affy (version 1.50.0)

generateExprVal.method.pdnn: Compute PM correction and summary expression value

Description

Computes PM correction and summary expression value with PDNN method.

Usage

pmcorrect.pdnn(object, params, gene=NULL, gene.i=NULL,
               params.chiptype=NULL, outlierlim=3, callingFromExpresso=FALSE)
pmcorrect.pdnnpredict(object, params, gene=NULL, gene.i=NULL,
               params.chiptype=NULL, outlierlim=3, callingFromExpresso=FALSE)
generateExprVal.method.pdnn(probes, params)

Arguments

object

object of ProbeSet.

probes

matrix of PM-corrected signals (should be coming out of pmcorrect.pdnn).

params

experiments specific parameters.

gene

gene (probe set) ID (from wich the gene.i would be derived).

gene.i

gene index (see details).

params.chiptype

chip-specific parameters.

outlierlim

threshold for tagging a probe as an outlier.

callingFromExpresso

is the function called through expresso. DO NOT play with that.

Value

• pmcorrect.pdnn and pmcorrect.pdnnpredict return a matrix (one row per probe, one column per chip) with attributes attached. generateExprVal returns a list:
• exprs
  expression values
• se.exprs
  se expr. val.

Details

Only one of gene, gene.i should be specified. For most the users, this is gene. pmcorrect.pdnn and pmcorrect.pdnnpredict return what is called GSB and GSB + NSB + B in the paper by Zhang Li and collaborators.

See Also

pdnn.params.chiptype

Examples

data(hgu95av2.pdnn.params)
library(affydata)
data(Dilution)

## only one CEL to go faster
abatch <- Dilution[, 1]

## get the chip specific parameters
params <- find.params.pdnn(abatch, hgu95av2.pdnn.params)

## The thrill part: do we get like in the Figure 1-a of the reference ?
par(mfrow=c(2,2))
##ppset.name <- sample(featureNames(abatch), 2)
ppset.name <- c("41206_r_at", "31620_at")
ppset <- probeset(abatch, ppset.name)
for (i in 1:2) {
  ##ppset[[i]] <- transform(ppset[[i]], fun=log) # take the log as they do
  probes.pdnn <- pmcorrect.pdnnpredict(ppset[[i]], params,
                                       params.chiptype=hgu95av2.pdnn.params)
  ##probes.pdnn <- log(probes.pdnn)
  plot(ppset[[i]], main=paste(ppset.name[i], "(raw intensities)"))
  matplotProbesPDNN(probes.pdnn, main=paste(ppset.name[i], "(predicted intensities)"))
}

## pick the 50 first probeset IDs
## (to go faster)
ids <- featureNames(abatch)[1:100]

## compute the expression set (object of class 'ExpressionSet')
eset <- computeExprSet(abatch, pmcorrect.method="pdnn",
                       summary.method="pdnn", ids=ids,
                       summary.param = list(params, params.chiptype=hgu95av2.pdnn.params))