fitQC: Functions for fitting probe-level models
In xps: Processing and Analysis of Affymetrix Oligonucleotide Arrays including Exon Arrays, Whole Genome Arrays and Plate Arrays

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

This function allows to combine different algorithms to compute background correction, normalization and fit a multichip model for summarization.

fitQC(xps.data,
      filename = character(),
      filedir = getwd(),
      tmpdir = "",
      update = FALSE,
    # background correction
      bgcorrect.method  = "rma",
      bgcorrect.select  = "none",
      bgcorrect.option  = "pmonly:epanechnikov",
      bgcorrect.params  = c(16384),
    # normalization
      normalize.method  = "quantile",
      normalize.select  = "pmonly",
      normalize.option  = "transcript:together:none",
      normalize.logbase = "0",
      normalize.params  = c(0.0),
    # quality control
      qualify.method    = "rlm",
      qualify.select    = "pmonly",
      qualify.qualopt   = "all",
      qualify.option    = "transcript",
      qualify.estimator = "huber",
      qualify.logbase   = "log2",
      qualify.params    = list(10, 0.01, 1.0),
    # reference values
      reference.index   = 0,
      reference.method  = "mean",
      reference.params  = list(0.0),
    # misc.
      exonlevel  = "",
      xps.scheme = NULL,
      add.data   = FALSE,
      bufsize    = 32000,
      verbose    = TRUE)

xpsQualityControl(object, ...)

`xps.data`	object of class `DataTreeSet`.
`filename`	file name of ROOT data file.
`filedir`	system directory where ROOT data file should be stored.
`tmpdir`	optional temporary directory where temporary ROOT files should be stored.
`update`	logical. If `TRUE` the existing ROOT data file `filename` will be updated.
`bgcorrect.method`	background method to use.
`bgcorrect.select`	type of probes to select for background correction.
`bgcorrect.option`	type of background correction to use.
`bgcorrect.params`	vector of parameters for background method.
`normalize.method`	normalization method to use.
`normalize.select`	type of probes to select for normalization.
`normalize.option`	normalization option.
`normalize.logbase`	logarithm base as character, one of ‘0’, ‘log’, ‘log2’, ‘log10’.
`normalize.params`	vector of parameters for normalization method.
`qualify.method`	qualification method to use, currently `rlm`.
`qualify.select`	type of probes to select for qualification.
`qualify.qualopt`	option determining the data to which to apply qualification, one of ‘raw’, ‘adjusted’, ‘normalized’, ‘all’.
`qualify.option`	option determining the grouping of probes for qualification, one of ‘transcript’, ‘exon’, ‘probeset’; exon arrays only.
`qualify.estimator`	option determining the M-estimator to use, one of ‘huber’, ‘fair’, ‘cauchy’, ‘gemanmcclure’, ‘welsch’, ‘tukey’, ‘andrew’.
`qualify.logbase`	logarithm base as character, one of ‘0’, ‘log’, ‘log2’, ‘log10’.
`qualify.params`	vector of parameters for qualification method.
`reference.index`	index of reference tree to use, or 0.
`reference.method`	for `refindex=0`, either trimmed mean or median of trees.
`reference.params`	vector of parameters for reference method.
`exonlevel`	exon annotation level determining which probes should be used for summarization; exon/genome arrays only.
`xps.scheme`	optional alternative `SchemeSet`.
`add.data`	logical. If `TRUE` expression data will be included as slot `data`.
`bufsize`	integer which sets the buffer size of the tree branch baskets (default is 32000).
`verbose`	logical, if `TRUE` print status information.
`object`	object of class `DataTreeSet`.
`...`	the arguments described above.

This function allows to combine different algorithms to compute background correction, normalization and fit a multichip model for summarization.

xpsQualityControl is the DataTreeSet method called by function fitQC, containing the same parameters.

An object of type QualTreeSet.

Christian Stratowa

fitRLM, qualify, express

## Not run: 
## load existing ROOT scheme file and ROOT data file
scheme.test3 <- root.scheme(paste(path.package("xps"),"schemes/SchemeTest3.root",sep="/"))
data.test3 <- root.data(scheme.test3, paste(path.package("xps"),"rootdata/DataTest3_cel.root",sep="/"))

## qualification - rlm
rlm.all <- fitQC(data.test3, "tmp_Test3RLMall", filedir=getwd(), tmpdir="",
                 qualify.method="rlm", qualify.qualopt="all", qualify.option="transcript", add.data=FALSE)

## get expression data.frame
expr.rlm.all <- validData(rlm.all)

## get borders
brd.rlm.all <- borders(rlm.all)

## get residuals
res.rlm.all <- residuals(rlm.all)

## get weights
w.rlm.all <- weights(rlm.all)

## plot expression levels
if (interactive()) {
coiplot(rlm.all)
borderplot(rlm.all)
nuseplot(rlm.all)
rleplot(rlm.all)
image(rlm.all, type="resids")
}

## End(Not run)