Iteratively optimize clustering until target accuracy is reached
Usage
sc_optimize_all(
X,
labels,
min_accuracy = 0.9,
max_rounds = 10,
classifier = "LR",
penalty = "l1",
lambda = NULL,
test_size = 0.5,
n_per_class = 100,
cv = 5,
n_iter = 3,
r1_cutoff = 0.5,
r2_cutoff = 0.05,
r1_step = 0.01,
r2_step = 0.001,
r1_mode = "1",
use_r1_only = FALSE,
use_r2_only = FALSE,
use_distance = FALSE,
dist_cutoff = 8,
use_projection = FALSE,
under_cluster_labels = NULL,
min_outer_iter = 3,
seed = 1,
n_cores = NULL,
verbose = TRUE
)Arguments
- X
Expression/feature matrix (cells x features)
- labels
Initial cluster labels (should be over-clustered)
- min_accuracy
Target minimum accuracy to achieve (default: 0.9)
- max_rounds
Maximum optimization rounds (default: 10)
- classifier
Classifier type
- penalty
For LR: regularization type
- lambda
For LR: regularization strength
- test_size
Fraction for test set
- n_per_class
Max samples per class
- cv
CV folds
- n_iter
Iterations per round for confusion matrix
- r1_cutoff
Initial R1 cutoff (default: 0.5)
- r2_cutoff
Initial R2 cutoff (default: 0.05)
- r1_step
Step to reduce R1 cutoff each outer iteration (default: 0.01)
- r2_step
Step to reduce R2 cutoff each outer iteration (default: 0.001)
- r1_mode
R1 normalization mode: "1" or "2" (default: "1")
- use_r1_only
Use only R1 for merging
- use_r2_only
Use only R2 for merging
- use_distance
Use distance matrix in merging decisions (default: FALSE)
- dist_cutoff
Distance cutoff for merging (default: 8.0)
- use_projection
Use self-projection labels for subsequent iterations (default: FALSE)
- under_cluster_labels
Optional: under-clustering labels as constraint
- min_outer_iter
Minimum outer iterations before allowing convergence
- seed
Random seed
- n_cores
Number of cores
- verbose
Print progress
Value
A list containing:
- final_labels
Final optimized cluster labels
- initial_labels
Initial cluster labels
- round_history
List of results from each round
- accuracy_history
Vector of accuracies per round
- n_clusters_history
Vector of cluster counts per round
- final_accuracy
Final achieved accuracy
- total_rounds
Number of rounds performed
- converged
Whether target accuracy was reached
Details
The optimization proceeds in two levels:
Outer iterations: Progressively lower the R1/R2 cutoffs
Inner rounds: Merge clusters based on current cutoffs
The process continues until:
Target accuracy is reached
Maximum rounds are exceeded
No more clusters can be merged