{ "_id": "6a153e97acfb0bcc41d551ff", "Package": "scGate", "Type": "Package", "Title": "Marker-Based Cell Type Purification for Single-Cell Sequencing\nData", "Version": "1.7.2", "Authors@R": "c(\nperson('Zaoqu', 'Liu',\nemail = 'liuzaoqu@163.com',\nrole = c('aut','cre'),\ncomment = c(ORCID = '0000-0002-0452-742X')),\nperson('Massimo', 'Andreatta',\nemail = 'massimo.andreatta@unige.ch',\nrole = c('aut'),\ncomment = c(ORCID = '0000-0002-8036-2647')),\nperson('Ariel','Berenstein',\nemail = 'arieljberenstein@gmail.com',\nrole = c('aut'),\ncomment = c(ORCID = '0000-0001-8540-5389')),\nperson('Josep','Garnica',\nemail = 'josep.garnicacaparros@unige.ch',\nrole = c('aut')),\nperson('Santiago', 'Carmona',\nemail = 'santiago.carmona@unige.ch',\nrole = c('aut'),\ncomment = c(ORCID = '0000-0002-2495-0671'))\n)", "Description": "A common bioinformatics task in single-cell data analysis\nis to purify a cell type or cell population of interest from\nheterogeneous datasets. 'scGate' automatizes marker-based\npurification of specific cell populations, without requiring\ntraining data or reference gene expression profiles. Briefly,\n'scGate' takes as input: i) a gene expression matrix stored in\na 'Seurat' object and ii) a “gating model” (GM), consisting of\na set of marker genes that define the cell population of\ninterest. The GM can be as simple as a single marker gene, or a\ncombination of positive and negative markers. More complex GMs\ncan be constructed in a hierarchical fashion, akin to gating\nstrategies employed in flow cytometry. 'scGate' evaluates the\nstrength of signature marker expression in each cell using the\nrank-based method 'UCell', and then performs k-nearest neighbor\n(kNN) smoothing by calculating the mean 'UCell' score across\nneighboring cells. kNN-smoothing aims at compensating for the\nlarge degree of sparsity in scRNA-seq data. Finally, a\nuniversal threshold over kNN-smoothed signature scores is\napplied in binary decision trees generated from the\nuser-provided gating model, to annotate cells as either “pure”\nor “impure”, with respect to the cell population of interest.\nSee the related publication Andreatta et al. (2022)\n.", "biocViews": "", "VignetteBuilder": "knitr", "URL": "https://zaoqu-liu.github.io/scGate/,\nhttps://github.com/Zaoqu-Liu/scGate", "BugReports": "https://github.com/Zaoqu-Liu/scGate/issues", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.3", "Config/pak/sysreqs": "cmake libglpk-dev make libicu-dev libpng-dev\nlibuv1-dev libxml2-dev libssl-dev python3 zlib1g-dev", "Repository": "https://zaoqu-liu.r-universe.dev", "Date/Publication": "2026-01-24 04:43:14 UTC", "RemoteUrl": "https://github.com/Zaoqu-Liu/scGate", "RemoteRef": "main", "RemoteSha": "464ffc9b424f70ef6754a3cbe146ed50f61aeffb", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-26 06:26:39 UTC", "User": "root" }, "Author": "Zaoqu Liu [aut, cre] (ORCID: ),\nMassimo Andreatta [aut] (ORCID:\n),\nAriel Berenstein [aut] (ORCID: ),\nJosep Garnica [aut],\nSantiago Carmona [aut] (ORCID: )", "Maintainer": "Zaoqu Liu ", "MD5sum": "07c5676ed1224fbbcb55d7bb60879982", "_user": "zaoqu-liu", 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