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:	1.1.0
Depends:	R (≥ 3.6)
Imports:	dplyr, GSEABase, GSVA (≥ 1.26.0), magrittr, methods, rlang (≥ 0.1.2), singscore, tibble, tidyr, utils
Suggests:	covr, knitr, prettydoc, rmarkdown, roxygen2, testthat (≥ 2.1.0)
Published:	2021-01-26
Author:	Igor Dolgalev [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/, https://github.com/igordot/clustermole
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	clustermole results

Documentation:

Reference manual:	clustermole.pdf
Vignettes:	Introduction to clustermole

Downloads:

Package source:	clustermole_1.1.0.tar.gz
Windows binaries:	r-devel: clustermole_1.1.0.zip, r-release: clustermole_1.1.0.zip, r-oldrel: clustermole_1.1.0.zip
macOS binaries:	r-release (arm64): clustermole_1.1.0.tgz, r-oldrel (arm64): clustermole_1.1.0.tgz, r-release (x86_64): clustermole_1.1.0.tgz, r-oldrel (x86_64): clustermole_1.1.0.tgz
Old sources:	clustermole archive

Linking:

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