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  "Title": "FEAture SelcTion (FEAST) for Single-cell clustering",
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  "Authors@R": "c(person(\"Kenong\",\"Su\", email =\"kenong.su@emory.edu\", role = c(\"aut\",\"cre\")),person(\"Hao\",\"Wu\", email =\"hao.wu@emory.edu\", role =\"aut\"))",
  "Description": "Cell clustering is one of the most important and commonly\nperformed tasks in single-cell RNA sequencing (scRNA-seq) data\nanalysis. An important step in cell clustering is to select a\nsubset of genes (referred to as “features”), whose expression\npatterns will then be used for downstream clustering. A good\nset of features should include the ones that distinguish\ndifferent cell types, and the quality of such set could have\nsignificant impact on the clustering accuracy. FEAST is an R\nlibrary for selecting most representative features before\nperforming the core of scRNA-seq clustering. It can be used as\na plug-in for the etablished clustering algorithms such as SC3,\nTSCAN, SHARP, SIMLR, and Seurat. The core of FEAST algorithm\nincludes three steps: 1. consensus clustering; 2. gene-level\nsignificance inference; 3. validation of an optimized feature\nset.",
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  "Author": "Kenong Su [aut, cre], Hao Wu [aut]",
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    },
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    },
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      "title": "The FEAST User's Guide",
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