Abstract
Single-cell data provide a means to dissect the composition of complex tissues and specialized cellular environments. However, the analysis of such measurements is complicated by high levels of technical noise and intrinsic biological variability. We describe a probabilistic model of expression-magnitude distortions typical of single-cell RNA-sequencing measurements, which enables detection of differential expression signatures and identification of subpopulations of cells in a way that is more tolerant of noise.
Keywords
Computational biologySingle-cell analysisIdentification (biology)Bayesian probabilityExpression (computer science)Noise (video)BiologyCellRNAProbabilistic logicDifferential (mechanical device)Computer scienceGene expressionBiological systemCell biologyGeneGeneticsArtificial intelligencePhysics
MeSH Terms
AlgorithmsBayes TheoremGene Expression ProfilingOligonucleotide Array Sequence AnalysisSequence AnalysisRNASingle-Cell Analysis
Affiliated Institutions
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Publication Info
- Year
- 2014
- Type
- article
- Volume
- 11
- Issue
- 7
- Pages
- 740-742
- Citations
- 1475
- Access
- Closed
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Cite This
Peter V. Kharchenko,
Lev Silberstein,
David T. Scadden
(2014).
Bayesian approach to single-cell differential expression analysis.
Nature Methods
, 11
(7)
, 740-742.
https://doi.org/10.1038/nmeth.2967
Identifiers
- DOI
- 10.1038/nmeth.2967
- PMID
- 24836921
- PMCID
- PMC4112276