Hi Mark,

I think it was a great suggestion and I am providing the result, which I got
after the command, topTable(fit, coef="normvstest", adjust="fdr").
I believe this preliminary results solved the problem of very high fold
value, which I was getting earlier. I will be looking at the entire data and
proved a better view at the earliest. But I have some doubts in this table
itself. Here I am getting negative odds ratio and in some cases negative t
value. What should I do in these scenario?


topTable(fit, coef="normvstest", adjust="fdr")
           logFC   AveExpr         t      P.Value adj.P.Val         B
35726 -2.1103554 11.936825 -9.602581 8.028641e-06 0.3614093 -3.912633
1968  -1.3413791  9.974470 -6.960746 9.138620e-05 0.7305539 -3.971277
5558  -1.6566417 10.885625 -6.506724 1.487751e-04 0.7305539 -3.987395
34497  1.0445013 10.251047  6.185219 2.132063e-04 0.7305539 -4.000460
33195 -1.3603874 13.373106 -6.116817 2.305481e-04 0.7305539 -4.003438
44662  0.9528248 11.180259  6.045345 2.503347e-04 0.7305539 -4.006630
24980 -1.5689151 10.824414 -5.932376 2.855094e-04 0.7305539 -4.011846
30206  2.2991372 13.647875  5.926758 2.873946e-04 0.7305539 -4.012112
26046 -1.1709614  9.505652 -5.746545 3.557246e-04 0.7305539 -4.020911
27210  1.4815342  9.416698  5.656415 3.964086e-04 0.7305539 -4.025537

Thanks in advance

Best
Abhilash


On Tue, Jun 17, 2008 at 4:38 AM, Mark Cowley <m.cowley0@gmail.com> wrote:

> Hi Abhilash,
> Your code looks good, except that usually you will want to normalise log
> transformed data. thus try:
>
>> MA<-normalizeBetweenArrays( log2(Rgene$G), method="quantile")
>>
>
> If your logFC ratios still look very high, then try convincing yourself of
> their accuracy by looking at the raw data (RG$R) for some of the most
> differentially expressed genes, and also plot the expression values for some
> of these DE genes.
>
> good luck,
> Mark
> Peter Wills Bioinformatics Centre
> Garvan Institute of Medical Research
>
>
>
> On 17/06/2008, at 1:17 AM, Abhilash Venu wrote:
>
>  Hi list,
>>
>> I am still wonder about the data, which I analyzed by the limma. I accept
>> that  I am a biology graduate student, and in the learning stage. I am
>> analyzing the single color data, which had been generated by Agilent 4x44k
>> platform. With the help of mailing list and limma users guide, I have done
>> the following analysis. But logFC gives very high values like 320, 1320
>> etc.
>> I don't know how really the fitting is happening. Can I rely on this
>> result.
>> How should I go about it.
>> #Reading the data.
>>
>>  RG<-read.maimages(txt_files, columns = list(G = "gMeanSignal", Gb =
>>>
>>>  "gBGMeanSignal",
>> R="gMedianSignal",Rb="gBGMedian
>>
>>>
>>> Signal"),
>>> annotation= c("Row", "Col",
>>> "ProbeUID","ProbeName", "GeneName",))
>>>
>>
>>
>> Rgene<-backgroundCorrect(RG,method='subtract')
>>
>> #Considering only G as it is single color experiment.
>> MA<-normalizeBetweenArrays(Rgene$G,method="quantile")
>>
>> design <- cbind(norm=1,normvstest=c(1,1,1,1,0,0,0,0))
>> fit <- lmFit(MA, design)
>> fit <- eBayes(fit)
>> topTable(fit, coef="normvstest", adjust="fdr")
>> --
>>
>> Regards,
>> Abhilash
>>
>>        [[alternative HTML version deleted]]
>>
>> _______________________________________________
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>>
>
>
>
> ----------------------------------------------------------------------
> Mark Cowley, BSc (Bioinformatics)(Hons)
>
> Peter Wills Bioinformatics Centre
> Garvan Institute of Medical Research
> 384 Victoria St                                 Tel:  +61 2 9295 8542
> Darlinghurst, NSW 2010          Fax:  +61 2 9295 8538
> Australia                                       email:
> m.cowley@garvan.org.au
> www.garvan.org.au
> ----------------------------------------------------------------------
>
>


-- 

Regards,
Abhilash

	[[alternative HTML version deleted]]

