filter_regions {Melissa} | R Documentation

## Filtering process prior to running Melissa

### Description

Fuctions for filter genomic regions due to (1) low CpG coverage, (2) low coverage across cells, or (3) low mean methylation variability.

### Usage

    filter_by_cpg_coverage(obj, min_cpgcov = 10)

    filter_by_coverage_across_cells(obj, min_cell_cov_prcg = 0.5)

    filter_by_variability(obj, min_var = 0.1)

### Arguments

`obj` | Melissa data object.
`min_cpgcov` | Minimum CpG coverage for each genomic region.
`min_cell_cov_prcg` | Threshold on the proportion of cells that have coverage for each region.
`min_var` | Minimum variability of mean methylation across cells, measured in terms of standard deviation.

### Details

The (1) 'filter_by_cpg_coverage' function does not actually remove the region, it only sets NA to those regions. The (2) 'filter_by_coverage_across_cells' function keeps regions from which we can share information across cells. The (3) 'filter_by_variability' function keeps variable regions which are informative for cell subtype identification.

### Value

The filtered Melissa data object

### Author(s)

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

### See Also

`melissa`, `create_melissa_data_obj`

### Examples

    # Run on synthetic data from Melissa package
    filt_obj <- filter_by_cpg_coverage(melissa_encode_dt, min_cpgcov = 20)

    # Run on synthetic data from Melissa package
    filt_obj <- filter_by_coverage_across_cells(melissa_encode_dt,
                                                min_cell_cov_prcg = 0.7)

    # Run on synthetic data from Melissa package
    filt_obj <- filter_by_variability(melissa_encode_dt, min_var = 0.1)

[Package Melissa version 1.6.0 Index]