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Genome Biol. 2018 Feb 26;19(1):24. doi: 10.1186/s13059-018-1406-4.

Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications.

Author information

1
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, Ghent, 9000, Belgium.
2
Bioinformatics Institute Ghent, Ghent University, Ghent, 9000, Belgium.
3
Division of Biostatistics, School of Public Health, University of California, Berkeley, USA.
4
Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland.
5
SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, Switzerland.
6
Department of Biostatistics and Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
7
Division of Biostatistics and Epidemiology, Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, USA.
8
MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France.
9
Institut Curie, Paris, France.
10
INSERM U900, Paris, France.
11
Ecole Normale Supérieure, Department of Mathematics and Applications, Paris, France.
12
Department of Statistics, University of California, Berkeley, USA.

Abstract

Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.

KEYWORDS:

Differential expression; Single-cell RNA sequencing; Weights; Zero-inflated negative binomial

PMID:
29478411
PMCID:
PMC6251479
DOI:
10.1186/s13059-018-1406-4
[Indexed for MEDLINE]
Free PMC Article

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