
Dear James,Thanks a lot for your help. I will try it again.Thanks,Himanshu Sharma.

> Date: Tue, 17 Apr 2012 15:15:49 -0400
> From: jmacdon@uw.edu
> To: hsharm03@students.poly.edu
> CC: bioconductor@r-project.org
> Subject: Re: [BioC] Affy data analysis
> 
> Hi Himanshu,
> 
> On 4/17/2012 2:54 PM, hsharm03@students.poly.edu wrote:
> > Dear James ,
> > I was able to get the topTable using the method you told me to follow 
> > from the limma manual. But when I try to annotate the genes using 
> > ENTREZ , it requires id's but the topTable we found does not have the 
> > Id's tab. So what should I be doing?.
> 
> If you are using an ExpressionSet when you run lmFit(), then you should 
> automatically get the ID column in your topTable() output. If not, note 
> that the row.names for your topTable() output correspond to the rows of 
> your data, so you can get the probeset IDs that way.
> 
> Best,
> 
> Jim
> 
> 
> > Thanks,
> > Himanshu Sharma.
> >
> > > Date: Mon, 16 Apr 2012 09:49:10 -0400
> > > From: jmacdon@uw.edu
> > > To: hsharm03@students.poly.edu
> > > CC: bioconductor@r-project.org
> > > Subject: Re: [BioC] Affy data analysis
> > >
> > > Hi Himanshu Sharma,
> > >
> > > On 4/14/2012 6:42 PM, hsharm03@students.poly.edu wrote:
> > > > Dear all,I have data from affy HT430mgpm and I need to analyze the 
> > data for differential expression and pathway analysis. I have 3 
> > wildtype controls (Wt neurospheres 2 and 3) for the control analysis. 
> > I have two other tumors (1509 and 1701) for the analysis. From the cel 
> > files, it doesn’t appear that we did replicates for the tumors, just 
> > one each, the rationale at the time being that we had wanted to first 
> > quickly scan the tumors for common signatures. Those genes that are 
> > clearly highly expressed should however represent additional oncogenic 
> > signatures, that may stem from the same or related activating 
> > pathways.For now, my analysis for controls should give me an accurate 
> > expression data for the controls. The tumors will have to be compared 
> > across the samples to look for the low hanging fruits.??I am not sure 
> > how do I go about doing this since I have 3 replicates for the control 
> > but 1 each for different tumors. What should be the strategy that I 
> > should use in order to do my analysis.
> > >
> > > You can just analyze your data as indicated in the limma User's Guide.
> > > Note that although you only have one sample for each of the tumor
> > > samples, since you have three replicates for the control you end up 
> > with
> > > 2 degrees of freedom, so can actually fit a model and compute 
> > contrasts.
> > > Here is an example using some fake data:
> > >
> > > > x <- matrix(rnorm(5e5), ncol = 5)
> > > > design <- model.matrix(~factor(rep(1:3, c(3,1,1))))
> > > > fit <- lmFit(x, design)
> > > > fit2 <- eBayes(fit)
> > > > topTable(fit2, 2)
> > > logFC t P.Value adj.P.Val B
> > > 27913 -5.164721 -4.474076 7.678459e-06 0.6669534 -4.402008
> > > 98975 4.907831 4.251539 2.124031e-05 0.6669534 -4.421736
> > > 90287 4.800002 4.158128 3.209996e-05 0.6669534 -4.429717
> > > 41684 -4.754741 -4.118920 3.808058e-05 0.6669534 -4.433015
> > > 43210 -4.711426 -4.081397 4.478309e-05 0.6669534 -4.436141
> > > 46761 4.705393 4.076171 4.580108e-05 0.6669534 -4.436574
> > > 37345 -4.687702 -4.060846 4.891387e-05 0.6669534 -4.437841
> > > 98788 4.633203 4.013635 5.981260e-05 0.6669534 -4.441714
> > > 46584 4.606493 3.990496 6.595873e-05 0.6669534 -4.443596
> > > 72789 -4.603451 -3.987861 6.669534e-05 0.6669534 -4.443809
> > > > topTable(fit2, 3)
> > > logFC t P.Value adj.P.Val B
> > > 19401 -5.232576 -4.532857 5.822486e-06 0.5822486 -1.796077
> > > 883 4.813581 4.169892 3.048726e-05 0.8544860 -2.252617
> > > 87408 -4.667879 -4.043673 5.263993e-05 0.8544860 -2.402452
> > > 76730 4.641339 4.020682 5.805112e-05 0.8544860 -2.429249
> > > 50261 4.533133 3.926946 8.605996e-05 0.8544860 -2.536920
> > > 63980 4.502927 3.900780 9.591473e-05 0.8544860 -2.566524
> > > 783 -4.498102 -3.896600 9.758446e-05 0.8544860 -2.571235
> > > 59496 -4.441207 -3.847313 1.194575e-04 0.8544860 -2.626398
> > > 92491 4.427735 3.835642 1.252750e-04 0.8544860 -2.639357
> > > 22351 -4.420041 -3.828977 1.287163e-04 0.8544860 -2.646741
> > >
> > > As you can see, limma is happy to run the analysis without any
> > > replication for two of the sample types.
> > >
> > > Best,
> > >
> > > Jim
> > >
> > >
> > > > Thanks,Himanshu Sharma
> > > > [[alternative HTML version deleted]]
> > > >
> > > >
> > > >
> > > > _______________________________________________
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> > > > Search the archives: 
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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
> > >
> > >
> 
> -- 
> James W. MacDonald, M.S.
> Biostatistician
> University of Washington
> Environmental and Occupational Health Sciences
> 4225 Roosevelt Way NE, # 100
> Seattle WA 98105-6099
> 
 		 	   		  
	[[alternative HTML version deleted]]

