library(rmodel)

print(package.version("rmodel"))
print(package.version("mouse4302rmodelcdf"))


#read in the CEL data
mydata<-ReadAffy(cdfname="mouse4302rmodelcdf")


#############################################################################################
#rmodel now provides mas5 background correction and constant median normalization in addition to building the rmodel batch object
myrmodel<-rmodel(mydata,
sample.a=c(1,2,3),
sample.b=c(4,5,6),
label.a="gv",
label.b="m2",
mas5calls=3,
masChips=c(1,2,3,4,5,6),
nullmodel="within.grp",
mode="sample")

myrmodel<-buildOdds(myrmodel,fd.rate=0.1,useRdiff=TRUE,min.set=3,n=30000)

myrmodel<-scoreSegs(myrmodel)

genomeTracks(myrmodel)

save.image(file=paste(myrmodel@title,".myrmodel.Rdata",sep=""))

#############################################################################################
#rmodel now provides mas5 background correction and constant median normalization in addition to building the rmodel batch object
myrmodel<-rmodel(mydata,
sample.a=c(1,2,3),
sample.b=c(7,8,9),
label.a="gv",
label.b="m2pa",
mas5calls=3,
masChips=c(1,2,3,7,8,9),
nullmodel="within.grp",
mode="sample")

myrmodel<-buildOdds(myrmodel,fd.rate=0.1,useRdiff=TRUE,min.set=3,n=30000)

myrmodel<-scoreSegs(myrmodel)

genomeTracks(myrmodel)

save.image(file=paste(myrmodel@title,".myrmodel.Rdata",sep=""))

#############################################################################################
#rmodel now provides mas5 background correction and constant median normalization in addition to building the rmodel batch object
myrmodel<-rmodel(mydata,
sample.a=c(7,8,9),
sample.b=c(10,11,12),
label.a="m2pa",
label.b="dko",
mas5calls=3,
masChips=c(7,8,9,10,11,12),
nullmodel="within.grp",
mode="sample")

myrmodel<-buildOdds(myrmodel,fd.rate=0.1,useRdiff=TRUE,min.set=3,n=30000)

myrmodel<-scoreSegs(myrmodel)

genomeTracks(myrmodel)

save.image(file=paste(myrmodel@title,".myrmodel.Rdata",sep=""))