Abstract
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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Acknowledgements
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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Contributions
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.
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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). https://doi.org/10.1038/nmeth.4207
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DOI: https://doi.org/10.1038/nmeth.4207
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