Beta-Poisson model for single-cell RNA-seq data analyses

Trung Nghia Vu; Quin F Wills; Krishna R Kalari; Nifang Niu; Liewei Wang; Mattias Rantalainen; Yudi Pawitan

doi:10.1093/bioinformatics/btw202

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Beta-Poisson model for single-cell RNA-seq data analyses

Journal article

Open access

Peer reviewed

Beta-Poisson model for single-cell RNA-seq data analyses

Trung Nghia Vu, Quin F Wills, Krishna R Kalari, Nifang Niu, Liewei Wang, Mattias Rantalainen and Yudi Pawitan

Bioinformatics (Oxford, England), Vol.32(14), pp.2128-2135

07/15/2016

DOI: 10.1093/bioinformatics/btw202

PMCID: PMC13048230

PMID: 27153638

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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.

Computational Biology - methods

Gene Expression Profiling

Models, Theoretical

RNA

Sequence Analysis, RNA

Single-Cell Analysis

Details

Title: Subtitle: Beta-Poisson model for single-cell RNA-seq data analyses
Creators: Trung Nghia Vu - Karolinska Institutet
Quin F Wills - University of Oxford
Krishna R Kalari - Mayo Clinic in Florida
Nifang Niu - Mayo Clinic
Liewei Wang - Mayo Clinic
Mattias Rantalainen - Karolinska Institutet
Yudi Pawitan - Karolinska Institutet
Resource Type: Journal article
Publication Details: Bioinformatics (Oxford, England), Vol.32(14), pp.2128-2135
DOI: 10.1093/bioinformatics/btw202
PMID: 27153638
PMCID: PMC13048230
NLM abbreviation: Bioinformatics
ISSN: 1367-4803
eISSN: 1367-4811
Language: English
Date published: 07/15/2016
Academic Unit: Stead Family Department of Pediatrics; Medical Genetics and Genomics
Record Identifier: 9984701651002771

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