impute_met_state {Melissa}R Documentation

Impute/predict methylation states

Description

Make predictions of missing methylation states, i.e. perfrom imputation using Melissa. This requires keepin a subset of data as a held out test set during Melissa inference.

Usage

impute_met_state(obj, test, basis = NULL, is_predictive = TRUE,
  return_test = FALSE)

Arguments

obj

Output of Melissa inference object.

test

Test data to evaluate performance.

basis

Basis object, if NULL we perform imputation using Melissa, otherwise using BPRMeth.

is_predictive

Logical, use predictive distribution for imputation, or choose the cluster label with the highest responsibility.

return_test

Whether or not to return a list with the predictions.

Value

A list containing two vectors, the true methylation state and the predicted/imputed methylation states.

Author(s)

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

See Also

create_melissa_data_obj, melissa, filter_regions, eval_imputation_performance, eval_cluster_performance

Examples

# 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_met_state(obj = melissa_obj, test = dt$met_test)


[Package Melissa version 1.0.0 Index]