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
| Maintainer | Luca De Sano <luca.desano@gmail.com> |
| Author | Daniele 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] |
| License | file LICENSE |
| URL | https://github.com/BatzoglouLabSU/SIMLR |
| Bug Reports | https://github.com/BatzoglouLabSU/SIMLR |
| Downloads rank | 646 |
| Source branch | devel |
| biocViews | Clustering, GeneExpression, ImmunoOncology, Sequencing, SingleCell, Software |
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | SIMLR_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 |