cellembedding_image_seurat: Compute Spatial-Aware Cell Embeddings with Image Information

In CAESAR.Suite: CAESAR: a Cross-Technology and Cross-Resolution Framework for Spatial Omics Annotation

Compute Spatial-Aware Cell Embeddings with Image Information

Description

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

Usage

cellembedding_image_seurat(
  seu,
  adjm,
  assay = NULL,
  slot = "data",
  q = 10,
  approx_Phi = FALSE,
  reduction.name = "caesar",
  var.features = NULL,
  ...
)

Arguments

seu	A Seurat object containing gene expression data. The object should have variable features identified prior to running this function.
adjm	A spatial adjacency matrix representing relationships between cells or spots.
assay	A character string specifying which assay to use from the Seurat object. If NULL, the function will use the default assay set in the Seurat object.
slot	The data slot to use for feature extraction (e.g., "data", "scale.data"). Default is "data".
q	An integer specifying the number of dimensions for the reduced embeddings. Default is 10.
approx_Phi	Logical, indicating whether to use an approximate method for estimating the Phi matrix. Default is FALSE.
reduction.name	A character string specifying the name for the dimensional reduction result. Default is "caesar".
var.features	A vector of variable features (genes) to use for the analysis. If NULL, the function will automatically use the variable features stored in the Seurat object.
...	Additional arguments passed to 'cellembedding_image_matrix'.

Value

The modified Seurat object with the cell embedding results stored in the specified dimensional reduction slot.

See Also

cellembedding_image_matrix for additional arguments used to compute cell embeddings.

CAESAR.Suite documentation built on April 3, 2025, 10:32 p.m.