Predictive Cell Identity Classification Using DE / Top Variable Features

Trains a classifier on simulated features (80% train / 20% test) to verify whether the simulated biological signals are sufficiently predictive of cell identities. If de_features is NULL, the top variable features are automatically selected.

Usage

evaluate_predictive_de_model(
  data,
  group,
  de_features = NULL,
  n_top = 50,
  method = c("knn", "rf", "svm")
)

Arguments

data

Matrix (features x cells).

group

Cell type labels.

de_features

Optional character vector of selected DE feature names.

n_top

Number of top features to auto-select if de_features is NULL (default 50).

method

Classifier: "knn" (default), "rf", or "svm".

Value

A list containing Accuracy, Macro Precision, Macro Recall, and Macro F1 score.

Examples

data(example_scrna, package = "scSimEval")
evaluate_predictive_de_model(example_scrna$ref, group = example_scrna$cell_types)
#> $accuracy
#> [1] 0.625
#> 
#> $precision
#> [1] 0.6181818
#> 
#> $recall
#> [1] 0.6031746
#> 
#> $F1
#> [1] 0.6
#>