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However, most computational methods still stick with the old mentality of viewing differential expression as a simple \u2018up or down\u2019 phenomenon. We advocate that we should fully embrace the features of single cell data, which allows us to observe binary (from Off to On) as well as continuous (the amount of expression) regulations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We develop a method, termed SC2P, that first identifies the phase of expression a gene is in, by taking into account of both cell- and gene-specific contexts, in a model-based and data-driven fashion. We then identify two forms of transcription regulation: phase transition, and magnitude tuning. We demonstrate that compared with existing methods, SC2P provides substantial improvement in sensitivity without sacrificing the control of false discovery, as well as better robustness. Furthermore, the analysis provides better interpretation of the nature of regulation types in different genes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>SC2P is implemented as an open source R package publicly available at https:\/\/github.com\/haowulab\/SC2P.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty329","type":"journal-article","created":{"date-parts":[[2018,4,23]],"date-time":"2018-04-23T06:41:15Z","timestamp":1524465675000},"page":"3340-3348","source":"Crossref","is-referenced-by-count":32,"title":["Two-phase differential expression analysis for single cell RNA-seq"],"prefix":"10.1093","volume":"34","author":[{"given":"Zhijin","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Brown University, Providence, RI, USA"},{"name":"Center for Statistical Sciences, Brown University, Providence, RI, USA"},{"name":"Center for Computational Molecular Biology, Brown University, Providence, RI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Brown University, Providence, RI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael L","family":"Stitzel","sequence":"additional","affiliation":[{"name":"The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA"},{"name":"Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA"},{"name":"Department of Genetics & Genome Sciences, University of Connecticut, Farmington, CT, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,4,24]]},"reference":[{"key":"2023012712490624800_bty329-B1","doi-asserted-by":"crossref","first-page":"R106.","DOI":"10.1186\/gb-2010-11-10-r106","article-title":"Differential expression analysis for sequence count data","volume":"11","author":"Anders","year":"2010","journal-title":"Genome Biol"},{"key":"2023012712490624800_bty329-B2","doi-asserted-by":"crossref","first-page":"63.","DOI":"10.1186\/s13059-016-0927-y","article-title":"Design and computational analysis of single-cell RNA-sequencing experiments","volume":"17","author":"Bacher","year":"2016","journal-title":"Genome Biol"},{"key":"2023012712490624800_bty329-B3","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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