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

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


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.

MeSH terms

  • Algorithms
  • Computational Biology / methods
  • Humans
  • Neutrophils / cytology
  • Neutrophils / physiology
  • Sequence Analysis, RNA / methods*
  • Single-Cell Analysis / methods*
  • Software*