Bayesian approach to single-cell differential expression analysis (original) (raw)

Nature Methods volume 11, pages 740–742 (2014)Cite this article

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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.

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References

  1. Tang, F. et al. Nat. Methods 6, 377–382 (2009).
    Article CAS Google Scholar
  2. Islam, S. et al. Genome Res. 21, 1160–1167 (2011).
    Article CAS Google Scholar
  3. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. Cell Reports 2, 666–673 (2012).
    Article CAS Google Scholar
  4. Ramsköld, D. et al. Nat. Biotechnol. 30, 777–782 (2012).
    Article Google Scholar
  5. Dalerba, P. et al. Nat. Biotechnol. 29, 1120–1127 (2011).
    Article CAS Google Scholar
  6. Tang, F. et al. PLoS ONE 6, e21208 (2011).
    Article CAS Google Scholar
  7. Brouilette, S. et al. Dev. Dyn. 241, 1584–1590 (2012).
    Article CAS Google Scholar
  8. Buganim, Y. et al. Cell 150, 1209–1222 (2012).
    Article CAS Google Scholar
  9. Munsky, B., Neuert, G. & van Oudenaarden, A. Science 336, 183–187 (2012).
    Article CAS Google Scholar
  10. Brennecke, P. et al. Nat. Methods 10, 1093–1095 (2013).
    Article CAS Google Scholar
  11. Wills, Q.F. et al. Nat. Biotechnol. 31, 748–752 (2013).
    Article CAS Google Scholar
  12. Deng, Q., Ramskold, D., Reinius, B. & Sandberg, R. Science 343, 193–196 (2014).
    Article CAS Google Scholar
  13. Anders, S. & Huber, W. Genome Biol. 11, R106 (2010).
    Article CAS Google Scholar
  14. Trapnell, C. et al. Nat. Biotechnol. 31, 46–53 (2013).
    Article CAS Google Scholar
  15. McDavid, A. et al. Bioinformatics 29, 461–467 (2013).
    Article CAS Google Scholar
  16. Robinson, M.D. & Smyth, G.K. Bioinformatics 23, 2881–2887 (2007).
    Article CAS Google Scholar
  17. Moliner, A., Enfors, P., Ibanez, C.F. & Andang, M. Stem Cells Dev. 17, 233–243 (2008).
    Article CAS Google Scholar
  18. Tischler, J. & Surani, M.A. Curr. Opin. Biotechnol. 24, 69–78 (2013).
    Article CAS Google Scholar
  19. Cauffman, G. et al. Mol. Hum. Reprod. 11, 405–411 (2005).
    Article CAS Google Scholar
  20. Pan, H.A. et al. Fertil. Steril. 89, 1324–1327 (2008).
    Article CAS Google Scholar
  21. Trapnell, C., Pachter, L. & Salzberg, S.L. Bioinformatics 25, 1105–1111 (2009).
    Article CAS Google Scholar
  22. Grün, B., Scharl, T. & Leisch, F. Bioinformatics 28, 222–228 (2012).
    Article Google Scholar
  23. Andäng, M., Moliner, A., Doege, C.A., Ibanez, C.F. & Ernfors, P. Nat. Protoc. 3, 1013–1017 (2008).
    Article Google Scholar

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Acknowledgements

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

  1. Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
    Peter V Kharchenko
  2. Hematology/Oncology Program, Children's Hospital, Boston, Massachusetts, USA
    Peter V Kharchenko
  3. Harvard Stem Cell Institute, Cambridge, Massachusetts, USA
    Peter V Kharchenko, Lev Silberstein & David T Scadden
  4. Center for Regenerative Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
    Lev Silberstein & David T Scadden
  5. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA
    Lev Silberstein & David T Scadden

Authors

  1. Peter V Kharchenko
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  2. Lev Silberstein
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  3. David T Scadden
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Contributions

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.

Corresponding author

Correspondence toPeter 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.

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Kharchenko, P., Silberstein, L. & Scadden, D. Bayesian approach to single-cell differential expression analysis.Nat Methods 11, 740–742 (2014). https://doi.org/10.1038/nmeth.2967

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