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We\ndeveloped a data-driven approach using a measure of expression\ninvariance to find candidate TREGs in postmortem human brain\nsingle nucleus RNA-seq. This R package implements the method\nfor identifying candidate TREGs from snRNA-seq data.", "License": "Artistic-2.0", "URL": "https://github.com/LieberInstitute/TREG,\nhttp://research.libd.org/TREG/", "BugReports": "https://support.bioconductor.org/t/TREG", "biocViews": "Software, SingleCell, RNASeq, GeneExpression,\nTranscriptomics, Transcription, Sequencing", "Encoding": "UTF-8", "LazyData": "true", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.2.3", "Config/testthat/edition": "3", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "zlib1g-dev", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-04-28 12:57:58 UTC", "RemoteUrl": "https://github.com/bioc/TREG", "RemoteRef": "HEAD", "RemoteSha": "930d109d6887de3e181523ee143983d70b0e70cf", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-10 09:39:38 UTC", "User": "root" }, "Author": "Louise Huuki-Myers [aut, cre] (ORCID:\n),\nLeonardo Collado-Torres [ctb] (ORCID:\n)", "Maintainer": "Louise 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