Alias for sc_optimize for compatibility
Usage
SCCAF_optimize(
X,
labels,
classifier = "LR",
penalty = "l1",
lambda = NULL,
test_size = 0.5,
n_per_class = 100,
cv = 5,
n_iter = 3,
r1_cutoff = 0.1,
r2_cutoff = 0.05,
r1_mode = "1",
use_r1_only = FALSE,
use_r2_only = FALSE,
use_distance = FALSE,
dist_cutoff = 8,
use_projection = FALSE,
connection_matrix = NULL,
resolution = 1,
seed = 1,
n_cores = NULL,
verbose = TRUE
)Arguments
- X
Expression/feature matrix (cells x features)
- labels
Current cluster labels
- classifier
Classifier type: "LR", "RF", "SVM", etc.
- penalty
For LR: regularization type
- lambda
For LR: regularization strength
- test_size
Fraction for test set
- n_per_class
Max samples per class in training
- cv
Cross-validation folds (0 to skip)
- n_iter
Number of sampling iterations for confusion matrix (default: 3)
- r1_cutoff
Threshold for R1-normalized confusion (default: 0.1)
- r2_cutoff
Threshold for R2-normalized confusion (default: 0.05)
- r1_mode
R1 normalization mode: "1" or "2" (default: "1", as in SCCAF)
- use_r1_only
Use only R1 normalization for merging decisions
- use_r2_only
Use only R2 normalization for merging decisions
- use_distance
Use distance matrix in merging decision (default: FALSE)
- dist_cutoff
Distance cutoff for merging (default: 8.0)
- use_projection
Use self-projection labels for subsequent iterations (default: FALSE)
- connection_matrix
Optional connection matrix for constrained merging
- resolution
Louvain resolution for merging (default: 1.0)
- seed
Random seed
- n_cores
Number of cores for parallel processing
- verbose
Print progress