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SIMLR

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

Bioconductor version: 3.24 · Package version: 1.39.0

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.

Installation

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

BiocManager::install("SIMLR")

Details

MaintainerLuca De Sano <luca.desano@gmail.com>
AuthorDaniele Ramazzotti [aut] (ORCID: <https://orcid.org/0000-0002-6087-2666>), Bo Wang [aut], Luca De Sano [cre, aut] (ORCID: <https://orcid.org/0000-0002-9618-3774>), Serafim Batzoglou [ctb]
Licensefile LICENSE
URLhttps://github.com/BatzoglouLabSU/SIMLR
Bug Reportshttps://github.com/BatzoglouLabSU/SIMLR
Downloads rank646
Source branchdevel
biocViewsClustering, GeneExpression, ImmunoOncology, Sequencing, SingleCell, Software

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packageSIMLR_1.39.0.tar.gz
Windows binary (x86_64)SIMLR_1.39.0.zip
macOS binary (arm64)SIMLR_1.39.0.tgz
macOS binary (x86_64)SIMLR_1.39.0.tgz
Dependencies

Depends: R (>= 4.1.0)

Imports: parallel, Matrix, stats, methods, Rcpp, pracma, RcppAnnoy, RSpectra

LinkingTo: Rcpp

Suggests: BiocGenerics, BiocStyle, testthat, knitr, igraph