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scROSHI: Robust Supervised Hierarchical Identification of Single Cells

Identifying cell types based on expression profiles is a pillar of single cell analysis. 'scROSHI' identifies cell types based on expression profiles of single cell analysis by utilizing previously obtained cell type specific gene sets. It takes into account the hierarchical nature of cell type relationship and does not require training or annotated data. A detailed description of the method can be found at: Prummer, Bertolini, Bosshard, Barkmann, Yates, Boeva, The Tumor Profiler Consortium, Stekhoven, and Singer (2022) <doi:10.1101/2022.04.05.487176>.

Version: 1.0.0.0
Depends: R (≥ 3.60)
Imports: limma, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, utils, uwot
Published: 2023-01-10
DOI: 10.32614/CRAN.package.scROSHI
Author: Lars Bosshard ORCID iD [aut, cre], Michael Prummer ORCID iD [aut]
Maintainer: Lars Bosshard <bosshard at nexus.ethz.ch>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: scROSHI results

Documentation:

Reference manual: scROSHI.pdf

Downloads:

Package source: scROSHI_1.0.0.0.tar.gz
Windows binaries: r-devel: scROSHI_1.0.0.0.zip, r-release: scROSHI_1.0.0.0.zip, r-oldrel: scROSHI_1.0.0.0.zip
macOS binaries: r-release (arm64): scROSHI_1.0.0.0.tgz, r-oldrel (arm64): scROSHI_1.0.0.0.tgz, r-release (x86_64): scROSHI_1.0.0.0.tgz, r-oldrel (x86_64): scROSHI_1.0.0.0.tgz

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.
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