Evaluate Prediction Accuracy and Confusion by Cell Type

Computes overall accuracy, per-class accuracy, and confusion matrix given predicted vs. true labels.

Usage

evaluatePredictionPerformance(
  prediction_df = NULL,
  truth = NULL,
  low_conf_label = "unknown"
)

Arguments

prediction_df

Output from predictRankModel (return_confidence = TRUE) or filtered version.

truth

A vector of true labels matching prediction_df.

low_conf_label

Character. Label used for low-confidence predictions (e.g., "unknown" or "uncertain"). These will be excluded from evaluation. Set to NULL to include all predictions.

Value

A list with:

overall_accuracy

Proportion of correct predictions

per_class_accuracy

Accuracy per true cell type

confusion_matrix

Contingency table (true × predicted)

Examples

# Read in single-cell reference data
seu_sc <- readRDS(system.file("extdata", "seu_sc.rds",
    package = "RankMap"
))
#> Loading required namespace: SeuratObject

# Read in Xenium spatial data
seu_xen <- readRDS(system.file("extdata", "seu_xen.rds",
    package = "RankMap"
))

# Predict cell type for Xenium data
prediction_df <- RankMap(
    ref_data = seu_sc,
    ref_labels = seu_sc$cell_type,
    new_data = seu_xen
)

performance <- evaluatePredictionPerformance(
    prediction_df = prediction_df,
    truth = seu_xen$cell_type_SingleR
)
performance
#> $overall_accuracy
#> [1] 0.9466667
#> 
#> $per_class_accuracy
#> Basal    LP Tumor 
#>  0.96  0.90  0.98 
#> 
#> $confusion_matrix
#>        Predicted
#> True    Basal LP Tumor
#>   Basal    48  2     0
#>   LP        4 45     1
#>   Tumor     1  0    49
#>