Filter MiDAS object by frequency

Filter MiDAS object by frequency

filterByFrequency(
  object,
  experiment,
  lower_frequency_cutoff = NULL,
  upper_frequency_cutoff = NULL,
  carrier_frequency = FALSE
)

Arguments

object

MiDAS object.

experiment

String specifying experiment.

lower_frequency_cutoff

Number giving lower frequency threshold. Numbers greater than 1 are interpreted as the number of feature occurrences, numbers between 0 and 1 as fractions.

upper_frequency_cutoff

Number giving upper frequency threshold. Numbers greater than 1 are interpreted as the number of feature occurrences, numbers between 0 and 1 as fractions.

carrier_frequency

Logical flag indicating if carrier frequency should be returned.

Value

Filtered MiDAS object.

Examples

filterByFrequency(object = MiDAS_tut_object, 
                  experiment = "hla_alleles",
                  lower_frequency_cutoff = 0.05,
                  upper_frequency_cutoff = 0.95,
                  carrier_frequency = TRUE)
#> A MiDAS object of 10 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 10:
#>  [1] hla_alleles: SummarizedExperiment with 75 rows and 1000 columns
#>  [2] hla_aa: SummarizedExperiment with 1223 rows and 1000 columns
#>  [3] hla_g_groups: SummarizedExperiment with 46 rows and 1000 columns
#>  [4] hla_supertypes: SummarizedExperiment with 12 rows and 1000 columns
#>  [5] hla_NK_ligands: SummarizedExperiment with 5 rows and 1000 columns
#>  [6] kir_genes: SummarizedExperiment with 16 rows and 1000 columns
#>  [7] kir_haplotypes: SummarizedExperiment with 6 rows and 1000 columns
#>  [8] hla_kir_interactions: SummarizedExperiment with 29 rows and 1000 columns
#>  [9] hla_divergence: matrix with 4 rows and 1000 columns
#>  [10] hla_het: SummarizedExperiment with 9 rows and 1000 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files