eval_imputation_performance: Evaluate imputation performance
In Melissa: Bayesian clustering and imputationa of single cell methylomes

Description Usage Arguments Value Author(s) See Also Examples

eval_imputation_performance is a wrapper function for computing imputation/clustering performance in terms of different metrics, such as AUC and precision recall curves.

1	eval_imputation_performance(obj, imputation_obj)

`obj`	Output of Melissa inference object.
`imputation_obj`	List containing two vectors of equal length, corresponding to true methylation states and predicted/imputed methylation states.

The 'melissa' object, with an additional slot named 'imputation', containing the AUC, F-measure, True Positive Rate (TPR) and False Positive Rate (FPR), and Precision Recall (PR) curves.

C.A.Kapourani C.A.Kapourani@ed.ac.uk

create_melissa_data_obj, melissa, impute_test_met, filter_regions, eval_imputation_performance, eval_cluster_performance

# First take a subset of cells to efficiency
# Extract synthetic data
dt <- melissa_synth_dt

# Partition to train and test set
dt <- partition_dataset(dt)

# Create basis object from BPRMeth package
basis_obj <- BPRMeth::create_rbf_object(M = 3)

# Run Melissa
melissa_obj <- melissa(X = dt$met, K = 2, basis = basis_obj, vb_max_iter = 10,
  vb_init_nstart = 1, is_parallel = FALSE, is_verbose = FALSE)

imputation_obj <- impute_test_met(obj = melissa_obj, test = dt$met_test)

melissa_obj <- eval_imputation_performance(obj = melissa_obj,
                                           imputation_obj = imputation_obj)

cat("AUC: ", melissa_obj$imputation$auc)