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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 <https://xiyupeng.github.io/SpaTopic/>.
| Version: |
1.1.0 |
| 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: |
2024-04-22 |
| 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 |
Documentation:
Downloads:
Linking:
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https://CRAN.R-project.org/package=SpaTopic
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.