impute_met_files {Melissa} | R Documentation

## Impute/predict methylation files

### Description

Make predictions of missing methylation states, i.e. perfrom imputation using Melissa. Each file in the directory will be used as input and a new file will be created in `outdir` with an additional column containing the predicted met state (value between 0 and 1). Note that predictions will be made only on `annotation regions` that were used for training Melissa. Check `impute_test_met`, if you want to make predictions only on test data.

### Usage

    impute_met_files(
      met_dir,
      outdir = NULL,
      obj,
      anno_region,
      basis = NULL,
      is_predictive = TRUE,
      no_cores = NULL
    )

### Arguments

`met_dir` | Directory of methylation files, each file corresponds to a single cell. It should contain three columns <chr> <pos> <met_state> (similar to the input required by `create_melissa_data_obj`), where `met_state` can be any value that denotes missing CpG information, e.g. -1. Note that files can contain also CpGs for which we have coverage information, and we can check the predictions made by Melissa, hence the value can also be 0 (unmet) or (1) met. Predictions made by Melissa, will not change the <met_state> column. Melissa will just add an additional column named <predicted>.
`outdir` | Directory to store the output files for each cell with exactly the same name. If NULL, then a directory called 'imputed' inside 'met_dir' will be created by default.
`obj` | Output of Melissa inference object.
`anno_region` | Annotation region object. This will be the outpuf of `create_melissa_data_obj` function, e..g melissa_data$anno_region. This is required to select those regions that were used to train Melissa.
`basis` | Basis object, if NULL we perform imputation using Melissa, otherwise using BPRMeth (then `obj` should be BPRMeth output).
`is_predictive` | Logical, use predictive distribution for imputation, or choose the cluster label with the highest responsibility.
`no_cores` | Number of cores to be used for parallel processing of data.

### Value

A new directory `outdir` containing files (cells) with predicted / imputed methylation states per CpG location.

### Author(s)

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

### See Also

`create_melissa_data_obj`, `melissa`, `filter_regions`

### Examples

    ## Not run:
    # Met directory
    met_dir <- "name_of_met_dir"
    # Annotation file name
    anno_file <- "name_of_anno_file"
    # Create data object
    melissa_data <- create_melissa_data_obj(met_dir, anno_file)
    # Run Melissa
    melissa_obj <- melissa(X = melissa_data$met, K = 2)
    # Annotation object
    anno_region <- melissa_data$anno_region

    # Peform imputation
    impute_met_dir <- "name_of_met_dir_for_imputing_cells"
    out <- impute_met_files(met_dir = impute_met_dir, obj = melissa_obj,
                            anno_region = anno_region)

    ## End(Not run)

[Package Melissa version 1.6.0 Index]