Aggregate an assay's quantitative features with shared peptide redistribution

This function aggregates the quantitative features of an assay, applying a summarization function (fun) to sets of features. The fcol variable name points to a rowData column that defines how to group the features during aggregate. This variable has to be an adjacency matrix. This function uses inner.aggregate.iter() to aggregate quantitative data.

The list of agregation methods can be obtained with the function aggregateMethods(). This function compiles both methods from the packages DaparToolshed and QFeatures.

Usage

aggregateRedistribution(object, ...)

# S4 method for class 'QFeatures'
aggregateRedistribution(
  object,
  i,
  name = "newAssay",
  fcol,
  init.method = "Mean",
  method = "Mean",
  ponderation = "Global",
  n = NULL,
  uniqueiter = FALSE,
  max_iter = 500
)

# S4 method for class 'SummarizedExperiment'
aggregateRedistribution(
  object,
  fcol,
  init.method = "Mean",
  method = "Mean",
  ponderation = "Global",
  n = NULL,
  uniqueiter = FALSE,
  conds,
  max_iter = 500
)

Arguments

object

An instance of class QFeatures or SummarizedExperiment

...

xxx

i

The index or name of the assay which features will be aggregated the create the new assay.

name

A character(1) naming the new assay. Default is newAssay. Note that the function will fail if there's already an assay with name.

fcol

A character(1) naming a rowdata variable (of assay i in case of a QFeatures) defining how to aggregate the features of the assay. This variable is a (possibly sparse) matrix. See below for details.

init.method

A function used for initializing the aggregation. Available functions are Sum, Mean, Median, medianPolish or robustSummary. See inner.aggregate.iter() for details.

method

A function used for the aggregation. Available functions are Sum, Mean, Median or medianPolish. See inner.aggregate.iter() for details.

ponderation

A character(1) defining what to consider to create the coefficient for redistribution of shared peptides. Available values are Global (default), Condition or Sample.

n

A numeric(1) specifying the number of peptides to use for each protein. If NULL, all peptides are considered.

uniqueiter

xxx

max_iter

A numeric(1) setting the maximum number of iteration.

conds

xxx

Value

A QFeatures object with an additional assay or a SummarizedExperiment object (or subclass thereof).

Details

This function uses inner.aggregate.iter() to aggregate quantitative data.

Iterative aggregation function

xxxxxx xxxxx

Quantitative metadata aggregation

The function to aggregate the quantitative metadata is aggQmetadat()

See also

The QFeatures vignette provides an extended example and the Aggregation vignette, for a complete quantitative proteomics data processing pipeline.

Examples

## ---------------------------------------
## An example QFeatures with PSM-level data
## ---------------------------------------
if (FALSE) { # \dontrun{
data(ft, package='DaparToolshed')
library(SummarizedExperiment)
ft

## Aggregate peptides into proteins
## using the adjacency matrix
feat1 <- aggregateRedistribution(object = ft,
i = 1,
name = 'aggregated',
fcol = 'adjacencyMatrix',
init.method = 'Mean',
method = 'Mean')
feat1

assay(feat1[[1]])
assay(feat1[[2]])
aggcounts(feat1[[2]])
assay(feat1[[3]])
aggcounts(feat1[[3]])
rowData(feat1[[2]])
} # }