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BayesSpace

Clustering and Resolution Enhancement of Spatial Transcriptomes

Bioconductor version: 3.23 · Package version: 1.22.0

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.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("BayesSpace")

Details

MaintainerSenbai Kang <senbai.kang@chuv.ch>
AuthorEdward Zhao [aut], Senbai Kang [aut, cre], Matt Stone [aut], Xing Ren [ctb], Raphael Gottardo [ctb]
LicenseMIT + file LICENSE
URLedward130603.github.io/BayesSpace
Bug Reportshttps://github.com/edward130603/BayesSpace/issues
System RequirementsC++17
Downloads rank762
Source branchRELEASE_3_23
biocViewsClustering, DataImport, GeneExpression, ImmunoOncology, SingleCell, Software, Transcriptomics

Documentation

Download

Dependencies

Depends: R (>= 4.0.0), SingleCellExperiment

Imports: Rcpp (>= 1.0.4.6), stats, methods, purrr, scater, scran, SummarizedExperiment, coda, rhdf5, S4Vectors, Matrix, magrittr, assertthat, arrow, mclust, RCurl, DirichletReg, xgboost (>= 3.0.0), utils, dplyr, rlang, ggplot2, tibble, rjson, tidyr, scales, microbenchmark, BiocFileCache, BiocSingular, BiocParallel

LinkingTo: Rcpp, RcppArmadillo, RcppDist, RcppProgress

Suggests: testthat, knitr, rmarkdown, igraph, spatialLIBD, viridis, patchwork, RColorBrewer, Seurat

Reverse dependencies

Imports Me (1): RegionalST