0.1 Overview

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..

0.2 Installing SIMLR

The SIMLR package can be installed from Bioconductor as follow.

if (!require("BiocManager", quietly = TRUE))


1 load SIMLR library

library(“SIMLR”) ```

1.1 Debug

Please feel free to contact us if you have problems running our tool at or .