AdjMatFilters | R Documentation |
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.
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)
object |
An object of class |
fun |
A |
top |
A |
An object of class function
of length 1.
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()
:
Samuel Wieczorek
The QFeatures-filtering-oneSE man page for the class FunctionFilter
.
#------------------------------------------------ # 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 )
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