R: Spatially-Aware Cell Clustering Algorithm with Cluster Significant Assessment

# Spatially-Aware Cell Clustering Algorithm with Cluster Significant Assessment

## Documentation for package ‘coFAST’ version 0.2.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

AddAdj | Calculate the adjacency matrix given a spatial coordinate matrix
AddCluster | Find clusters for SRT data
Addcoord2embed | Add the spatial coordinates to the reduction slot
AggregationScore | Calculate the aggregation score for specific clusters
coembedding_umap | Calculate UMAP projections for coembedding of cells and features
coembed_plot | Coembedding dimensional reduction plot
coFAST | Cell-feature coembedding for SRT data
CosMx_subset | A CosMix spatial transcriptomics data
diagnostic.cor.eigs | Determine the dimension of low dimensional embedding
diagnostic.cor.eigs.default | Determine the dimension of low dimensional embedding
diagnostic.cor.eigs.Seurat | Determine the dimension of low dimensional embedding
find.signature.genes | Find the signature genes for each group of cell/spots
get.top.signature.dat | Obtain the top signature genes and related information
NCFM | Cell-feature coembedding for scRNA-seq data
pbmc3k_subset | A toy single-cell RNA-seq data
pdistance | Calculate the cell-feature distance matrix
top5_signatures | A dataframe including top five signature genes