\uff08\u884c\uff09\u304c\u3042\u308a\u307e\u3059\u3002\u5404\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f5\u3064\u306e\u5217\u304c\u3042\u308a\u3001<\/p>\n
\n
TCGA[[1]] |> head() |> knitr::kable()<\/code><\/pre>\n<\/div>\n
\u4ee3\u8868\u7684\u306a\u304c\u3093\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n
\n
sapply(TCGA, \"[[\", 1, 5) |> unlist() |> knitr::kable()<\/code><\/pre>\n<\/div>\n
GridDB\u306b\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u3080<\/h2>\n
\u30c7\u30fc\u30bf\u3092\u53d6\u308a\u8fbc\u3080\u305f\u3081\u306b\u3001\u307e\u305a\u3001\u4e0a\u3067\u5b9a\u7fa9\u3057\u305f\u95a2\u6570\u3092\u4f7f\u3063\u3066GridDB\u30af\u30e9\u30b9\u30bf\u3078\u306e\u63a5\u7d9a\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002\u6b21\u306b\u3001\u30c6\u30fc\u30d6\u30eb\u3092\u4f5c\u6210\u3059\u308b\u95a2\u6570\u3068\u3001\u30c6\u30fc\u30d6\u30eb\u306b\u30c7\u30fc\u30bf\u3092\u633f\u5165\u3059\u308b\u95a2\u6570\u3092\u5b9a\u7fa9\u3057\u307e\u3059\u3002 \u3053\u308c\u3089\u306e\u95a2\u6570\u306f\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306e\u30ea\u30b9\u30c8\u3092\u5faa\u74b0\u3055\u305b\u3001R\u306e*apply<\/code>\u95a2\u6570\u30d5\u30a1\u30df\u30ea\u30fc\u3084purrr<\/code>\u306emap_*<\/code>\u95a2\u6570\u30d5\u30a1\u30df\u30ea\u30fc\u3092\u7528\u3044\u3066\u3001\u5404\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u30b7\u30fc\u30e0\u30ec\u30b9\u306b\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u30ed\u30fc\u30c9\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3088\u3046\u306b\u3057\u307e\u3059\u3002<\/p>\n
\n
griddb <- connect_to_griddb()\ncreate_table <- function(table_name) {\n  dbSendUpdate(\n    griddb,\n    sprintf(\n      \"CREATE TABLE IF NOT EXISTS %s (Gene STRING, Sample STRING, Expression INTEGER, Code STRING, Name STRING);\",\n      table_name\n    )\n  )\n}\ninsert_table <- function(conn, name, df, append = TRUE) {\n  for (i in seq_len(nrow(df))) {\n    dbWriteTable(conn, name, df[i, ], append = append)\n  }\n}\n# using `invisible` here to hide non-informative output\ninvisible(lapply(names(TCGA), create_table))\nDBI::dbListTables(griddb)\n# 6 minutes for 10K rows per table. So 6 min for 100K rows\n# 1h for 100K rows per table. So 1h for 1M rows.\ninvisible(pbapply::pblapply(seq_along(TCGA), function(i)\n  insert_table(\n    conn = griddb,\n    name = names(TCGA)[[i]],\n    df = TCGA[[i]][1:100000, ],\n    append = TRUE\n  ))\n)\nDBI::dbDisconnect(griddb)<\/code><\/pre>\n<\/div>\n
\n
griddb <- connect_to_griddb()\niter <- setNames(names(TCGA), names(TCGA))\nmap_dfr(iter, function(x) {\n  griddb |> tbl(x) |> collect() |> nrow()\n}) |> knitr::kable()<\/code><\/pre>\n<\/div>\n
\u30c7\u30fc\u30bf\u5206\u6790<\/h2>\n
