
Dear James,
I completely understand the importance of me learning on my own. But, the thing here is this data is not my own experiment. I have been given these 4 files as a test for a job interview. I do not have any other detail regarding it. I just have these 4 cel files. I know the first 3 are wild types or control and the other one is a tumor. I have to find the differential expression of tumor as compared to control. I have been reading up on the case studies as well as limma user manual. 
I have understood that my case is the two groups :Affymetrix case as according to section 8.5 of limma user manual. But, as per there manual , I am trying to create my design matrix and still I am not able to get it. I tried doing the contrast matrix also but I am still not getting it. 
I thought a lot about it and now I used the following command :

design <- model.matrix(~factor(rep(1:2, c(3,1))))
fit <- lmFit(eset, design)
fit2 <- eBayes(fit)
topTable(fit2, 1)
                   ID    logFC  AveExpr        t      P.Value    adj.P.Val
973   1416642_PM_a_at 13.64906 13.60996 208.6925 6.608690e-09 1.436053e-06
8941    1424635_PM_at 13.55811 13.52669 206.8301 6.839348e-09 1.436053e-06
22282 1437976_PM_x_at 13.41281 13.41262 205.0409 7.070582e-09 1.436053e-06
955   1416624_PM_a_at 13.61291 13.61261 204.7329 7.111384e-09 1.436053e-06
32409 1448109_PM_a_at 13.35854 13.39007 203.3663 7.296015e-09 1.436053e-06
548   1416217_PM_a_at 13.31925 13.33522 202.1300 7.468282e-09 1.436053e-06
21301 1436995_PM_a_at 13.33443 13.35885 202.0929 7.473534e-09 1.436053e-06
33736 1449436_PM_s_at 13.36569 13.33920 201.5598 7.549461e-09 1.436053e-06
1748  1417417_PM_a_at 13.36793 13.26406 201.5213 7.554988e-09 1.436053e-06
8069  1423763_PM_x_at 13.20019 13.21718 200.1702 7.752016e-09 1.436053e-06
             B
973   9.389064
8941  9.383131
22282 9.377292
955   9.376273
32409 9.371700
548   9.367491
21301 9.367364
33736 9.365526
1748  9.365393
8069  9.360674
> topTable(fit2, 2)
                   ID     logFC  AveExpr         t      P.Value adj.P.Val
44020 1459725_PM_s_at  9.002733 5.546952  66.37302 5.294023e-07 0.0064537
21237   1436931_PM_at -7.605935 9.492585 -53.42400 1.213995e-06 0.0064537
5418    1421112_PM_at -6.853626 7.778874 -51.89407 1.356622e-06 0.0064537
39116   1454821_PM_at -6.814400 9.007994 -48.96033 1.694613e-06 0.0064537
8985    1424679_PM_at  6.579054 5.260500  48.16929 1.803474e-06 0.0064537
3964    1419633_PM_at  6.547493 4.696412  48.04883 1.820817e-06 0.0064537
8706  1424400_PM_a_at -8.609042 9.895516 -47.03876 1.974842e-06 0.0064537
7235  1422929_PM_s_at  6.579742 6.075454  46.85107 2.005251e-06 0.0064537
34850   1450555_PM_at  7.541370 4.892047  46.41624 2.077999e-06 0.0064537
1790    1417459_PM_at  6.722704 5.163969  45.95859 2.158198e-06 0.0064537
             B
44020 5.930831
21237 5.650912
5418  5.606440
39116 5.511995
8985  5.484246
3964  5.479928
8706  5.442737
7235  5.435626
34850 5.418903
1790  5.400918

Is this approach correct?. I know I am asking a bit too much but this is the first time I am trying it and so want to be sure about it. Also , if possible can you tell me the difference in both the topTables. Which of the two tables are tbe difference between Tumor and control. What I understand is that co-ef = 2 is the one of my interest because it gives the differential expression in tumor. I am really sorry for so many questions and I hope this will clear up the situation a little bit.
Thanks,
Himanshu Sharma.
> Date: Wed, 18 Apr 2012 14:50:15 -0400
> From: jmacdon@uw.edu
> To: hsharm03@students.poly.edu
> CC: bioconductor@r-project.org
> Subject: Re: [BioC] Affy PhenoData
> 
> Hi Himanshu,
> 
> On 4/18/2012 12:27 PM, hsharm03@students.poly.edu wrote:
> > Dear all,
> > I have 4 samples of HT 430mgpm array plate. Three of them are replicates of wild types and 1 is a tumor condition with no replicates. But I do not have any other information regarding how was the experiment conducted and I am not able to figure out how to create a phenodata of the same. When I create the expression set of this data using rma function of affy library I can see the names of the samples as they were and sample numbers namely 1,2,3,4. What I understand is it takes alll the four samples as different and when I do the differential expression analysis using limma , I try to create the model.matrix using the following command :
> >
> > design<- model.matrix(~sample, pData(eset))
> >
> > But what I understand is that the sample that are present in the data it is taking 1 condition each of 4 samples. Am I understanding it correctly?.
> 
> Yes, you are understanding it correctly. But this leads me to a separate 
> point, below.
> 
> > If so what should I be doing to get differential expression of genes in tumor as compare to the 3 wild type replicates that I have .
> 
> The simple answer is that you should use the correct input to 
> model.matrix(), designed for your experiment. I realize that is a vague 
> and wholly unsatisfying answer, but we have arrived at the point for you 
> that occurs for all long term R users, when they either decide to figure 
> stuff out themselves or they become disillusioned and give up.
> 
> It would be simple for me to tell you exactly what you need to do for 
> this step in your analysis. And then answer the next question, and the 
> one after that. But that helps no one.
> 
> If you are really going to analyze your own data (not recommended IMO, 
> but that's the beauty of Open Source software - we get both the rope and 
> the tree, and are free to hang ourselves) you will have to learn how to 
> figure out both what you should be doing, and how to do it.
> 
> So the best advice I can offer is to recommend that you find a local 
> statistician to help with your analysis. Barring that, you should 
> closely read the 'limma User's guide', probably the 'Introduction to R', 
> certainly look at ?formula, ?model.matrix, and ?factor. The Bioconductor 
> Case Studies contain many useful examples. There are also any number of 
> presentations given over the years that you could find via the BioC 
> website, or with good google skills.
> 
> Best,
> 
> Jim
> 
> 
> >
> > I am very new to this field and so I am not sure how to proceed
> > Any help will be much appreciated.
> > Thanks ,
> > Himanshu Sharma.
> >   		 	   		
> > 	[[alternative HTML version deleted]]
> >
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> 
> -- 
> James W. MacDonald, M.S.
> Biostatistician
> University of Washington
> Environmental and Occupational Health Sciences
> 4225 Roosevelt Way NE, # 100
> Seattle WA 98105-6099
> 
 		 	   		  
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