spruce: Spatial Random Effects Clustering of Single Cell Data

Allows for identification of cell sub-populations within tissue samples using Bayesian multivariate mixture models with spatial random effects to account for a wide range of spatial gene expression patterns, as described in Allen et. al, 2021 <doi:10.1101/2021.06.23.449615>. Bayesian inference is conducted using efficient Gibbs sampling implemented using 'Rcpp'.

Version: 0.99.1
Depends: R (≥ 4.0)
Imports: Rcpp, mvtnorm, BayesLogit, truncnorm, stats, igraph, MCMCpack, patchwork, tidyr, dplyr, ggplot2, tidyselect, Seurat, rlang
LinkingTo: Rcpp, RcppArmadillo
Published: 2022-02-21
Author: Carter Allen ORCID iD [aut, cre], Dongjun Chung [aut]
Maintainer: Carter Allen <carter.allen12 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: spruce results


Reference manual: spruce.pdf


Package source: spruce_0.99.1.tar.gz
Windows binaries: r-devel: spruce_0.99.1.zip, r-release: spruce_0.99.1.zip, r-oldrel: not available
macOS binaries: r-release (arm64): spruce_0.99.1.tgz, r-release (x86_64): spruce_0.99.1.tgz, r-oldrel: spruce_0.99.1.tgz

Reverse dependencies:

Reverse imports: maple


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