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  "Type": "Package",
  "Title": "Rank Constrained Similarity Learning for single cell RNA\nsequencing data",
  "Version": "1.21.0",
  "Date": "2021-04-01",
  "Authors@R": "c(\nperson(given = \"Qinglin\", family = \"Mei\", email = \"meiqinglinkf@163.com\", role = c(\"cre\",\"aut\")),\nperson(given = \"Guojun\", family = \"Li\", email = \"guojunsdu@gmail.com\", role = \"fnd\"),\nperson(given = \"Zhengchang\", family = \"Su\", email = \"zcsu@uncc.edu\", role = \"fnd\"))",
  "Maintainer": "Qinglin Mei <meiqinglinkf@163.com>",
  "Description": "A novel clustering algorithm and toolkit RCSL (Rank\nConstrained Similarity Learning) to accurately identify various\ncell types using scRNA-seq data from a complex tissue. RCSL\nconsiders both lo-cal similarity and global similarity among\nthe cells to discern the subtle differences among cells of the\nsame type as well as larger differences among cells of\ndifferent types. RCSL uses Spearman’s rank correlations of a\ncell’s expression vector with those of other cells to measure\nits global similar-ity, and adaptively learns neighbour\nrepresentation of a cell as its local similarity. The overall\nsimilar-ity of a cell to other cells is a linear combination of\nits global similarity and local similarity.",
  "URL": "https://github.com/QinglinMei/RCSL",
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  "Author": "Qinglin Mei [cre, aut], Guojun Li [fnd], Zhengchang Su [fnd]",
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      "engine": "knitr::rmarkdown",
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