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) for more details.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("RCSL")

0.99.95 by Qinglin Mei, 3 months ago


https://github.com/QinglinMei/RCSL


Report a bug at https://github.com/QinglinMei/RCSL/issues


Browse source code at https://github.com/cran/RCSL


Authors: Qinglin Mei [aut, cre] , Guojun Li [ctb] , Zhengchang Su [fnd]


Documentation:   PDF Manual  


GPL-3 license


Imports RcppAnnoy, igraph, mclust, NbClust, Rtsne, ggplot2, methods, pracma, umap, grDevices, graphics, stats, SingleCellExperiment

Suggests knitr, rmarkdown


See at CRAN