\u7c21\u5358\u306a\u7d71\u8a08\u30c7\u30fc\u30bf\u3092\u307e\u3068\u3081\u305f\u3082\u306e\u3067\u3059\u3002\u3053\u306e\u6642\u70b9\u3067\u306f\u751f\u7269\u5b66\u7684\u306b\u610f\u5473\u306e\u3042\u308b\u3082\u306e\u306f\u3042\u308a\u307e\u305b\u3093\u3002\u4e3b\u306a\u30a2\u30a4\u30c7\u30a2\u306f\u3001dplyr<\/code>\u3001tidyr<\/code>\u3001purrr<\/code>\u306a\u3069\u306e\u304a\u306a\u3058\u307f\u306eR\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u3063\u3066\u3001\u9ad8\u901f\u306aGridDB\u30d0\u30c3\u30af\u30a8\u30f3\u30c9\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u554f\u3044\u5408\u308f\u305b\u308b\u3053\u3068\u3067\u3059\u3002 \u4ee5\u4e0b\u306eR\u30b3\u30fc\u30c9\u306f\u3001MariaDB<\/code>\u3084PostgresSQL<\/code>\u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u7528\u306b\u958b\u767a\u3055\u308c\u305f\u3082\u306e\u3067\u3059\u304c\u3001GridDB\u306e\u30bb\u30c3\u30c8\u30a2\u30c3\u30d7\u3067\u3082\u540c\u69d8\u306b\u52d5\u4f5c\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3059\u3002<\/p>\n
\u5e73\u5747\u767a\u73fe\u91cf\u304c2000\u3092\u8d85\u3048\u308b\u304c\u3093\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3054\u3068\u306e\u907a\u4f1d\u5b50<\/h3>\n
\n
high_expression_genes <- purrr::map_dfr(iter, .id = \"Cancer\", \n  function(x) {\n    griddb |>\n      tbl(x) |>\n      group_by(Gene) |>\n      summarise(Mean_expression = mean(Expression, na.rm = TRUE)) |>\n      filter(Mean_expression >= 2000) |> \n      collect()\n  }\n)\ndim(high_expression_genes)\nhead(high_expression_genes) |> knitr::kable()<\/code><\/pre>\n<\/div>\n
\u5e73\u5747\u767a\u73fe\u91cf\u304c300\u672a\u6e80\u3067\u3042\u308b\u304c\u3093\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3054\u3068\u306e\u907a\u4f1d\u5b50<\/h3>\n
\n
low_expression_genes <- purrr::map_dfr(iter, .id = \"Cancer\",\n  function(x) {\n    griddb |>\n      tbl(x) |>\n      group_by(Gene) |>\n      summarise(Mean_expression = mean(Expression, na.rm = TRUE)) |>\n      filter(Mean_expression <= 300) |> \n      collect() \n  }\n)\ndim(low_expression_genes)\ntail(low_expression_genes) |> knitr::kable()<\/code><\/pre>\n<\/div>\n
\u3059\u3079\u3066\u306e\u304c\u3093\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u5171\u901a\u3059\u308b\u907a\u4f1d\u5b50<\/h3>\n
\n
unique_genes_per_dataset <- purrr::map(iter, \n  function(x) {\n    griddb |>\n      tbl(x) |>\n      collect() |>\n      pull(Gene) |> \n      unique()\n  }\n)\nshared_genes <- Reduce(intersect, unique_genes_per_dataset)\nshared_genes<\/code><\/pre>\n<\/div>\n
\u5168\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u5171\u901a\u3059\u308b\u6700\u521d\u306e10\u907a\u4f1d\u5b50\u306e\u3001\u304c\u3093\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3054\u3068\u306e\u767a\u73fe\u91cf\u306e\u4e2d\u592e\u5024<\/h3>\n
\n
first10 <- shared_genes[1:10]\niter2 <- expand.grid(iter, first10, stringsAsFactors = FALSE)\nfirst10_expression <-\n  purrr::map2_dfr(iter2$Var1, iter2$Var2, function(.x, .y) {\n    griddb |>\n      tbl(.x) |>\n      filter(Gene == .y) |> \n      collect() \n  })\nfirst10_expression |>\n  group_by(Code, Name, Gene) |>\n  summarise(Mean_expression = mean(Expression, na.rm = TRUE)) |>\n  ungroup() |> \n  select(-Name) |>\n  pivot_wider(names_from = Code, values_from = Mean_expression) |> \n  knitr::kable()<\/code><\/pre>\n<\/div>\n
