SpaTopic: Topic Inference to Identify Tissue Architecture in Multiplexed Images

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 <>.

Version: 1.0.1
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 (≥, doParallel (≥ 1.0)
Published: 2024-01-17
Author: Xiyu Peng ORCID iD [aut, cre]
Maintainer: Xiyu Peng <pansypeng124 at>
License: GPL (≥ 3)
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: SpaTopic results


Reference manual: SpaTopic.pdf
Vignettes: SpaTopic Basics


Package source: SpaTopic_1.0.1.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
macOS binaries: r-prerel (arm64): SpaTopic_1.0.1.tgz, r-release (arm64): SpaTopic_1.0.1.tgz, r-oldrel (arm64): SpaTopic_1.0.1.tgz, r-prerel (x86_64): SpaTopic_1.0.1.tgz, r-release (x86_64): SpaTopic_1.0.1.tgz


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