Package: oCELLoc 1.0.0

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.

Authors:Afeefa Zainab [aut, cre], Vladyslav Honcharuk [aut], Alexis Vandenbon [aut]

oCELLoc_1.0.0.tar.gz
oCELLoc_1.0.0.zip(r-4.7)oCELLoc_1.0.0.zip(r-4.6)oCELLoc_1.0.0.zip(r-4.5)
oCELLoc_1.0.0.tgz(r-4.6-any)oCELLoc_1.0.0.tgz(r-4.5-any)
oCELLoc_1.0.0.tar.gz(r-4.7-any)oCELLoc_1.0.0.tar.gz(r-4.6-any)
oCELLoc_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
oCELLoc/json (API)

# Install 'oCELLoc' in R:
install.packages('oCELLoc', repos = c('https://afeefa-zainab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/afeefa-zainab/ocelloc/issues

Datasets:

On CRAN:

Conda:

3.00 score 1 stars 502 downloads 1 exports 32 dependencies

Last updated from:f4b22bb7e4. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK131
source / vignettesOK213
linux-release-x86_64OK131
macos-release-arm64OK127
macos-oldrel-arm64OK172
windows-develOK86
windows-releaseOK86
windows-oldrelOK89
wasm-releaseOK110

Exports:predict_cell_types

Dependencies:clicodetoolscpp11farverforeachggplot2glmnetgluegtableisobanditeratorslabelinglatticelifecyclemagrittrMatrixplyrR6RColorBrewerRcppRcppEigenreshape2rlangS7scalesshapestringistringrsurvivalvctrsviridisLitewithr