SIMLR

DOI: 10.18129/B9.bioc.SIMLR  

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

Bioconductor version: Release (3.16)

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>

Installation

To install this package, start R (version "4.2") 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")

 

PDF	R Script	Single-cell Interpretation via Multi-kernel LeaRning (\Biocpkg{SIMLR})
PDF		Reference Manual
Text		NEWS
Text		LICENSE

Details

biocViews	Clustering, GeneExpression, ImmunoOncology, Sequencing, SingleCell, Software
Version	1.24.3
In Bioconductor since	BioC 3.4 (R-3.3) (6.5 years)
License	file LICENSE
Depends	R (>= 4.1.0)
Imports	parallel, Matrix, stats, methods, Rcpp, pracma, RcppAnnoy, RSpectra
LinkingTo	Rcpp
Suggests	BiocGenerics, BiocStyle, testthat, knitr, igraph
SystemRequirements
Enhances
URL	https://github.com/BatzoglouLabSU/SIMLR
BugReports	https://github.com/BatzoglouLabSU/SIMLR
Depends On Me
Imports Me	ccImpute, SingleCellSignalR
Suggests Me
Links To Me
Build Report

Package Archives

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

Source Package	SIMLR_1.24.3.tar.gz
Windows Binary	SIMLR_1.24.3.zip
macOS Binary (x86_64)	SIMLR_1.24.3.tgz
macOS Binary (arm64)	SIMLR_1.24.3.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
Old Source Packages for BioC 3.16	Source Archive