SC.MEB: Spatial Clustering with Hidden Markov Random Field using Empirical Bayes

Spatial clustering with hidden markov random field fitted via EM algorithm, details of which can be found in Yi Yang (2021) <doi:10.1101/2021.06.05.447181>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.

Version: 1.1
Depends: mclust, parallel, ggplot2, Matrix, R (≥ 3.5)
Imports: Rcpp (≥ 1.0.6), SingleCellExperiment, purrr, BiocSingular, SummarizedExperiment, scater, scran, S4Vectors
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2021-10-08
Author: Yi Yang [aut, cre], Xingjie Shi [aut], Jin Liu [aut]
Maintainer: Yi Yang <yygaosansiban at sina.com>
License: GPL-3
NeedsCompilation: yes
Materials: README
CRAN checks: SC.MEB results

Documentation:

Reference manual: SC.MEB.pdf
Vignettes: SC-MEB
SC-MEB CRC

Downloads:

Package source: SC.MEB_1.1.tar.gz
Windows binaries: r-devel: SC.MEB_1.1.zip, r-devel-UCRT: SC.MEB_1.1.zip, r-release: SC.MEB_1.1.zip, r-oldrel: SC.MEB_1.1.zip
macOS binaries: r-release (arm64): SC.MEB_1.1.tgz, r-release (x86_64): SC.MEB_1.1.tgz, r-oldrel: not available
Old sources: SC.MEB archive

Linking:

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