#install the most recent release version of Bioconductor
source("http://www.biocondcutor.org/getBioC.R")
getBioC("all","release")


#load the affy package and some example data
library(affy)
library(affydata)


#accessing the Vignettes
openVignette()

#dilution is a sample AffyBatch
data(Dilution)
Dilution

#some subsetting/merging tests

Dilution[1:2]
Newdata <- merge(Dilution[1:2],Dilution[3:4])


#pm() and mm() accessors

pm(Dilution)
mm(Dilution)

# geneNames access the names of the probsets
my.names <- geneNames(Dilution)


#accessing a particular probeset
pm(Dilution,my.names[1000])


#applying some preprocessing methods to an AffyBatch

Dilution.normalized <- normalize(Dilution)

pm(Dilution,my.names[1000])
pm(Dilution.normalized,my.names[1000])

boxplot(Dilution)
boxplot(Dilution.normalized)



#methods of computing RMA
#rma() is faster

system.time(eset1 <- rma(Dilution))

system.time(eset2 <- expresso(Dilution, bgcorrect.method= "rma"  , normalize.method= "quantiles"  , pmcorrect.method="pmonly"  , summary.method= "medianpolish"))

# Load in the AffyExtensions package
# if not loaded in already then affy/affydata will be loaded also
library(AffyExtensions)

#the default model
Pset <- fitPLM(Dilution)
image(Pset,which=2)
boxplot(Pset)

#using Tukey Biweight
Pset2 <- fitPLM(Dilution,psi.type="Tukey",psi.k=4.6851,se.type=3)
par(mfrow=c(1,2))
image(Pset,which=2)
image(Pset2,which=2)

#using a different model
phenoData(Dilution)
pData(Dilution)
Pset3 <- fitPLM(Dilution,model = PM ~ -1 + probes + liver + scanner)

# a quick look at the coefficients
coefs(Pset)[1:5,]
coefs(Pset2)[1:5,]
coefs(Pset3)[1:5,]

# standard errors
se(Pset)[1:5,]
se(Pset2)[1:5,]
se(Pset3)[1:5,]