filterFeatures: Filter co-elution feature table

In CCprofiler/CCprofiler: Complex feature detection using SEC protein chromatograms.

Description

Filter co-elution feature table according to desired criteria.

Usage

filterFeatures(feature_table, complex_ids = NULL, protein_ids = NULL,
  min_feature_completeness = NULL, min_hypothesis_completeness = NULL,
  min_subunits = NULL, min_peak_corr = NULL,
  min_monomer_distance_factor = 2)

Arguments

feature_table	data.table as reported by findComplexFeatures or findProteinFeatures.
complex_ids	character vector containing all desired complex_id values. Only applicable to complex feature tables. Defaults to NULL.
protein_ids	character vector containing all desired protein_id values. Only applicable to protein feature tables. Defaults to NULL.
min_feature_completeness	Numeric between 0 and 1, specifying the required completeness of a feature reltive to the tested hypothesis (keeps all features if at least one is bigger than the cutoff).
min_hypothesis_completeness	Numeric between 0 and 1, specifying the required completeness of the most complete feature reltive to the tested hypothesis (keeps all features if at least one feature is bigger than the cutoff).
min_subunits	Integer specifying minimum number of subunits in a co-elution feature.
min_peak_corr	Numeric value betwee 0 and 1 specifying minimum peak correlation, defaults to NULL.
min_monomer_distance_factor	Numeric value specifying a factor to multiply the largest monomer molecular weight, defaults to 2. This filters out features that have their apex at a smaller molecular weight than the resulting min_monomer_distance_factor*max(monomer_mw) value. Using this filtering option can, for example, remove complex features that are likely spontaneous co-elutions of the subunits monomers.

Value

The same feture table as teh input, but filtered according to the provided parameters.

Examples

## Load example complex feature finding results:
complexFeatures <- exampleComplexFeatures
## Run summary function:
summarizeFeatures(complexFeatures)
## Filter complex features by a peak correlation of 0.5, a minimum hypothesis
## completeness of 0.5 and a minimum distance to the monomers by a factor of 2:
filteredComplexFeatures <- filterFeatures(complexFeatures,
                                     min_peak_corr=0.5,
                                     min_hypothesis_completeness=0.5,
                                     min_monomer_distance_factor=2)
## Run summary function on filtered data:
summarizeFeatures(filteredComplexFeatures)

CCprofiler/CCprofiler documentation built on May 19, 2021, 7:40 p.m.