summarizePeptides: Add information to a peptideSet and summarize peptides
In RGLab/pepStat: Statistical analysis of peptide microarrays

Description Usage Arguments Details Value Author(s) See Also Examples

This function merges the replicates and adds information from a peptide collection to a peptideSet. This collection can include coordinates, alignment information, Z-scales, and other peptide information.

1	summarizePeptides(peptideSet, summary = "median", position = NULL)

`peptideSet`	A `peptideSet`, as created by `makePeptideSet`
`summary`	A `character` string. The method used for merging replicates. Available are: "mean" and "median".
`position`	A `data.frame` or `GRanges` object. A peptide collection such as the ones available in `pepDat`. See details below and vignettes for more information.

The object in the position argument will be passed to create_db, it can either be a GRanges object with a peptide as a metadata column, or a data.frame that can be used to create such GRanges.

Some peptide collections can be found in the pepDat package.

An object of class peptideSet with added columns and updated ranges.

Raphael Gottardo, Greory Imholte

makePeptideSet, create_db, create_db

## This example curated from the vignette -- please see vignette("pepStat")
## for more information
if (require("pepDat")) {

  ## Get example GPR files + associated mapping file
  dirToParse <- system.file("extdata/gpr_samples", package = "pepDat")
  mapFile <- system.file("extdata/mapping.csv", package = "pepDat")

  ## Make a peptide set
  pSet <- makePeptideSet(files = NULL, path = dirToParse,
                         mapping.file = mapFile, log=TRUE)

  ## Plot array images -- useful for quality control
  plotArrayImage(pSet, array.index = 1)
  plotArrayResiduals(pSet, array.index = 1, smooth = TRUE)

  ## Summarize peptides, using pep_hxb2 as the position database
  data(pep_hxb2)
  psSet <- summarizePeptides(pSet, summary = "mean", position = pep_hxb2)

  ## Normalize the peptide set
  pnSet <- normalizeArray(psSet)

  ## Smooth
  psmSet <- slidingMean(pnSet, width = 9)

  ## Make calls
  calls <- makeCalls(psmSet, freq = TRUE, group = "treatment",
                     cutoff = .1, method = "FDR", verbose = TRUE)

  ## Produce a summary of the results
  summary <- restab(psmSet, calls)

}