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:10.1093/nar/gkac219>.
GiRaF, MASS, Matrix, mclust, methods, purrr, utils, S4Vectors,
RColorBrewer, Seurat, cowplot, scater, pbapply, patchwork,
ggthemes, dplyr, ggplot2, stats, DR.SC, scales, ggrastr, Rcpp
(>= 1.0.5)

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>.
GiRaF, MASS, Matrix, mclust, methods, purrr, utils, Seurat,
cowplot, scater, pbapply, patchwork, ggthemes, dplyr, ggplot2,
stats, DR.SC, scales, Rcpp (>= 1.0.5)

GiRaF, MASS, Matrix, mclust, methods, purrr, utils, Seurat,
cowplot, patchwork, scater, pbapply, ggthemes, dplyr, ggplot2,
stats, DR.SC, scales, Rcpp (>= 1.0.5)

Interactive Document for Preprocessing the Dataset
An interactive document for preprocessing the dataset using  'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/PREPShiny/>.

Parametric Simplex Method for Sparse Learning
Implements a unified framework of parametric simplex method for a variety of sparse learning problems (e.g., Dantzig selector (for linear regression), sparse quantile regression, sparse support vector machines, and compressive sensing) combined with efficient hyper-parameter selection strategies. The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) <https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning.pdf>.