filter_regions: Filtering process prior to running Melissa

Description Usage Arguments Details Value Author(s) See Also Examples

Description

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

Usage

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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

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# 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)

andreaskapou/Melissa documentation built on June 12, 2020, 5:54 p.m.