recipe bioconductor-rcsl

Rank Constrained Similarity Learning for single cell RNA sequencing data






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.

package bioconductor-rcsl

(downloads) docker_bioconductor-rcsl



depends r-base:


depends r-ggplot2:

depends r-igraph:

depends r-nbclust:

depends r-pracma:

depends r-rcppannoy:

depends r-rtsne:

depends r-umap:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-rcsl

and update with::

   mamba update bioconductor-rcsl

To create a new environment, run:

mamba create --name myenvname bioconductor-rcsl

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-rcsl/tags`_ for valid values for ``<tag>``)

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