Bayesian approach to single-cell differential expression analysis.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 24836921.
- Also identified by DOI 10.1038/nmeth.2967 and PMC identifier 4112276.
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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.
Medical subject headings
- Gene Expression Profiling
- Sequence Analysis, RNA
- Single-Cell Analysis