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 
[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 |
Documentation:
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