Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical for the identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization..
The R version of SIMLR can be installed from Github. To do so, we need to install the R packages SIMLR depends on and the devtools package.
# install SIMLR dependencies if (!require("Matrix")) install.packages("Matrix") if (!require("Rcpp")) install.packages("Rcpp") if (!require("RcppAnnoy")) install.packages("RcppAnnoy") if (!require("RSpectra")) install.packages("RSpectra") if (!require("pracma")) install.packages("pracma") # install SIMLR library if (!require("devtools")) install.packages("devtools") library("devtools") install_github("BatzoglouLabSU/SIMLR", ref = "master") # load SIMLR library library("SIMLR")