Spatially-Aware Cell Clustering Algorithm with Cluster Significant Assessment
Calculate the adjacency matrix given a spatial coordinate matrix
Find clusters for SRT data
Add the spatial coordinates to the reduction slot
Calculate the aggregation score for specific clusters
Coembedding dimensional reduction plot
Calculate UMAP projections for coembedding of cells and features
Cell-feature coembedding for SRT data
Determine the dimension of low dimensional embedding
Find the signature genes for each group of cell/spots
Obtain the top signature genes and related information
Cell-feature coembedding for scRNA-seq data
Calculate the cell-feature distance matrix
A spatially-aware cell clustering algorithm is provided with cluster significance assessment. It comprises four key modules: spatially-aware cell-gene co-embedding, cell clustering, signature gene identification, and cluster significant assessment. More details can be referred to Peng Xie, et al. (2025) <doi:10.1016/j.cell.2025.05.035>.