filterByStepwiseCompleteness: Filter co-elution feature table by stepwise completeness...

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

Description

Filter co-elution feature table by two consecutive completeness cutoffs to treat small and large complexes differenetly.

Usage

filterByStepwiseCompleteness(feature_table, min_subunits_annotated = 4,
  completeness_vector = c(0.75, 0.5), level = "hypothesis")

Arguments

feature_table	data.table as reported by findComplexFeatures or findProteinFeatures.
min_subunits_annotated	Integer specifying the number of annotated hypothesis components (peptides / proteins). This is the cutoff for using the two different completeness cutoffs as provided by the completeness_vector. Default is 4.
completeness_vector	Numeric vector of length 2. The first value is the completeness cutoff apllied to all hypotheses with <= min_subunits_annotated subunits and the secind value is applied to all hypotheses with more than min_subunits_annotated subunits. Default is c(0.75,0.5)
level	Character string defining level of filterng, allowed values are "feature" or "hypothesis". "feature" filters all features by their completeness. "hypothesis" filters by the completeness of the of the largest feature within one hypothesis. Default is "hypothesis".

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 subunit cutoff of 4 and the completness cutoffs
## 0.75 for hypotheses <= 4 subunits and 0.5 for hypotheses with > 4 annotated subunits.
filteredComplexFeatures <- filterByStepwiseCompleteness(feature_table=complexFeatures,
                                          min_subunits_annotated=4,
                                          completeness_vector=c(0.75,0.5),
                                          level="hypothesis")
## Run summary function on filtered data:
summarizeFeatures(filteredComplexFeatures)

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