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An important step in cell clustering is to select a subset of genes (referred to as \u2018features\u2019), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have a significant impact on the clustering accuracy. All existing scRNA-seq clustering tools include a feature selection step relying on some simple unsupervised feature selection methods, mostly based on the statistical moments of gene-wise expression distributions. In this work, we carefully evaluate the impact of feature selection on cell clustering accuracy. In addition, we develop a feature selection algorithm named FEAture SelecTion (FEAST), which provides more representative features. We apply the method on 12 public scRNA-seq datasets and demonstrate that using features selected by FEAST with existing clustering tools significantly improve the clustering accuracy.<\/jats:p>","DOI":"10.1093\/bib\/bbab034","type":"journal-article","created":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T20:25:56Z","timestamp":1611606356000},"source":"Crossref","is-referenced-by-count":57,"title":["Accurate feature selection improves single-cell RNA-seq cell clustering"],"prefix":"10.1093","volume":"22","author":[{"given":"Kenong","family":"Su","sequence":"first","affiliation":[{"name":"Department of Computer Science, Emory University, Atlanta, GA 30322, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianwei","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Data Science, The Chinese University of Hong Kong, Shenzhen, 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