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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:
Please use the canonical form
https://CRAN.R-project.org/package=SpaTopic
to link to this page.
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.
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