This function can recover SEs from scRNA-seq data or single-cell spatial data by assigning each single cell to an SE or NonSE.
Usage
RecoverSE(
dat,
celltypes = NULL,
scale = TRUE,
ncell.per.run = 500,
Ws = NULL,
min.score = 0.6,
ncores = 1
)Arguments
- dat
A numeric (sparse) matrix of gene expression data, from single-cell spatial transcriptomics or scRNA-seq data.
- celltypes
Character vector specifying the cell type annotations for cells included in the gene expression `dat`. If you're using the default model, cell types including B, CD4T, CD8T, NK, Plasma, Macrophage, DC, Fibroblast, Endothelial are expected.
- scale
Logical indicating whether to perform unit-variance normalization (default: TRUE). Change it with caution.
- ncell.per.run
Integer specifying the maximum number of cells per NMF prediction run to avoid memory issues.
- Ws
A list of cell-type-specific W matrices used to recover SE-specific cell states. Each element in the list should be named after the corresponding cell type and contain a W matrix from an NMF model.
- min.score
A numeric threshold (0-1) specifying the minimum prediction score for SE classification; cells with lower scores are assigned to NonSE.
- ncores
Integer specifying the number of CPU cores to use for parallel processing (default: 1).