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. 2017 Apr;14(4):414-416.
doi: 10.1038/nmeth.4207. Epub 2017 Mar 6.

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

Bo Wang  1 , Junjie Zhu  2 , Emma Pierson  1 , Daniele Ramazzotti  1   3 , Serafim Batzoglou  1
Affiliations

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

Bo Wang et al. Nat Methods. 2017 Apr.

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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