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

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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
Single-cell RNA sequencing, Differential expression, Zero-inflated negative binomial, Weights, DIFFERENTIAL EXPRESSION ANALYSIS, GENE-EXPRESSION, SEQUENCING DATA, MAMMALIAN-CELLS, HETEROGENEITY, TRANSCRIPTOMICS, PACKAGE

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Chicago
Van den Berge, Koen, Fanny Perraudeau, Charlotte Soneson, Michael I Love, Davide Risso, Jean-Philippe Vert, Mark D Robinson, Sandrine Dudoit, and Lieven Clement. 2018. “Observation Weights Unlock Bulk RNA-seq Tools for Zero Inflation and Single-cell Applications.” Genome Biology 19.
APA
Van den Berge, K., Perraudeau, F., Soneson, C., Love, M. I., Risso, D., Vert, J.-P., Robinson, M. D., et al. (2018). Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications. GENOME BIOLOGY, 19.
Vancouver
1.
Van den Berge K, Perraudeau F, Soneson C, Love MI, Risso D, Vert J-P, et al. Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications. GENOME BIOLOGY. 2018;19.
MLA
Van den Berge, Koen, Fanny Perraudeau, Charlotte Soneson, et al. “Observation Weights Unlock Bulk RNA-seq Tools for Zero Inflation and Single-cell Applications.” GENOME BIOLOGY 19 (2018): n. pag. Print.
@article{8557546,
  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.},
  articleno    = {24},
  author       = {Van den Berge, Koen and Perraudeau, Fanny and Soneson, Charlotte and Love, Michael I and Risso, Davide and Vert, Jean-Philippe and Robinson, Mark D and Dudoit, Sandrine and Clement, Lieven},
  issn         = {1474-760X},
  journal      = {GENOME BIOLOGY},
  keywords     = {Single-cell RNA sequencing,Differential expression,Zero-inflated negative binomial,Weights,DIFFERENTIAL EXPRESSION ANALYSIS,GENE-EXPRESSION,SEQUENCING DATA,MAMMALIAN-CELLS,HETEROGENEITY,TRANSCRIPTOMICS,PACKAGE},
  language     = {eng},
  pages        = {17},
  title        = {Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications},
  url          = {http://dx.doi.org/10.1186/s13059-018-1406-4},
  volume       = {19},
  year         = {2018},
}

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