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bioRxiv · 10.1101/118901

SIMLR: A Tool For Large-Scale Single-Cell Analysis By Multi-Kernel Learning

Wang, B.·Ramazzotti, D.·De Sano, L.·Zhu, J.·Pierson, E.·Batzoglou, S.
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Abstract

MotivationWe here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a cell-to-cell similarity measure from single-cell RNA-seq data. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of cells. SIMLR was benchmarked against state-of-the-art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization.\n\nAvailability and ImplementationSIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on bioconductor.org.\n\nContactbowang87@stanford.edu or daniele.ramazzotti@stanford.edu\n\nSupplementary InformationSupplementary data are available at Bioinformatics online.

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Wang, B., Ramazzotti, D., De Sano, L., Zhu, J., Pierson, E., Batzoglou, S.. 2017-03-21. SIMLR: A Tool For Large-Scale Single-Cell Analysis By Multi-Kernel Learning. https://doi.org/10.1101/118901

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