Probabilistic Factor Analysis for Spatially-Aware Dimension Reduction
Calculate the adjacency matrix given a spatial coordinate matrix
Add FAST model settings for a PRECASTObj object
Coembedding dimensional reduction plot
Calculate UMAP projections for coembedding of cells and features
Determine the dimension of low dimensional embedding
(Varitional) ICM-EM algorithm for implementing FAST model
Fit FAST model for single-section SRT data
(Varitional) ICM-EM algorithm for implementing FAST model with structu...
Run FAST model for a PRECASTObj object
Find the signature genes for each group of cell/spots
Calcuate the the adjusted McFadden's pseudo R-square
Obtain the top signature genes and related information
Integrate multiple SRT data into a Seurat object
Fit an iSC-MEB model using specified multi-section embeddings
Set parameters for FAST model
Cell-feature coembedding for SRT data
Cell-feature coembedding for scRNA-seq data
Calculate the cell-feature distance matrix
Embedding alignment and clustering based on the embeddings from FAST
Fit an iSC-MEB model using the embeddings from FAST
Select housekeeping genes
Transfer gene names from one fortmat to the other format
Probabilistic factor analysis for spatially-aware dimension reduction across multi-section spatial transcriptomics data with millions of spatial locations. More details can be referred to Wei Liu, et al. (2023) <doi:10.1101/2023.07.11.548486>.