Package: scROSHI 1.0.0.1

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: Michael Prummer, Anne Bertolini, Lars Bosshard, Florian Barkmann, Josephine Yates, Valentina Boeva, The Tumor Profiler Consortium , Daniel Stekhoven, Franziska Singer, scROSHI: robust supervised hierarchical identification of single cells, NAR Genomics and Bioinformatics, Volume 5, Issue 2, June 2023, lqad058, <doi:10.1093/nargab/lqad058>.

Authors:Lars Bosshard [aut], Dominik Burri [aut, cre], Michael Prummer [aut]

scROSHI_1.0.0.1.tar.gz
scROSHI_1.0.0.1.zip(r-4.7-any)scROSHI_1.0.0.1.zip(r-4.6-any)scROSHI_1.0.0.1.zip(r-4.5-any)
scROSHI_1.0.0.1.tgz(r-4.6-any)scROSHI_1.0.0.1.tgz(r-4.5-any)
scROSHI_1.0.0.1.tar.gz(r-4.7-any)scROSHI_1.0.0.1.tar.gz(r-4.6-any)
scROSHI_1.0.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
scROSHI/json (API)

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

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.70 score 1 stars 6 scripts 633 downloads 6 exports 31 dependencies

Last updated from:569f7f592f. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK135
source / vignettesOK156
linux-releaseOK135
macos-releaseOK155
macos-oldrelOK182
windows-develOK123
windows-releaseOK85
windows-oldrelOK88
wasm-releaseOK106

Exports:f_annot_ctgenesf_my_correlation_testf_my_wilcox_testf_score_ctgenes_Uf_score_profile_corscROSHI

Dependencies:abindBHBiobaseBiocGenericsDelayedArraydqrngFNNgenericsGenomicRangesIRangesirlbalatticelimmaMatrixMatrixGenericsmatrixStatsRcppRcppAnnoyRcppEigenRcppProgressRSpectraS4ArraysS4VectorsSeqinfoSingleCellExperimentsitmoSparseArraystatmodSummarizedExperimentuwotXVector