clustermole: Unbiased Cell Type Identification of Single-Cell Transcriptomic Data

A typical computational pipeline to process single-cell RNA sequencing (scRNA-seq) data involves clustering of cells. Assignment of cell type labels to those 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 if you are not familiar with all the captured subpopulations or have unexpected contaminants. 'clustermole' provides a comprehensive meta collection of cell identity markers for thousands of human and mouse cell types sourced from a variety of databases as well as methods to query them.

Version: 1.0.0
Depends: R (≥ 3.4)
Imports: dplyr, GSVA (≥ 1.26.0), magrittr, methods, rlang (≥ 0.1.2), tibble, tidyr, utils
Suggests: covr, roxygen2, testthat (≥ 2.1.0), knitr, rmarkdown
Published: 2020-01-20
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://github.com/igordot/clustermole
NeedsCompilation: no
Materials: README NEWS
CRAN checks: clustermole results

Downloads:

Reference manual: clustermole.pdf
Vignettes: Introduction to clustermole
Package source: clustermole_1.0.0.tar.gz
Windows binaries: r-devel: clustermole_1.0.0.zip, r-devel-gcc8: not available, r-release: clustermole_1.0.0.zip, r-oldrel: clustermole_1.0.0.zip
OS X binaries: r-release: clustermole_1.0.0.tgz, r-oldrel: clustermole_1.0.0.tgz

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