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SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning

Bo Wang, Daniele Ramazzotti, Luca De Sano, Junjie Zhu, Emma Pierson, Serafim Batzoglou
doi: https://doi.org/10.1101/118901
Bo Wang
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Daniele Ramazzotti
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Luca De Sano
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Junjie Zhu
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Emma Pierson
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Serafim Batzoglou
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Abstract

Motivation We 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.

Availability and Implementation SIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on bioconductor.org.

Contact bowang87{at}stanford.edu or daniele.ramazzotti{at}stanford.edu

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted March 21, 2017.
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SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning
Bo Wang, Daniele Ramazzotti, Luca De Sano, Junjie Zhu, Emma Pierson, Serafim Batzoglou
bioRxiv 118901; doi: https://doi.org/10.1101/118901
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SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning
Bo Wang, Daniele Ramazzotti, Luca De Sano, Junjie Zhu, Emma Pierson, Serafim Batzoglou
bioRxiv 118901; doi: https://doi.org/10.1101/118901

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