SIMLR

DOI: 10.18129/B9.bioc.SIMLR    

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

Bioconductor version: Release (3.8)

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, cre], Bo Wang [aut], Luca De Sano [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 and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("SIMLR", version = "3.8")

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, Sequencing, SingleCell, Software
Version 1.8.0
In Bioconductor since BioC 3.4 (R-3.3) (2 years)
License file LICENSE
Depends R (>= 3.5)
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
Suggests Me
Links To Me
Build Report  

Package Archives

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

Source Package SIMLR_1.8.0.tar.gz
Windows Binary SIMLR_1.8.0.zip (32- & 64-bit)
Mac OS X 10.11 (El Capitan) SIMLR_1.8.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/SIMLR
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/SIMLR
Package Short Url http://bioconductor.org/packages/SIMLR/
Package Downloads Report Download Stats

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