We here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a sample-to-sample similarity measure from expression data observed for heterogenous samples. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of samples. SIMLR was benchmarked against state-of-the-art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization. Availability and Implementation SIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on http://bioconductor.org.

翻译：我们在此介绍SIMLR(通过多内核LeaRning的单一细胞解释),这是一个开放源码工具,它实施一个创新框架,从不同样本所观察到的表达数据中学习一个样到样的相似度计量,可以有效地利用SIMLR来完成诸如尺寸减少、集群和不同样本群的可视化等任务,SIMLR在几个公共数据集中参照了这三项任务的最新先进方法,表明它可以缩放,能够大大改进集群的性能,并通过更好的可视化提供宝贵的洞察力,使数据更易于解释。GitHub在R和MATLAB执行中都有SIMLR的提供和实施,此外,SIMLR还以http://biocularor.org的R软件包形式提供。