title: Beta-Poisson model for single-cell RNA-seq data analyses.
abstract: Single-cell RNA-sequencing technology allows detection of gene expression at the single-cell level. One typical feature of the data is a bimodality in the cellular distribution even for highly expressed genes, primarily caused by a proportion of non-expressing cells. The standard and the over-dispersed gamma-Poisson models that are commonly used in bulk-cell RNA-sequencing are not able to capture this property.
  
  
  
  We introduce a beta-Poisson mixture model that can capture the bimodality of the single-cell gene expression distribution. We further integrate the model into the generalized linear model framework in order to perform differential expression analyses. The whole analytical procedure is called BPSC. The results from several real single-cell RNA-seq datasets indicate that ∼90% of the transcripts are well characterized by the beta-Poisson model; the model-fit from BPSC is better than the fit of the standard gamma-Poisson model in > 80% of the transcripts. Moreover, in differential expression analyses of simulated and real datasets, BPSC performs well against edgeR, a conventional method widely used in bulk-cell RNA-sequencing data, and against scde and MAST, two recent methods specifically designed for single-cell RNA-seq data.
  
  
  
  An R package BPSC for model fitting and differential expression analyses of single-cell RNA-seq data is available under GPL-3 license at https://github.com/nghiavtr/BPSC CONTACT: yudi.pawitan@ki.se or mattias.rantalainen@ki.se
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden.
authors.forename: Trung Nghia
authors.initials: TN
authors.lastname: Vu
authors.affiliation: Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK.
authors.forename: Quin F
authors.initials: QF
authors.lastname: Wills
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.
authors.forename: Krishna R
authors.initials: KR
authors.lastname: Kalari
authors.affiliation: Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.
authors.forename: Nifang
authors.initials: N
authors.lastname: Niu
authors.affiliation: Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.
authors.forename: Liewei
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden.
authors.forename: Mattias
authors.initials: M
authors.lastname: Rantalainen
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden.
authors.forename: Yudi
authors.initials: Y
authors.lastname: Pawitan
mesh_terms: D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling; D008962:Models, Theoretical; D012313:RNA; D017423:Sequence Analysis, RNA; D059010:Single-Cell Analysis
chemical_list: D012313:RNA