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Bayesian approach to single-cell differential expression analysis


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.

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Figure 1: Modeling single-cell RNA-seq measurement.
Figure 2: Applying single-cell models for differential expression and subpopulation analyses.


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We thank X. Wang (Harvard Medical School) for help with packaging the implementation and F. Ferrari (Harvard Medical School) and M.B. Johnson (Children's Hospital, Boston) for critical review of the manuscript and SCDE implementation. This work was supported by US National Institutes of Health (NIH) grant K25AG037596 to P.V.K., fellowship awards from Leukemia and Lymphoma Research UK and Leukemia and Lymphoma Society to L.S. and NIH grants R01DK050234-15A1 and R01HL097794-03 to D.T.S.

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Authors and Affiliations



P.V.K. conceived and implemented the computational approach. L.S. and D.T.S. designed and carried out the initial experimental study that led to the development of the presented approach.

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Correspondence to Peter V Kharchenko.

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Competing interests

D.T.S. is a shareholder in Fate Therapeutics and is a consultant for Fate Therapeutics, Hospira, GlaxoSmithKline and Bone Therapeutics. The remaining authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5 (PDF 1451 kb)

Supplementary Software

Software for single-cell differential analysis. (ZIP 499 kb)

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Kharchenko, P., Silberstein, L. & Scadden, D. Bayesian approach to single-cell differential expression analysis. Nat Methods 11, 740–742 (2014).

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