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SIMLR

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


Bioconductor version: Release (3.18)

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 gmail.com>

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

Installation

To install this package, start R (version "4.3") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("SIMLR")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("SIMLR")
Introduction HTML R Script
Running SIMLR HTML R Script
Reference Manual PDF
NEWS Text
LICENSE Text

Details

biocViews Clustering, GeneExpression, ImmunoOncology, Sequencing, SingleCell, Software
Version 1.28.0
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
System Requirements
URL https://github.com/BatzoglouLabSU/SIMLR
Bug Reports https://github.com/BatzoglouLabSU/SIMLR
See More
Suggests BiocGenerics, BiocStyle, testthat, knitr, igraph
Linking To Rcpp
Enhances
Depends On Me
Imports Me ccImpute
Suggests Me
Links To Me
Build Report Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package SIMLR_1.28.0.tar.gz
Windows Binary SIMLR_1.28.0.zip
macOS Binary (x86_64) SIMLR_1.28.0.tgz
macOS Binary (arm64) SIMLR_1.28.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/SIMLR
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/SIMLR
Bioc Package Browser https://code.bioconductor.org/browse/SIMLR/
Package Short Url https://bioconductor.org/packages/SIMLR/
Package Downloads Report Download Stats