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Package: PRECAST 1.9

Wei Liu

PRECAST: Embedding and Clustering with Alignment for Spatial Omics 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>.

Authors:Wei Liu [aut, cre], Yi Yang [aut], Jin Liu [aut]

PRECAST_1.9.tar.gz
PRECAST_1.9.zip(r-4.7-x86_64)PRECAST_1.9.zip(r-4.7-arm64)PRECAST_1.9.zip(r-4.6-x86_64)PRECAST_1.9.zip(r-4.6-arm64)
PRECAST_1.9.tar.gz(r-4.7-arm64)PRECAST_1.9.tar.gz(r-4.7-x86_64)PRECAST_1.9.tar.gz(r-4.6-arm64)PRECAST_1.9.tar.gz(r-4.6-x86_64)
PRECAST_1.9.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
PRECAST/json (API)

# Install 'PRECAST' in R:

install.packages('PRECAST', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/feiyoung/precast/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

Human_HK_genes - Human housekeeping genes database
Mouse_HK_genes - Mouse housekeeping genes database
PRECASTObj - A simple PRECASTObj for example

On CRAN:

openblas cpp

4.83 score 135 scripts 918 downloads 28 exports 213 dependencies

Last updated from:d656e3611e. Checks:10 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	OK	361
linux-devel-x86_64	OK	471
source / vignettes	OK	424
linux-release-arm64	OK	372
linux-release-x86_64	OK	461
windows-devel-arm64	OK	506
windows-devel-x86_64	OK	614
windows-release-arm64	OK	584
windows-release-x86_64	OK	448
wasm-release	OK	225

Exports:Add_embed AddAdjList AddParSetting AddTSNE AddUMAP boxPlot chooseColors coordinate_rotate CreatePRECASTObject dimPlot doHeatmap drawFigs featurePlot firstup getAdj_fixedNumber getAdj_reg ICM.EM ICM.EM_structure IntegrateSpaData model_set plot_RGB plot_scatter PRECAST SelectModel SelectModel.PRECASTObj SelectModel.SeqK_PRECAST_Object SpaPlot volinPlot

Dependencies:abind askpass assorthead backports base64enc beachmat beeswarm BH Biobase BiocGenerics BiocNeighbors BiocParallel BiocSingular bitops boot broom bslib cachem car carData caTools cli cluster codetools colorspace commonmark CompQuadForm corrplot cowplot cpp11 crosstalk curl data.table DelayedArray deldir Deriv digest doBy dotCall64 dplyr dqrng DR.SC evaluate farver fastDummies fastmap fitdistrplus FNN fontawesome forecast formatR Formula fracdiff fs futile.logger futile.options future future.apply generics GenomicRanges ggbeeswarm ggplot2 ggpubr ggrepel ggridges ggsci ggsignif ggthemes globals glue goftest gplots gridExtra gtable gtools here highr htmltools htmlwidgets httpuv httr ica igraph IRanges irlba isoband jquerylib jsonlite KernSmooth knitr labeling lambda.r later lattice lazyeval lifecycle listenv lme4 lmtest magrittr MASS Matrix MatrixGenerics MatrixModels matrixStats mclust memoise mgcv mime miniUI minqa modelr nlme nloptr nnet numDeriv openssl otel parallelly patchwork pbapply pbkrtest pheatmap pillar pkgconfig plotly plyr png polyclip polynom progressr promises purrr quantreg R6 RANN rappdirs rbibutils RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppHNSW RcppML RcppProgress RcppThread RcppTOML Rdpack reformulas reshape2 reticulate rlang rmarkdown ROCR rprojroot RSpectra rstatix rsvd Rtsne S4Arrays S4Vectors S7 sass ScaledMatrix scales scater scattermore sctransform scuttle Seqinfo Seurat SeuratObject shiny SingleCellExperiment sitmo snow sourcetools sp spam SparseArray SparseM spatstat.data spatstat.explore spatstat.geom spatstat.random spatstat.sparse spatstat.univar spatstat.utils stringi stringr SummarizedExperiment survival sys tensor tibble tidyr tidyselect timeDate tinytex urca utf8 uwot vctrs vipor viridis viridisLite withr xfun xtable XVector yaml zoo

PRECAST: DLPFC Single Sample Analysis

Compare PRECAST with DR-SC in analyzing one sample | Prepare the PRECASTObject. | Add the model setting | Fit PRECAST

Rendered fromPRECAST.DLPFC.Rmdusingknitr::rmarkdown

Last update: 2025-10-01
Started: 2022-10-18

PRECAST: Four DLPFC Sample Analysis

Compare PRECAST with DR-SC in analyzing one sample | Prepare the PRECASTObject. | Add the model setting | Fit PRECAST

Rendered fromPRECAST.DLPFC4.Rmdusingknitr::rmarkdown

Last update: 2025-10-01
Started: 2024-01-26

PRECAST: Human Breast Cancer Data Analysis

Create a PRECASTObject object | Prepare the PRECASTObject with preprocessing step. | Add the model setting | Fit PRECAST using this data | Fit PRECAST

Rendered fromPRECAST.BreastCancer.Rmdusingknitr::rmarkdown

Last update: 2025-10-01
Started: 2022-10-18

PRECAST: simulation

Rendered fromPRECAST.Simu.Rmdusingknitr::rmarkdown

Last update: 2025-10-01
Started: 2022-06-22

PRECAST: installation

Install the PRECAST | Setup on Linux or MacOS system

Rendered fromPRECAST.Rmdusingknitr::rmarkdown

Last update: 2022-06-22
Started: 2022-06-22

Help page	Topics
Add embeddings for a Seurat object	Add_embed
Add adjacency matrix list for a PRECASTObj object	AddAdjList
Add model settings for a PRECASTObj object	AddParSetting
Add tSNE embeddings for a Seurat object	AddTSNE
Add UMAP embeddings for a Seurat object	AddUMAP
Boxplot for a matrix	boxPlot
Choose color schema from a palette	chooseColors
Coordinates rotation for visualization	coordinate_rotate
Create the PRECAST object with preprocessing step.	CreatePRECASTObject
Low-dimensional embeddings' plot	dimPlot
Heatmap for spots-by-feature matrix	doHeatmap
Draw a figure using a group of ggplot objects	drawFigs
Spatial expression heatmap	featurePlot
Set the first letter of a string vector to captial	firstup
Calculate adjacency matrix by user-specified number of neighbors	getAdj_fixedNumber
Calculate adjacency matrix for regular spatial coordinates.	getAdj_reg
getneighborhood_fast	getneighborhood_fast
Human housekeeping genes database	Human_HK_genes
ICM-EM algorithm implementation	ICM.EM
ICM-EM algorithm implementation with organized paramters	ICM.EM_structure
Integrate multiple SRT data	IntegrateSpaData
PRECAST model setting	model_set
Mouse housekeeping genes database	Mouse_HK_genes
Spatial RGB heatmap	plot_RGB
Scatter plot for two-dimensional embeddings	plot_scatter
Fit a PRECAST model	PRECAST
A simple PRECASTObj for example	PRECASTObj
Each PRECASTObj object has a number of slots which store information.	PRECASTObj-class
Select common genes for multiple data batches	selectIntFeatures
Select best PRECAST model from candidated models	SelectModel SelectModel.PRECASTObj SelectModel.SeqK_PRECAST_Object
Spatial heatmap	SpaPlot
Volin/boxplot plot	volinPlot

Package: PRECAST 1.9

PRECAST: Embedding and Clustering with Alignment for Spatial Omics Datasets

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)