RCSL: Rank Constrained Similarity Learning for Single Cell RNA Sequencing Data

A novel clustering algorithm and toolkit to accurately identify various cell types using single cell RNA sequencing data from a complex tissue. This algorithm considers both local 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. This algorithm uses Spearman’s rank correlations of a cell’s expression vector with those of other cells to measure its global similarity, and learns neighbour representation of a cell as its local similarity. The overall similarity of a cell to other cells is a linear combination of its global similarity and local similarity. See Mei et. al. (2021) <doi:10.1101/2021.04.12.439254> for more details.

Version: 0.99.95
Depends: R (≥ 4.0)
Imports: RcppAnnoy, igraph, mclust, NbClust, Rtsne, ggplot2, methods, pracma, umap, grDevices, graphics, stats, SingleCellExperiment
Suggests: knitr, rmarkdown
Published: 2021-04-19
Author: Qinglin Mei [aut, cre], Guojun Li [ctb], Zhengchang Su [fnd]
Maintainer: Qinglin Mei <meiqinglinkf at 163.com>
BugReports: https://github.com/QinglinMei/RCSL/issues
License: GPL-3
URL: https://github.com/QinglinMei/RCSL
NeedsCompilation: no
Materials: README
CRAN checks: RCSL results


Reference manual: RCSL.pdf
Vignettes: RCSL package manual


Package source: RCSL_0.99.95.tar.gz
Windows binaries: r-devel: RCSL_0.99.95.zip, r-release: RCSL_0.99.95.zip, r-oldrel: RCSL_0.99.95.zip
macOS binaries: r-release (arm64): RCSL_0.99.95.tgz, r-release (x86_64): RCSL_0.99.95.tgz, r-oldrel: RCSL_0.99.95.tgz


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