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oCELLoc: Predicts Suitable Cell Types in Spatial Transcriptomics and scRNA-seq Data

Picks the suitable cell types in spatial and scRNA-seq data using shrinkage methods. The package includes curated reference gene expression profiles for human and mouse cell types, facilitating immediate application to common spatial transcriptomics or scRNA datasets. Additionally, users can input custom reference data to support tissue- or experiment-specific analyses.

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: glmnet, utils, stats, ggplot2, rlang, reshape2
Suggests: testthat (≥ 3.0.0), knitr, rmarkdown
Published: 2025-12-22
DOI: 10.32614/CRAN.package.oCELLoc
Author: Afeefa Zainab ORCID iD [aut, cre], Vladyslav Honcharuk ORCID iD [aut], Alexis Vandenbon ORCID iD [aut]
Maintainer: Afeefa Zainab <afeeffazainab at gmail.com>
License: MIT + file LICENSE
URL: https://doi.org/10.64898/2025.12.11.693812
NeedsCompilation: no
Materials: README
CRAN checks: oCELLoc results

Documentation:

Reference manual: oCELLoc.html , oCELLoc.pdf

Downloads:

Package source: oCELLoc_1.0.0.tar.gz
Windows binaries: r-devel: oCELLoc_1.0.0.zip, r-release: oCELLoc_1.0.0.zip, r-oldrel: oCELLoc_1.0.0.zip
macOS binaries: r-release (arm64): oCELLoc_1.0.0.tgz, r-oldrel (arm64): oCELLoc_1.0.0.tgz, r-release (x86_64): oCELLoc_1.0.0.tgz, r-oldrel (x86_64): oCELLoc_1.0.0.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.