Performs DCA metric learning for single-cell data. This is the original metric learning
method used in scLearn, implementing the algorithm from the dml package.
Usage
runDCA(
high_varGenes,
expression_profile,
sample_information,
strength = 0.1,
seed = 1,
verbose = TRUE
)
Arguments
- high_varGenes
Character vector of high-variance genes
- expression_profile
Numeric matrix (genes x cells)
- sample_information
Named vector of cell type labels
- strength
Subsampling strength (default: 0.1)
- seed
Random seed (default: 1)
- verbose
Print progress messages (default: TRUE)
Value
List containing:
expression_profile_trans: Transformed matrix (genes x cells)
expression_profile_origin: Original matrix
trans_matrix: Learned transformation matrix
sample_information: Input cell labels