CAESAR.Suite (version 0.1.0)

cellembedding_matrix: Compute Spatial-Aware Cell Embeddings

Description

This function computes low-dimensional cell embeddings from a gene-by-cell matrix. The method initializes cell embeddings using approximate PCA and refines them through a linear factor model nested a intrinsical conditional autoregressive model.

Usage

cellembedding_matrix(X, adjm, q = 50, reduction.name = "caesar", ...)

Value

A matrix containing the computed cell embeddings. The number of rows corresponds to the number of cells, and the number of columns corresponds to the specified number of dimensions (`q`).

Arguments

X

A gene-by-cell matrix (e.g., the `data` slot from a Seurat object) that serves as the input data for dimensional reduction.

adjm

A spatial adjacency matrix representing the relationships between cells or spots in spatial transcriptomic data.

q

An integer specifying the number of dimensions to reduce to. Default is 50.

reduction.name

A character string specifying the name of the dimensional reduction method. Default is 'caesar'.

...

Additional parameters passed to `ProFAST::FAST_run`.

See Also

FAST_run for the main FAST dimensionality reduction algorithm.

Examples

data(toydata)

seu <- toydata$seu
pos <- toydata$pos

adjm <- ProFAST::AddAdj(as.matrix(pos), radius.upper = 200)
X <- Seurat::GetAssayData(object = seu, slot = "data", assay = "RNA")
cellembedding <- cellembedding_matrix(
    X = X,
    adjm = adjm
)
print(cellembedding[1:3, 1:3])