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))