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

In affypdnn: Probe Dependent Nearest Neighbours (PDNN) for the affy package

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.

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.

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.

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], "\n(raw intensities)"))
  matplotProbesPDNN(probes.pdnn, main=paste(ppset.name[i], "\n(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))

affypdnn documentation built on Oct. 31, 2019, 7:34 a.m.