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A discriminative learning approach to differential expression analysis for single-cell RNA-seq

Nature Methods volume 16, pages 163–166 (2019)Cite this article

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Matters Arising to this article was published on 01 June 2020

Abstract

Single-cell RNA-seq makes it possible to characterize the transcriptomes of cell types across different conditions and to identify their transcriptional signatures via differential analysis. Our method detects changes in transcript dynamics and in overall gene abundance in large numbers of cells to determine differential expression. When applied to transcript compatibility counts obtained via pseudoalignment, our approach provides a quantification-free analysis of 3′ single-cell RNA-seq that can identify previously undetectable marker genes.

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Fig. 1: Logistic regression applied to scRNA-seq.
Fig. 2: Logistic regression identifies CD45 in purified T cell types.

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Code availability

The code required to conduct the simulations and reproduce the analyses is available at https://github.com/pachterlab/NYMP_2018. We also have provided the Github repository that was zipped at the time of manuscript acceptance as Supplementary Software.

Data availability

The myogenesis dataset (Trapnell et al.10) is available on the conquer database and on GEO as series GSE52529. The dataset on embryogenesis is available on the conquer database (Petropoulos et al.22). The 10x PBMC dataset is available from the 10x Genomics Support website19.

References

  1. Soneson, C. & Robinson, M. D. Nat. Methods 15, 255–261 (2018).

    Article  CAS  Google Scholar 

  2. Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Nat. Methods 11, 740–742 (2014).

    Article  CAS  Google Scholar 

  3. Finak, G. et al. Genome Biol. 16, 278 (2015).

    Article  Google Scholar 

  4. Yamazaki, T. et al. Genes Dev. 32, 1161–1174 (2018).

    Article  CAS  Google Scholar 

  5. Vitting-Seerup, K. & Sandelin, A. Mol. Cancer Res. 15, 1206–1220 (2017).

    Article  CAS  Google Scholar 

  6. Arzalluz-Luque, Á. & Conesa, A. Genome Biol. 19, 110 (2018).

    Article  Google Scholar 

  7. Gupta, I. et al. bioRxiv Preprint at https://www.biorxiv.org/content/early/2018/07/08/364950 (2018).

  8. Xing, E. P., Jordan, M. I. & Karp, R. M. in ICML ‘01 Proceedings of the Eighteenth International Conference on Machine Learning (eds Brodley, C. E. & Pohoreckyj Danyluk, A.) 601–608 (Morgan Kaufmann, San Francisco, 2001).

  9. Shevade, S. K. & Keerthi, S. S. Bioinformatics 19, 2246–2253 (2003).

    Article  CAS  Google Scholar 

  10. Trapnell, C. et al. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  Google Scholar 

  11. Zheng, G. X. et al. Nat. Commun. 8, 14049 (2017).

    Article  CAS  Google Scholar 

  12. Macosko, E. Z. et al. Cell 161, 1202–1214 (2015).

    Article  CAS  Google Scholar 

  13. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Nat. Biotechnol. 34, 525–527 (2016).

    Article  CAS  Google Scholar 

  14. Nicolae, M., Mangul, S., Măndoiu, I. I. & Zelikovsky, A. Algorithms Mol. Biol. 6, 9 (2011).

    Article  Google Scholar 

  15. Ntranos, V., Kamath, G. M., Zhang, J. M., Pachter, L. & Tse, D. N. Genome Biol. 17, 112 (2016).

    Article  Google Scholar 

  16. Yi, L., Pimentel, H., Bray, N. L. & Pachter, L. Genome Biol. 19, 53 (2018).

    Article  Google Scholar 

  17. Peterson, V. M. et al. Nat. Biotechnol. 35, 936–939 (2017).

    Article  CAS  Google Scholar 

  18. Byrne, A. et al. Nat. Commun. 8, 16027 (2017).

    Article  CAS  Google Scholar 

  19. 10x Genomics. Single cell gene expression datasets. 10x Genomics Support https://support.10xgenomics.com/single-cell-gene-expression/datasets (2018).

  20. Wolf, F. A., Angerer, P. & Theis, F. J. Genome Biol. 19, 15 (2018).

    Article  Google Scholar 

  21. Bradley, R. K. et al. PLoS Comput. Biol. 5, e1000392 (2009).

    Article  Google Scholar 

  22. Petropoulos, S. et al. Cell 165, 1012–1026 (2016).

    Article  CAS  Google Scholar 

  23. Conway, J. R., Lex, A. & Gehlenborg, N. Bioinformatics 33, 2938–2940 (2017).

    Article  CAS  Google Scholar 

  24. Love, M. I., Huber, W. & Anders, S. Genome Biol. 15, 550 (2014).

    Article  Google Scholar 

  25. Li, B. & Dewey, C. N. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  Google Scholar 

  26. Zappia, L., Phipson, B. & Oshlack, A. Genome Biol. 18, 174 (2017).

    Article  Google Scholar 

  27. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  Google Scholar 

  28. Soneson, C., Love, M. I. & Robinson, M. D. F1000Res. 4, 1521 (2015).

    Article  Google Scholar 

Download references

Acknowledgements

We thank N. Bray, J. Gehring and V. Svensson for discussion and comments on the manuscript, and H. Pimentel for assisting with the simulations. We thank A. Butler and R. Satija for implementing this method in Seurat. V.N., L.Y. and L.P. are partially funded by NIH R012017-0569.

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Author notes

  1. These authors contributed equally: Vasilis Ntranos, Lynn Yi.

Authors and Affiliations

  1. Department of Electrical Engineering & Computer Science, UC Berkeley, Berkeley, CA, USA

    Vasilis Ntranos

  2. Department of Electrical Engineering, Stanford University, Stanford, CA, USA

    Vasilis Ntranos

  3. UCLA–Caltech Medical Science Training Program, UCLA, Los Angeles, CA, USA

    Lynn Yi

  4. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA

    Lynn Yi & Lior Pachter

  5. Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavík, Iceland

    Páll Melsted

  6. Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA

    Lior Pachter

Authors
  1. Vasilis Ntranos
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  2. Lynn Yi
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  3. Páll Melsted
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  4. Lior Pachter
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Contributions

V.N. developed the model during discussions with L.Y. and L.P, and analyzed the 10x PBMC dataset. L.Y. performed the simulations and analyzed the embryo SMART-Seq dataset. P.M. developed kallisto genomebam and assisted with analysis. All authors contributed extensively to the interpretation of the results and writing of the manuscript.

Corresponding author

Correspondence to Lior Pachter.

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The authors declare no competing interests.

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Ntranos, V., Yi, L., Melsted, P. et al. A discriminative learning approach to differential expression analysis for single-cell RNA-seq. Nat Methods 16, 163–166 (2019). https://doi.org/10.1038/s41592-018-0303-9

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