adjacency-matrix-filter: Filter a peptide assay on the basis of its adjacency matrix.

In samWieczorek/Dapar2: Tools for the Differential Analysis of Proteins Abundance with R

Filter a peptide assay on the basis of its adjacency matrix.

Description

These functions filters (delete) peptides of an assay, applying a function on peptides and proteins. They can be used alone but the usual usage is to create an instance of a class FunctionFilter and to pass it to the function filterFeaturesOneSE in order to create a new assay, embedded into the QFeatures object.

Usage

AdjMatFilters

AdjMatFilters()

allPeptides(object, ...)

specPeptides(object, ...)

subAdjMat_specificPeptides(X)

sharedPeptides(object, ...)

subAdjMat_sharedPeptides(X)

topnFunctions()

topnPeptides(object, fun, top)

subAdjMat_topnPeptides(X, qData, fun, top)

Arguments

object	An object of class SummarizedExperiment
fun	A list() of additional parameters
top	A integer(1) which is the number of xxx

Format

An object of class function of length 1.

Details

This function builds an intermediate matrix with scores for each peptide based on 'fun' parameter. Once this matrix is built, one select the 'n' peptides which have the higher score

The list of filter functions is given by adjMatFilters():

• specPeptides(): returns a new assay of class SummazizedExperiment with only specific peptides;

• sharedpeptides(): returns a new assay of class SummazizedExperiment with only shared peptides;

• topnPeptides(): returns a new assay of class SummazizedExperiment with only the 'n' peptides which best satisfies the condition. The condition is represented by functions which calculates a score for each peptide among all samples. The list of these functions is given by topnFunctions():

Author(s)

Samuel Wieczorek

See Also

The QFeatures-filtering-oneSE man page for the class FunctionFilter.

Examples

#------------------------------------------------
# This function will keep only specific peptides
#------------------------------------------------

f1 <- FunctionFilter('specPeptides', list())

#------------------------------------------------
# This function will keep only shared peptides
#------------------------------------------------

f2 <- FunctionFilter('sharedPeptides', list())

#------------------------------------------------
# This function will keep only the 'n' best peptides
# w.r.t the quantitative sum of each peptides among 
# all samples
#------------------------------------------------

f3 <- FunctionFilter('topnPeptides', fun = 'rowSums', top = 2)

#------------------------------------------------------
# To run the filter(s) on the dataset, use [xxx()]
# IF several filters must be used, store them in a list
#------------------------------------------------------

data(ft)
lst.filters <- list()
lst.filters <- append(lst.filters, f1)
lst.filters <- append(lst.filters, f3)
 
ft <- filterFeaturesOneSE(object = ft,
                          i = 1,
                          name = 'filtered', 
                          filters = lst.filters
                          )
                          

samWieczorek/Dapar2 documentation built on May 13, 2022, 9:23 a.m.