Journal
NATURE METHODS
Volume 14, Issue 4, Pages 414-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/nmeth.4207
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Funding
- NDSEG Fellowship
- Hertz Fellowship
- Stanford Graduate Fellowship
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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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