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Single-cell Interpretation via Multi-kernel LeaRning (SIMLR)

Bioconductor version: Release (3.19)

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.

Author: Daniele Ramazzotti [aut] , Bo Wang [aut], Luca De Sano [cre, aut] , Serafim Batzoglou [ctb]

Maintainer: Luca De Sano <luca.desano at>

Citation (from within R, enter citation("SIMLR")):


To install this package, start R (version "4.4") and enter:

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


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Introduction HTML R Script
Running SIMLR HTML R Script
Reference Manual PDF


biocViews Clustering, GeneExpression, ImmunoOncology, Sequencing, SingleCell, Software
Version 1.30.0
In Bioconductor since BioC 3.4 (R-3.3) (7.5 years)
License file LICENSE
Depends R (>= 4.1.0)
Imports parallel, Matrix, stats, methods, Rcpp, pracma, RcppAnnoy, RSpectra
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Source Package SIMLR_1.30.0.tar.gz
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