\u30af\u30ea\u30fc\u30f3\u30a2\u30c3\u30d7<\/h2>\n
\n
DBI::dbDisconnect(griddb)<\/code><\/pre>\n<\/div>\n
\u7d50\u8ad6<\/h2>\n
\u3053\u306e\u30d3\u30cd\u30c3\u30c8\u3067\u306f\u3001R\u304b\u3089GridDB\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u5229\u7528\u3059\u308b\u65b9\u6cd5\u3092\u7d39\u4ecb\u3057\u307e\u3057\u305f\u300210\u500b\u306e\u30c6\u30fc\u30d6\u30eb\u306b\u3042\u308b100\u4e07\u884c\u306e\u907a\u4f1d\u5b50\u767a\u73fe\u30c7\u30fc\u30bf\u3092\u7d041\u6642\u9593\u3067\u53d6\u308a\u8fbc\u307f\u3001DBI<\/code>\u3084dplyr<\/code>\u3068\u3044\u3063\u305f\u4f7f\u3044\u6163\u308c\u305f\u30c4\u30fc\u30eb\u3092\u4f7f\u3063\u3066\u30b7\u30fc\u30e0\u30ec\u30b9\u306b\u30af\u30a8\u30ea\u3092\u5b9f\u884c\u3057\u307e\u3057\u305f\u3002<\/p>\n
\u4ed8\u9332<\/h2>\n
\u907a\u4f1d\u5b50\u767a\u73fe\u30c7\u30fc\u30bf\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3068\u524d\u51e6\u7406\u306b\u4f7f\u7528\u3057\u305fR \/ Bioconductor<\/code> \u30b3\u30fc\u30c9\u3002<\/p>\n
\n
# This has a lot of dependencies and can take a few minutes to install\n# BiocManager::install(\"GSEABenchmarkeR\")\nlibrary(GSEABenchmarkeR)\ntcga <- loadEData(\"tcga\", nr.datasets = 10)\ncancer_abbreviations <- names(tcga)\ncancer_names <- c(\"BLCA\" = \"Bladder Urothelial Carcinoma\",\n                  \"BRCA\" = \"Breast Invasive Carcinoma\",\n                  \"COAD\" = \"Colon Adenocarcinoma\",\n                  \"HNSC\" = \"Head and Neck Squamous Cell Carcinoma\",\n                  \"KICH\" = \"Kidney Chromophobe\",\n                  \"KIRC\" = \"Kidney Renal Clear Cell Carcinoma\",\n                  \"KIRP\" = \"Kidney Renal Papillary Cell Carcinoma\",\n                  \"LIHC\" = \"Liver Hepatocellular Carcinoma\",\n                  \"LUAD\" = \"Lung Adenocarcinoma\",\n                  \"LUSC\" = \"Lung Squamous Cell Carcinoma\")\nexpressions <- lapply(seq_along(tcga), function(i) {\n  tcga[[i]]@assays@data@listData |> \n    as.data.frame() |> \n    tibble::rownames_to_column(var = \"Gene\") |> \n    tidyr::pivot_longer(cols = -Gene, names_to = \"Sample\", values_to = \"Expression\") |> \n    mutate(cancer = cancer_abbreviations[[i]], cancer_name = cancer_names[[i]])\n})\nqs::qsave(expressions, \"expressions.qs\")<\/code><\/pre>\n<\/div>\n
\u30bb\u30c3\u30b7\u30e7\u30f3\u60c5\u5831<\/h2>\n
\n
sessionInfo()<\/code><\/pre>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
