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Nat Methods. 2014 Jul;11(7):740-2. doi: 10.1038/nmeth.2967. Epub 2014 May 18.

Bayesian approach to single-cell differential expression analysis.

Author information

1
1] Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA. [2] Hematology/Oncology Program, Children's Hospital, Boston, Massachusetts, USA. [3] Harvard Stem Cell Institute, Cambridge, Massachusetts, USA.
2
1] Harvard Stem Cell Institute, Cambridge, Massachusetts, USA. [2] Center for Regenerative Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA. [3] Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA.

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.

PMID:
24836921
PMCID:
PMC4112276
DOI:
10.1038/nmeth.2967
[Indexed for MEDLINE]
Free PMC Article
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