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SIMLR

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

Bioconductor version: Release (3.17)

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 1.26.1
In Bioconductor since BioC 3.4 (R-3.3) (7 years)
License file LICENSE
Depends R (>= 4.1.0)
Imports parallel, Matrix, stats, methods, Rcpp, pracma, RcppAnnoy, RSpectra
Linking To Rcpp
Suggests BiocGenerics, BiocStyle, testthat, knitr, igraph
System Requirements
Enhances
URL https://github.com/BatzoglouLabSU/SIMLR
Bug Reports https://github.com/BatzoglouLabSU/SIMLR
See More
Depends On Me
Imports Me ccImpute
Suggests Me
Links To Me
Build Report  

Package Archives

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

Source Package SIMLR_1.26.1.tar.gz
Windows Binary SIMLR_1.26.1.zip
macOS Binary (x86_64) SIMLR_1.26.1.tgz
macOS Binary (arm64) SIMLR_1.26.1.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 3.17 Source Archive