normalizeArray: Normalize tiling array data using sequence information
In pepStat: Statistical analysis of peptide microarrays

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

This function is used to normalize the peptide microarray data using sequence information.

1 2	normalizeArray(peptideSet, method = "ZpepQuad", robust = TRUE, centered = TRUE)

`peptideSet`	A `peptideSet`. The expression data for the peptides as well as annotations and ranges.
`method`	A `character`. The normalization method to be used. Can be "Zpep" or "ZpepQuad".
`robust`	A `logical`. If TRUE, reweigthed least-squares estimates are computed.
`centered`	A `logical`. If TRUE, recenter the data.

The available methods are "Zpep" and "ZpepQuad". These methods fit a linear model using either linear or linear and quadratic terms (respectively), regressing intensity on the peptides' five Z-scale scores. A peptide Z-scale score is obtained by summing over the Z-scale values in Sandburg et al (1998) of the amino acids the peptide comprises.

Peptide Z-scale scores may be provided in the featureRange slot of peptideSet. This slot is a GRanges object x, and the function will seek five columns labelled z1 through z5 in values(x). If these are not found, the function attempts to calculate Z-scales from sequence information found in peptide(peptideSet)

If robust = TRUE the linear model is fit with t_4 distributed errors. The method returns the residuals of each peptide intensity in the fitted linear model. If centered = TRUE the fitted intercept term is added back to the residuals of the fit.

A peptideSet object with updated normalized intensity values.

Raphael Gottardo, Gregory Imholte

Sandberg, M., Eriksson, L., Jonsson, J., Sjostrom, M., and Wold, S. (1998). New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids. Journal of Medicinal Chemistry 41, 2481-2491.

summarizePeptides, makeCalls

## 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)

}

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