| generateExprVal.method.pdnn {affypdnn} | R Documentation |
Compute PM correction and summary expression value with PDNN method
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)
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. |
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.
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. |
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))