DOI: 10.18129/B9.bioc.BayesSpace  

Clustering and Resolution Enhancement of Spatial Transcriptomes

Bioconductor version: Release (3.18)

Tools for clustering and enhancing the resolution of spatial gene expression experiments. BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into "sub-spots", for which features such as gene expression or cell type composition can be imputed.

Author: Edward Zhao [aut], Matt Stone [aut, cre], Xing Ren [ctb], Raphael Gottardo [ctb]

Maintainer: Matt Stone <mstone at>

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biocViews Clustering, DataImport, GeneExpression, ImmunoOncology, SingleCell, Software, Transcriptomics
Version 1.12.0
In Bioconductor since BioC 3.12 (R-4.0) (3 years)
License MIT + file LICENSE
Depends R (>= 4.0.0), SingleCellExperiment
Imports Rcpp (>=, stats, purrr, scater, scran, SummarizedExperiment, coda, rhdf5, S4Vectors, Matrix, assertthat, mclust, RCurl, DirichletReg, xgboost, utils, ggplot2, scales, BiocFileCache, BiocSingular
LinkingTo Rcpp, RcppArmadillo, RcppDist, RcppProgress
Suggests testthat, knitr, rmarkdown, igraph, spatialLIBD, dplyr, viridis, patchwork, RColorBrewer, Seurat
SystemRequirements C++11
Depends On Me
Imports Me RegionalST
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