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 (3.22)
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] ORCID: 0000-0002-6087-2666 , Bo Wang [aut], Luca De Sano [cre, aut] ORCID: 0000-0002-9618-3774 , Serafim Batzoglou [ctb]
Maintainer: Luca De Sano <luca.desano at gmail.com>
Citation (from within R, entercitation("SIMLR")): Installation
To install this package, start R (version "4.5") 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.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("SIMLR")Follow Installation instructions to use this package in your R session.
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