Hello All,

I am really having a lot of problems analysis hugene 2.0 st microarrays. 

My experimental setup is a control with 8 biological replicates then 3 timepoints with 9 replicates. The time points are months apart so it isn't really considered time series. 

R just keeps crashing when I try do anything above genelevel analysis. Prob level crashed. I am pretty lost. I want to get DE genes using all time points. I never worked with this type of data before. Can anyone help, give me a work flow. Teach me how to keep R from crashing.

Thank you

okay so this is the first thing you asked for:

function (package = NULL) 
{
    z <- list()
    z$R.version <- R.Version()
    z$platform <- z$R.version$platform
    if (nzchar(.Platform$r_arch)) 
        z$platform <- paste(z$platform, .Platform$r_arch, sep = "/")
    z$platform <- paste0(z$platform, " (", 8 * .Machine$sizeof.pointer, 
        "-bit)")
    z$locale <- Sys.getlocale()
    if (is.null(package)) {
        package <- grep("^package:", search(), value = TRUE)
        keep <- sapply(package, function(x) x == "package:base" || 
            !is.null(attr(as.environment(x), "path")))
        package <- sub("^package:", "", package[keep])
    }
    pkgDesc <- lapply(package, packageDescription, encoding = NA)
    if (length(package) == 0) 
        stop("no valid packages were specified")
    basePkgs <- sapply(pkgDesc, function(x) !is.null(x$Priority) && 
        x$Priority == "base")
    z$basePkgs <- package[basePkgs]
    if (any(!basePkgs)) {
        z$otherPkgs <- pkgDesc[!basePkgs]
        names(z$otherPkgs) <- package[!basePkgs]
    }
    loadedOnly <- loadedNamespaces()
    loadedOnly <- loadedOnly[!(loadedOnly %in% package)]
    if (length(loadedOnly)) {
        names(loadedOnly) <- loadedOnly
        pkgDesc <- c(pkgDesc, lapply(loadedOnly, packageDescription))
        z$loadedOnly <- pkgDesc[loadedOnly]
    }
    class(z) <- "sessionInfo"
    z
}
<bytecode: 0xb60e1f8>
<environment: namespace:utils>

Second my code (I don't even know if this is the right directions for DE) :

source("http://bioconductor.org/biocLite.R")
biocLite("oligo")
biocLite("limma")
biocLite("pd.hugene.2.1.st")
library(genefilter)
library(limma)
library(oligo)
library(pd.hugene.2.0.st)

#directory for the data
mydir <- "/data/leuven/314/vsc31409/Bioinformatics_Thesis_Concordia_Microarry_Data/Control"

#setting seed for reproducibility
set.seed(1)
#listing the files from directory using special CEL file read function
celList <- list.celfiles(mydir, full.names=TRUE)
#reading data from cellist and setting annotation package to approiate one for this microarray
rawData <- read.celfiles(celList, pkgname='pd.hugene.2.0.st')
#normalizing the data using RMA algorithm
normData <- rma(rawData)
#checking boxplot of raw data
par(mar=c(10,4.5,2,1))
boxplot(rawData,las=3)
#checking boxplot of normalized data
boxplot(normData,las=3)

#the annotation package
biocLite("hugene20sttranscriptcluster.db")
library(hugene20sttranscriptcluster.db)

# Strategy is to create data frame objects and merge them together - put expression info into a data frame
my_frame <- data.frame(exprs(normData))

# Put annotation information in a data frame.  To get specific fields, use packageNameSYMBOL, where the caps part names the type of data you're after
hugene20sttranscriptcluster()
Annot <- data.frame(ACCNUM=sapply(contents(hugene20sttranscriptclusterACCNUM), paste, collapse=", "),
                    SYMBOL=sapply(contents(hugene20sttranscriptclusterSYMBOL), paste, collapse=", "), 
                    DESC=sapply(contents(hugene20sttranscriptclusterGENENAME), paste, collapse=", "))
#retreaving feature data

featureData(normData) <- getNetAffx(normData, "transcript")

#addomg phenotypic data

phn = normData@phenoData

phn@data[1:14,2]= "treated" 

phn@data[15:27,2]="control"

colnames(phn@data)[2]="source"

phn@data

# Merge data frames together (like a database table join) also removing NAs
all <- merge(Annot, my_frame, by.x=0, by.y=0, all=T)

merge <- subset(all, SYMBOL!="NA")

# Write out to a file:
write.table(merge,file="thesis.ann.txt",sep="\t")

#plm

Pset = fitProbeLevelModel(rawData) <-----This is where it crashes.

I will try running in regular R. Thank so much for the help!