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

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:

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))

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(affypdnn)
Loading required package: affy
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

registering new summary method 'pdnn'.
registering new pmcorrect method 'pdnn' and 'pdnnpredict'.
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/affypdnn/generateExprVal.method.pdnn.Rd_%03d_medium.png", width=480, height=480)
> ### Name: generateExprVal.method.pdnn
> ### Title: Compute PM correction and summary expression value
> ### Aliases: generateExprVal.method.pdnn pmcorrect.pdnn
> ###   pmcorrect.pdnnpredict
> ### Keywords: manip
> 
> ### ** Examples
> 
> data(hgu95av2.pdnn.params)
> library(affydata)
     Package    LibPath                            Item      
[1,] "affydata" "/home/ddbj/local/lib64/R/library" "Dilution"
     Title                        
[1,] "AffyBatch instance Dilution"
> data(Dilution)
> 
> ## only one CEL to go faster
> abatch <- Dilution[, 1]
> 
> ## get the chip specific parameters
> params <- find.params.pdnn(abatch, hgu95av2.pdnn.params)
initializing data structure...
done.
dealing with CEL 1 :
  step 1...done.
  step 2...done.
There were 50 or more warnings (use warnings() to see the first 50)
> 
> ## 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))
100 ids to be processed
|                    |
|####################|
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>