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Nat Methods. 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.

Author information

1
Department of Computer Science, Stanford University, Stanford, California, USA.
2
Department of Electrical Engineering, Stanford University, Stanford, California, USA.
3
Department of Pathology, Stanford University, Stanford, California, USA.

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.

PMID:
28263960
DOI:
10.1038/nmeth.4207
[Indexed for MEDLINE]

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