filter_regions: Filtering process prior to running Melissa

In andreaskapou/Melissa: Bayesian clustering and imputationa of single cell methylomes

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 [e-mail]

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)

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