| cellembedding_seurat | R Documentation | 
This function computes cell embedding using the CAESAR framework with FAST for dimensionality reduction and spatial adjacency weights. It integrates variable feature selection and spatial adjacency information to generate low-dimensional representations for cells.
cellembedding_seurat(
  seu,
  adjm,
  assay = NULL,
  slot = "data",
  nfeatures = 2000,
  q = 50,
  reduction.name = "caesar",
  var.features = NULL,
  ...
)
seu | 
 A Seurat object. The Seurat object should contain gene expression data and be preprocessed with variable features identified.  | 
adjm | 
 A spatial adjacency matrix representing the relationships between cells or spots in spatial transcriptomic data.  | 
assay | 
 A character string specifying which assay to use from the Seurat object. If NULL, the function will use the default assay.  | 
slot | 
 The data slot to use for feature extraction (e.g., "data", "counts"). Default is "data".  | 
nfeatures | 
 The number of features to select for analysis. Default is 2000.  | 
q | 
 An integer specifying the number of dimensions for the reduced embeddings. Default is 50.  | 
reduction.name | 
 A character string specifying the name for the dimensional reduction. Default is "caesar".  | 
var.features | 
 A vector of variable features (genes) to use for the embedding. If NULL, the function will use variable features stored in the Seurat object.  | 
... | 
 Additional arguments passed to 'FAST_run'.  | 
The modified Seurat object with the co-embedding results (cell and gene embeddings) stored in the specified dimensional reduction slot.
FAST_run for the main FAST dimensionality reduction algorithm.
data(toydata)
seu <- toydata$seu
pos <- toydata$pos
adjm <- ProFAST::AddAdj(as.matrix(pos), radius.upper = 200)
seu <- cellembedding_seurat(
    seu = seu,
    adjm = adjm
)
print(seu)
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