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## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("SIMLR")

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SIMLR

DOI: 10.18129/B9.bioc.SIMLR    

Title: SIMLR: Single-cell Interpretation via Multi-kernel LeaRning

Bioconductor version: Release (3.6)

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 to 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. SIMLR is capable of separating known subpopulations more accurately in single-cell data sets than do existing dimension reduction methods. Additionally, SIMLR demonstrates high sensitivity and accuracy on high-throughput peripheral blood mononuclear cells (PBMC) data sets generated by the GemCode single-cell technology from 10x Genomics.

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

Maintainer: Daniele Ramazzotti <daniele.ramazzotti at yahoo.com>

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

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("SIMLR")

Documentation

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

browseVignettes("SIMLR")

 

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Details

biocViews Clustering, GeneExpression, Sequencing, SingleCell, Software
Version 1.4.0
In Bioconductor since BioC 3.4 (R-3.3) (1 year)
License file LICENSE
Depends R (>= 3.4)
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
Build Report  

Package Archives

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Source Package SIMLR_1.4.0.tar.gz
Windows Binary SIMLR_1.4.0.zip (32- & 64-bit)
Mac OS X 10.11 (El Capitan) SIMLR_1.4.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/SIMLR
Package Short Url http://bioconductor.org/packages/SIMLR/
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