Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • News & Views
  • Published:


Bayesian deep learning for single-cell analysis

A recent approach for single-cell RNA-sequencing data uses Bayesian deep learning to correct technical artifacts and enable accurate and multifaceted downstream analyses.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: scVI is a multifaceted tool for scRNA-seq data processing and analysis.

Kim Caesar/Springer Nature


  1. Regev, A. et al. eLife 6, e27041 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Hicks, S. C., Townes, F. W., Teng, M. & Irizarry, R. A. Biostatistics 19, 562–578 (2018).

    Article  PubMed  Google Scholar 

  3. Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Nat. Methods (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Paszke, A. et al. Automatic differentiation in PyTorch. 31st Conference on Neural Information Processing Systems, Long Beach, CA, 4–9 December 2017.

  5. Kingma, D. P. & Welling, M. arXiv Preprint at (2013).

  6. van Dijk, D. et al. Cell 174, 716–729 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S. & Theis, F. J. bioRxiv Preprint at (2018).

  8. Marouf, M. et al. bioRxiv Preprint at (2018).

  9. Ching, T. et al. J. R. Soc. Interface 15, 20170387 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Hu, Q. & Greene, C. S. bioRxiv Preprint at (2018).

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Casey S. Greene.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Way, G.P., Greene, C.S. Bayesian deep learning for single-cell analysis. Nat Methods 15, 1009–1010 (2018).

Download citation

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics