SpaTopic: Topic Inference to Identify Tissue Architecture in MultiplexedImages
A novel spatial topic model to integrate both cell type and spatial information to identify the complex spatial tissue architecture on multiplexed tissue images without human intervention. The Package implements a collapsed Gibbs sampling algorithm for inference. 'SpaTopic' is scalable to large-scale image datasets without extracting neighborhood information for every single cell. For more details on the methodology, see <https://xiyupeng.github.io/SpaTopic/>.
| Version: | 1.2.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | Rcpp (≥ 0.12.0),RANN (≥ 2.6.0),sf (≥ 1.0-12), methods (≥3.4),foreach (≥ 1.5.0),iterators (≥ 1.0) |
| LinkingTo: | Rcpp,RcppArmadillo,RcppProgress |
| Suggests: | knitr,rmarkdown,SeuratObject (≥ 4.9.9.9086),doParallel (≥ 1.0) |
| Published: | 2025-03-03 |
| DOI: | 10.32614/CRAN.package.SpaTopic |
| Author: | Xiyu Peng [aut, cre] |
| Maintainer: | Xiyu Peng <pansypeng124 at gmail.com> |
| BugReports: | https://github.com/xiyupeng/SpaTopic/issues |
| License: | GPL (≥ 3) |
| URL: | https://github.com/xiyupeng/SpaTopic |
| NeedsCompilation: | yes |
| Materials: | README,NEWS |
| CRAN checks: | SpaTopic results |
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