  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.
