SIMLR

DOI: 10.18129/B9.bioc.SIMLR    

This is the development version of SIMLR; for the stable release version, see SIMLR.

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

Bioconductor version: Development (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 [cre, aut] , 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 (version "4.3") and enter:

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

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("SIMLR")

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

Documentation

PDF   Reference Manual

Details

biocViews Clustering, GeneExpression, ImmunoOncology, Sequencing, SingleCell, Software
Version 1.25.0
In Bioconductor since BioC 3.4 (R-3.3) (6 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
Windows Binary
macOS Binary (x86_64) SIMLR_1.25.0.tgz
macOS Binary (arm64)
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 https://bioconductor.org/packages/SIMLR/
Package Downloads Report Download Stats

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