\u306f\u3058\u3081\u306b \u3053\u306e\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u3067\u306f\u3001\u307e\u305aGridDB\u306e\u8a2d\u5b9a\u3068R\u304b\u3089\u306e\u63a5\u7d9a\u306b\u95a2\u3059\u308b\u6ce8\u610f\u70b9\u3092\u8aac\u660e\u3057\u307e\u3059\u3002 \u6b21\u306b\u3001The Cancer Genome Atlas (TCGA) \u304b\u3089\u907a\u4f1d\u5b50\u767a\u73fe\u30c7\u30fc\u30bf\u3092\u53d6\u308a\u8fbc\u307f\u3001dplyr\u3092\u4f7f\u3063\u3066Gr […]<\/p>\n","protected":false},"author":41,"featured_media":49521,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1005],"tags":[],"class_list":["post-50847","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-1005"],"acf":[],"yoast_head":"\nGridDB\u3092\u7528\u3044\u305fR\u3067\u306e\u907a\u4f1d\u5b50\u767a\u73fe\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u53d6\u308a\u8fbc\u307f\u3068\u30af\u30a8\u30ea\u30fc | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" content=\"\u306f\u3058\u3081\u306b \u3053\u306e\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u3067\u306f\u3001\u307e\u305aGridDB\u306e\u8a2d\u5b9a\u3068R\u304b\u3089\u306e\u63a5\u7d9a\u306b\u95a2\u3059\u308b\u6ce8\u610f\u70b9\u3092\u8aac\u660e\u3057\u307e\u3059\u3002 \u6b21\u306b\u3001The Cancer Genome Atlas\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/griddb.net\/ja\/\u672a\u5206\u985e\/ingest-and-query-a-gene-expression-dataset-in-r-with-griddb\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"GridDB\u3092\u7528\u3044\u305fR\u3067\u306e\u907a\u4f1d\u5b50\u767a\u73fe\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u53d6\u308a\u8fbc\u307f\u3068\u30af\u30a8\u30ea\u30fc | GridDB: Open Source Time Series Database for IoT\" \/>\n<meta property=\"og:description\" content=\"\u306f\u3058\u3081\u306b \u3053\u306e\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u3067\u306f\u3001\u307e\u305aGridDB\u306e\u8a2d\u5b9a\u3068R\u304b\u3089\u306e\u63a5\u7d9a\u306b\u95a2\u3059\u308b\u6ce8\u610f\u70b9\u3092\u8aac\u660e\u3057\u307e\u3059\u3002 \u6b21\u306b\u3001The Cancer Genome Atlas\" \/>\n<meta property=\"og:url\" content=\"https:\/\/griddb.net\/ja\/\u672a\u5206\u985e\/ingest-and-query-a-gene-expression-dataset-in-r-with-griddb\/\" \/>\n<meta property=\"og:site_name\" content=\"GridDB: Open Source Time Series Database for IoT\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/griddbcommunity\/\" \/>\n<meta property=\"article:published_time\" content=\"2023-03-15T07:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-11-14T15:56:02+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/griddb.net\/wp-content\/uploads\/2023\/03\/Gene_Expression.png\" \/>\n\t<meta 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\/ Yoast SEO plugin. -->","yoast_head_json":{"title":"GridDB\u3092\u7528\u3044\u305fR\u3067\u306e\u907a\u4f1d\u5b50\u767a\u73fe\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u53d6\u308a\u8fbc\u307f\u3068\u30af\u30a8\u30ea\u30fc | GridDB: Open Source Time Series Database for IoT","description":"\u306f\u3058\u3081\u306b \u3053\u306e\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u3067\u306f\u3001\u307e\u305aGridDB\u306e\u8a2d\u5b9a\u3068R\u304b\u3089\u306e\u63a5\u7d9a\u306b\u95a2\u3059\u308b\u6ce8\u610f\u70b9\u3092\u8aac\u660e\u3057\u307e\u3059\u3002 \u6b21\u306b\u3001The Cancer Genome 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