[BioC] edgeR query

James W. MacDonald jmacdon at uw.edu
Wed Sep 5 20:04:44 CEST 2012

Hi Shravanthi,

 > library(GEOquery)
 > dat <- getGEO("GSE9936")[[1]]
 > dat
ExpressionSet (storageMode: lockedEnvironment)
assayData: 22283 features, 105 samples
   element names: exprs
protocolData: none
   sampleNames: GSM251354 GSM251355 ... GSM251458 (105 total)
   varLabels: title geo_accession ... data_row_count (37 total)
   varMetadata: labelDescription
   featureNames: 1007_s_at 1053_at ... AFFX-TrpnX-M_at (22283 total)
   fvarLabels: ID GB_ACC ... Gene Ontology Molecular Function (16 total)
   fvarMetadata: Column Description labelDescription
experimentData: use 'experimentData(object)'
Annotation: GPL96

 > table(pData(phenoData(dat))[,11])

                    treated with 300nM genistein
treated with 300nM genistein and 3uM ICI182,780
                      treated with 300nM S-Equol
  treated with 300nM S-Equol and 3uM ICI 182,780
               treated with 6nM 17beta-estradiol
                      treated with 6nM genistein
                    treated with vehicle control

Note that the limma package is quite happy to use an ExpressionSet as 
input, and the 11th column of the phenoData slot contains the treatment. 
That should get you well on your way. You should at the very least read 
the majority of the limma User's Guide.



On 9/5/2012 8:16 AM, Shravanthi P wrote:
> Thanks a lot for correcting my error. I have downloaded the Limma
> package(it seems to be majorly for spotted microarray as opposed to
> oligonucleotides microarry). I am having trouble even reading my data
> into R. I have the tab delimited format file for a series (eg GSE9936)
> .How do I read and  initialize it to an object in R? And how to I
> define groups within this file for a comparison.Limma user manual
> directed me to use the affy package for normalizing data.Can I not use
> the already available normalized data ?
> I have a time constraint and was hoping to avoid this step.
> My aim is to find the up regulated and downregulated genes in breast
> cancer cells when treated with Genistein on a time progression basis
> (with a constant concentration)
> On 9/5/12, Steve Lianoglou<mailinglist.honeypot at gmail.com>  wrote:
>> Hi Shravanthi,
>> On Tue, Sep 4, 2012 at 6:33 AM, Shravanthi P<shravanthipsg at gmail.com>
>> wrote:
>>> I am new to using R and my project is on differential gene expression .I
>>> was told to use Edger but I am having trouble reading a tab delimited
>>> format data for affy oligonucleotide microarray data .
>>> How can I read the data into R ? and how can I identify the upregulated
>>> and
>>> downregulated genes  ?
>> The bad news is that you've got a long way to go before you can get
>> from where you are to where you want to be. The good news is that
>> there are a lot of resources available for your self study that can
>> help you get to where you want to be.
>> It would be in your best interest to learn more about R first (start
>> with an intro to R) before you try to do anything more serious.
>> After you do that, and you then read the (very helpful) edgeR user
>> manual (as Wenhuo already suggested) -- you'll probably come to the
>> realization that edgeR is the wrong tool for this job. You have
>> microarray data and edgeR is used for sequencing data.
>> In this case, you'll find that the limma package is probably what
>> you're after. Fortunately for you, there is a very thorough limma user
>> guide that you can read through that gives you some idea of how to
>> process different types of microarray data. The goods at the link
>> below (and the associated packages) are worth some study as well:
>> http://bioconductor.org/help/workflows/oligo-arrays/
>> HTH,
>> -steve
>> --
>> Steve Lianoglou
>> Graduate Student: Computational Systems Biology
>>   | Memorial Sloan-Kettering Cancer Center
>>   | Weill Medical College of Cornell University
>> Contact Info: http://cbio.mskcc.org/~lianos/contact
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James W. MacDonald, M.S.
University of Washington
Environmental and Occupational Health Sciences
4225 Roosevelt Way NE, # 100
Seattle WA 98105-6099

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