DOI: 10.18129/B9.bioc.RCSL    

Rank Constrained Similarity Learning for single cell RNA sequencing data

Bioconductor version: Release (3.13)

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 Spearman’s rank correlations of a cell’s 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>

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biocViews Clustering, DimensionReduction, RNASeq, Sequencing, SingleCell, Software, Visualization
Version 1.0.0
In Bioconductor since BioC 3.13 (R-4.1) (< 6 months)
License GPL-3
Depends R (>= 4.1)
Imports RcppAnnoy, igraph, NbClust, Rtsne, ggplot2, methods, pracma, umap, grDevices, graphics, stats
Suggests knitr, rmarkdown, mclust, RcppAnnoy
URL https://github.com/QinglinMei/RCSL
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Source Package RCSL_1.0.0.tar.gz
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