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. (2023) <doi:10.1038/s41467-023-35947-w>.

Version: 1.6.4
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, ggpubr, graphics, colorspace, Rcpp (≥ 1.0.5)
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2024-01-25
Author: Wei Liu [aut, cre], Yi Yang [aut], Jin Liu [aut]
Maintainer: Wei Liu <wei.liu at duke-nus.edu.sg>
BugReports: https://github.com/feiyoung/PRECAST/issues
License: GPL-3
URL: https://github.com/feiyoung/PRECAST
NeedsCompilation: yes
Materials: README
CRAN checks: PRECAST results

Documentation:

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

Downloads:

Package source: PRECAST_1.6.4.tar.gz
Windows binaries: r-devel: PRECAST_1.6.4.zip, r-release: PRECAST_1.6.4.zip, r-oldrel: PRECAST_1.6.4.zip
macOS binaries: r-release (arm64): PRECAST_1.6.4.tgz, r-oldrel (arm64): PRECAST_1.6.4.tgz, r-release (x86_64): PRECAST_1.6.4.tgz
Old sources: PRECAST archive

Reverse dependencies:

Reverse imports: ProFAST

Linking:

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