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Ara-Gene-ST chips with R/Bioconductor/oligo()

We recommend R/Bioconductor - free analysis software - for most users on a budget. We can help you get started with training documents and advice on request (although there is plenty on line already).
This is a brief Bioconductor oligo() run-through for users of Ara-Gene-ST chips.

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source("http://bioconductor.org/biocLite.R")
biocLite("pd.aragene.1.0.st")

then

library("pd.aragene.1.0.st")

   Loading required package: XXX etc...

Using the R menu, make the working directory the one with cels in (all same type).
The command getwd() ..tells you where you are already...

celfiles<-list.celfiles()
NASCRaw<-read.celfiles(files=celfiles)
   Platform design info loaded.
   Reading in.....XXX etc...

NASCset<-rma(NASCRaw,target="probeset")
   Background correcting
   Normalizing
   Calculating Expression

 NOTE: 'probeset' (alternative =  'core') is the level of rma normalisation.

featureData(NASCset)<-getNetAffx(NASCset,"probeset") 

NOT "TRANSCRIPT" - that doesn't appear to return anything from NetAFFX.

   *LONG WAIT*

THEN to find the available headers:

varLabels(featureData(NASCset))
[1] "probesetid"                 "seqname"                    "strand"                     "start"                      "stop"                       "probecount"                
 [7] "transcriptclusterid"        "exonid"                     "psrid"                      "geneassignment"             "mrnaassignment"             "crosshybtype"             
[13] "numberindependentprobes"    "numbercrosshybprobes"       "numbernonoverlappingprobes" "level"                      "bounded"                    "noboundedevidence"        
[19] "hascds"                     "probesettype"                 

   ..and to look at the annotation (e.g mrnas) for a header across a few probesets:

pData(featureData(NASCset))[20000:20010,]$mrnaassignment

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If you wanted, you could  export the normalised data as a large delimited tab file and re-load into a GUI like oneChannelGUI or affylmGUI

Please be aware that these are multi-probe-per-exon chips; and as such are flexible in the way you consider them as a transcript unit (or many) per "gene".
It's all a bit cutting/bleeding edge... and we make no guarantees that this runthrough will work with your version of the packages since Bioconductor is always a 
work-in-progress and is frequently updated.