Embedding and Clustering with Alignment for Spatial Omics Datasets
Add embeddings for a Seurat object
Add adjacency matrix list for a PRECASTObj object
Add model settings for a PRECASTObj object
Add tSNE embeddings for a Seurat object
Add UMAP embeddings for a Seurat object
Boxplot for a matrix
Choose color schema from a palette
Coordinates rotation for visualization
Create the PRECAST object with preprocessing step.
Low-dimensional embeddings' plot
Heatmap for spots-by-feature matrix
Draw a figure using a group of ggplot objects
Spatial expression heatmap
Set the first letter of a string vector to captial
Calculate adjacency matrix by user-specified number of neighbors
Calculate adjacency matrix for regular spatial coordinates.
ICM-EM algorithm implementation with organized paramters
ICM-EM algorithm implementation
Integrate multiple SRT data
PRECAST model setting
Spatial RGB heatmap
Scatter plot for two-dimensional embeddings
A simple PRECASTObj for example
Fit a PRECAST model
Select common genes for multiple data batches
Select best PRECAST model from candidated models
Spatial heatmap
Volin/boxplot plot
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>.