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clustermole: Unbiased Single-Cell Transcriptomic Data Cell Type Identification

Assignment of cell type labels to single-cell RNA sequencing (scRNA-seq) clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging when unexpected or poorly described populations are present. The clustermole R package provides methods to query thousands of human and mouse cell identity markers sourced from a variety of databases.

Version: 1.1.1
Depends: R (≥ 4.3)
Imports: dplyr, GSEABase, GSVA (≥ 1.50.0), magrittr, methods, rlang, singscore, tibble, tidyr, utils
Suggests: covr, knitr, rmarkdown, roxygen2, testthat
Published: 2024-01-08
DOI: 10.32614/CRAN.package.clustermole
Author: Igor Dolgalev ORCID iD [aut, cre]
Maintainer: Igor Dolgalev <igor.dolgalev at nyumc.org>
BugReports: https://github.com/igordot/clustermole/issues
License: MIT + file LICENSE
URL: https://igordot.github.io/clustermole/
NeedsCompilation: no
Materials: README NEWS
In views: Omics
CRAN checks: clustermole results

Documentation:

Reference manual: clustermole.pdf
Vignettes: Introduction to clustermole

Downloads:

Package source: clustermole_1.1.1.tar.gz
Windows binaries: r-devel: clustermole_1.1.1.zip, r-release: clustermole_1.1.1.zip, r-oldrel: clustermole_1.1.1.zip
macOS binaries: r-release (arm64): clustermole_1.1.1.tgz, r-oldrel (arm64): not available, r-release (x86_64): clustermole_1.1.1.tgz, r-oldrel (x86_64): not available
Old sources: clustermole archive

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.