generateExprVal.method.pdnn {affypdnn}R Documentation

Compute PM correction and summary expression value

Description

Compute 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(geneNames(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 <- geneNames(abatch)[1:100]

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


[Package affypdnn version 1.1-0 Index]