Bayesian approach to single-cell differential expression analysis View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-07

AUTHORS

Peter V Kharchenko, Lev Silberstein, David T Scadden

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. More... »

PAGES

740-742

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nmeth.2967

DOI

http://dx.doi.org/10.1038/nmeth.2967

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1015866289

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24836921


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