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Single Cell Clustering Assessment

Source: R/assessment.R
sc_assessment.Rd

Main assessment function with user-friendly interface

Usage

sc_assessment(
  X,
  labels,
  classifier = "LR",
  penalty = "l1",
  lambda = NULL,
  test_size = 0.5,
  n_per_class = 100,
  cv = 5,
  seed = 1,
  n_cores = NULL,
  verbose = TRUE
)

Arguments

X

Expression/feature matrix (cells x features). Can be sparse.

labels

Cluster labels for each cell

classifier

Classifier type: "LR", "RF", "SVM", "NB", "DT", "XGB", "RANGER"

penalty

For LR: regularization type "l1", "l2", or "elasticnet"

lambda

For LR: regularization strength. If NULL, uses CV to select

test_size

Fraction of data for testing (default: 0.5)

n_per_class

Maximum samples per class in training set. If NULL, uses test_size

cv

Number of cross-validation folds on training set (0 to skip CV)

seed

Random seed for reproducibility

n_cores

Number of cores for parallel processing (NULL = auto-detect)

verbose

Print progress messages

Value

An scClustEval result object

See also

self_projection, RunAssessment

Examples

if (FALSE) { # \dontrun{
# Assess clustering quality
result <- sc_assessment(
  X = expression_matrix,
  labels = seurat_object$seurat_clusters
)

# Print summary
print(result)

# Plot ROC curves
plot_roc(result)
} # }