{ "_id": "6ab72d4399f7aa8c6aa3014d", "Package": "HTGM2D", "Version": "1.1.1", "Date": "2026-02-03", "Title": "Two Dimensional High Throughput 'GoMiner'", "Authors@R": "c(\nperson(\"Barry\", \"Zeeberg\", email = \"barryz2013@gmail.com\",\nrole = c(\"aut\", \"cre\")))", "Maintainer": "Barry Zeeberg ", "Author": "Barry Zeeberg [aut, cre]", "LazyData": "true", "LazyDataCompression": "xz", "Description": "The Gene Ontology (GO) Consortium\n organizes genes into hierarchical\ncategories based on biological process (BP), molecular function\n(MF) and cellular component (CC, i.e., subcellular\nlocalization). Tools such as 'GoMiner' (see Zeeberg, B.R.,\nFeng, W., Wang, G. et al. (2003) )\ncan leverage GO to perform ontological analysis of microarray\nand proteomics studies, typically generating a list of\nsignificant functional categories. Microarray studies are\nusually analyzed with BP, whereas proteomics researchers often\nprefer CC. To capture the benefit of both of those ontologies,\nI developed a two-dimensional version of 'High-Throughput\nGoMiner' ('HTGM2D'). I generate a 2D heat map whose axes are\nany two of BP, MF, or CC, and the value within a picture\nelement of the heat map reflects the Jaccard metric p-value for\nthe number of genes in common for the corresponding pair.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "VignetteBuilder": "knitr", "RoxygenNote": "7.3.3", "Config/testthat/edition": "3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-09-26 02:22:14 UTC", "User": "root" }, "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev zlib1g-dev", "Repository": "https://barryzee.r-universe.dev", "Date/Publication": "2026-02-03 23:00:32 UTC", "RemoteUrl": "https://github.com/cran/HTGM2D", "RemoteRef": "HEAD", "RemoteSha": "860e4a7b390e81039f2b10b3a1595858d4764e9a", "_user": "barryzee", "_type": "src", "_file": "HTGM2D_1.1.1.tar.gz", "_fileid": "https://r2.ropensci.org/199dc9f5cd2b2dcdd8117f410c93b71e6457d98ff41a0692593fa2fde96702df", "_filesize": 1585490, "_sha256": 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