Topic Inference to Identify Tissue Architecture in Multiplexed Images R package SpaTopic version 1.2.0 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/>. Software 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), Xiyu Peng <pansypeng124@gmail.com> Comprehensive R Archive Network (CRAN) Xiyu Peng [aut, cre] (ORCID: <https://orcid.org/0000-0003-4232-0910>) GPL (>= 3) 2025-03-03 application/tgz https://CRAN.R-project.org/package=SpaTopic doi:10.32614/CRAN.package.SpaTopic