title: Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning.
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.
authors.affiliation: Department of Computer Science, Stanford University, Stanford, California, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Wang
authors.affiliation: Department of Electrical Engineering, Stanford University, Stanford, California, USA.
authors.forename: Junjie
authors.initials: J
authors.lastname: Zhu
authors.affiliation: Department of Computer Science, Stanford University, Stanford, California, USA.
authors.forename: Emma
authors.initials: E
authors.lastname: Pierson
authors.affiliation: Department of Computer Science, Stanford University, Stanford, California, USA.|Department of Pathology, Stanford University, Stanford, California, USA.
authors.forename: Daniele
authors.identifier: http://orcid.org/0000-0002-6087-2666
authors.initials: D
authors.lastname: Ramazzotti
authors.affiliation: Department of Computer Science, Stanford University, Stanford, California, USA.
authors.forename: Serafim
authors.initials: S
authors.lastname: Batzoglou
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D006801:Humans; D009504:Neutrophils / Q000166:cytology / Q000502:physiology; D017423:Sequence Analysis, RNA / Q000379:methods*; D059010:Single-Cell Analysis / Q000379:methods*; D012984:Software