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Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning


We present single-cell interpretation via multikernel learning (SIMLR), an analytic framework and software which learns a similarity measure from single-cell RNA-seq data in order to perform dimension reduction, clustering and visualization. On seven published data sets, we benchmark SIMLR against state-of-the-art methods. We show that SIMLR is scalable and greatly enhances clustering performance while improving the visualization and interpretability of single-cell sequencing data.

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Figure 1: Overview of SIMLR.
Figure 2: Benchmark results on data sets with ground truth.
Figure 3: Comparison of 2D visualization.

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  1. Shapiro, E., Biezuner, T. & Linnarsson, S. Nat. Rev. Genet. 14, 618–630 (2013).

    Article  CAS  PubMed Central  Google Scholar 

  2. Pollen, A.A. et al. Nat. Biotechnol. 32, 1053–1058 (2014).

    Article  CAS  PubMed Central  Google Scholar 

  3. Usoskin, D. et al. Nat. Neurosci. 18, 145–153 (2015).

    Article  CAS  PubMed Central  Google Scholar 

  4. Kolodziejczyk, A.A. et al. Cell Stem Cell 17, 471–485 (2015).

    Article  CAS  PubMed Central  Google Scholar 

  5. Pierson, E. & Yau, C. Genome Biol. 16, 241 (2015).

    Article  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed Central  Google Scholar 

  8. Bach, F.R., Lanckriet, G.R.G. & Jordan, M.I. In Proc. 21st Int. Conf. Mach. Learn (eds. Greiner, R. & Schuurmans, D.) 6 (ICML, 2004).

  9. Gönen, M. & Alpaydin, E. J. Mach. Learn. Res. 12, 2211–2268 (2011).

    Google Scholar 

  10. Wang, B. et al. Nat. Methods 11, 333–337 (2014).

    Article  CAS  PubMed Central  Google Scholar 

  11. Buettner, F. et al. Nat. Biotechnol. 33, 155–160 (2015).

    Article  CAS  Google Scholar 

  12. Jolliffe, I. Principal Component Analysis (Wiley Online Library, 2002).

  13. Van der Maaten, L. & Hinton, G. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  14. Frey, B.J. & Dueck, D. Science 315, 972–976 (2007).

    Article  CAS  PubMed Central  Google Scholar 

  15. Ding, C. & He, X. In Proc. 21st Int. Conf. Mach. Learn (eds. Greiner, R. & Schuurmans, D.) 225–232 (ICML, 2004).

  16. Paul, F. et al. Cell 163, 1663–1677 (2015).

    Article  CAS  Google Scholar 

  17. Zeisel, A. et al. Title. Science 347, 1138–1142 (2015).

  18. von Luxburg, U. Stat. Comput. 17, 395–416 (2007).

    Article  Google Scholar 

  19. Wang, B. et al. Adv. Neural Inf. Process. Syst. 3297–3305 (2016).

  20. Nesterov, Y., Nemirovskii, A. & Ye, Y. Interior-Point Polynomial Algorithms in Convex Programming (SIAM, 1994).

  21. Parlett, B.N. The Symmetric Eigenvalue Problem (SIAM, 1980).

  22. Yang, J. & Leskovec, J. In Proc. 10th IEEE Conf. Data Min. (eds. Webb, G.I. et al.) 599–608 (IEEE, 2010).

  23. He, X., Cai, D. & Niyogi, P. Adv. Neural Inf. Process. Syst. 18, 507–514 (2005).

    Google Scholar 

  24. Kolde, R., Laur, S., Adler, P. & Vilo, J. Bioinformatics 28, 573–580 (2012).

    Article  CAS  PubMed Central  Google Scholar 

  25. Van Der Maaten, L. J. Mach. Learn. Res. 15, 3221–3245 (2014).

    Google Scholar 

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The authors would like to thank G.X. Zheng, J. Terry and T. Mikkelsen from 10x Genomics for providing access to the PBMC data as well as suggestions for the manuscript and the in silico experiments. E.P. acknowledges support from an NDSEG Fellowship and a Hertz Fellowship. J.Z. acknowledges support from a Stanford Graduate Fellowship.

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Authors and Affiliations



B.W., J.Z., and S.B. conceived the study and planned experiments. B.W. designed the algorithm and implemented the software in MATLAB. D.R. and B.W. developed the software package in R. J.Z. and E.P. performed data analysis and implemented the simulation study. J.Z. and E.P. drafted the manuscript. B.W. and S.B. contributed to the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Bo Wang or Serafim Batzoglou.

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Competing interests

S.B. is currently on a leave of absence from Stanford, and he is VP of Applied and Computational Biology at Illumina.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–29, Supplementary Tables 1–10 and Supplementary Notes 1–10 (PDF 18964 kb)

Supplementary Software 1

Matlab and R implementations of SIMLR with four small-scale single-cell RNA-seq datasets (ZIP 161889 kb)

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Wang, B., Zhu, J., Pierson, E. et al. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nat Methods 14, 414–416 (2017).

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