This is the development version of RCSL; for the stable release version, see RCSL.
Rank Constrained Similarity Learning for single cell RNA sequencing dataBioconductor version: Development (3.22)
A novel clustering algorithm and toolkit RCSL (Rank Constrained Similarity Learning) to accurately identify various cell types using scRNA-seq data from a complex tissue. RCSL considers both lo-cal similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. RCSL uses Spearmanâs rank correlations of a cellâs expression vector with those of other cells to measure its global similar-ity, and adaptively learns neighbour representation of a cell as its local similarity. The overall similar-ity of a cell to other cells is a linear combination of its global similarity and local similarity.
Author: Qinglin Mei [cre, aut], Guojun Li [fnd], Zhengchang Su [fnd]
Maintainer: Qinglin Mei <meiqinglinkf at 163.com>
Citation (from within R, entercitation("RCSL")): 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("RCSL")
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("RCSL")Follow Installation instructions to use this package in your R session.
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