PRECAST: Embedding and Clustering with Alignment for Spatial Datasets

An efficient data integration method is provided for multiple spatial transcriptomics data with non-cluster-relevant effects such as the complex batch effects. It unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, requiring only partially shared cell/domain clusters across datasets. More details can be referred to Wei Liu, et al. (2022) <doi:10.1101/2022.06.26.497672>.

Version: 1.3
Depends: parallel, gtools, R (≥ 4.0.0)
Imports: GiRaF, MASS, Matrix, mclust, methods, purrr, utils, Seurat, cowplot, patchwork, scater, pbapply, ggthemes, dplyr, ggplot2, stats, DR.SC, scales, Rcpp (≥ 1.0.5)
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2022-10-18
Author: Wei Liu [aut, cre], Yi Yang [aut], Jin Liu [aut]
Maintainer: Wei Liu <wei.liu at>
License: GPL-3
NeedsCompilation: yes
Materials: README
CRAN checks: PRECAST results


Reference manual: PRECAST.pdf
Vignettes: PRECAST: Human Breast Cancer Data Analysis
PRECAST: DLPFC Single Sample Analysis
PRECAST: simulation


Package source: PRECAST_1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): PRECAST_1.3.tgz, r-oldrel (arm64): PRECAST_1.3.tgz, r-release (x86_64): PRECAST_1.3.tgz, r-oldrel (x86_64): PRECAST_1.3.tgz
Old sources: PRECAST archive


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