[BioC] limma_analysis result

[Francois Pepin]

Hi Abilash,

you might want to read up a bit on those statistics and how they are 
generated. It definitely helps when it comes to interpreting their 
results properly.

The B statistic is a log odds ratio. It simply means that the actual log 
odds are between 0 and 1: less than 50% probability of differential 
expression according to this test.

The t statistics can be positive or negative. A negative t-statistic 
simply means that the mean of the second group ("test" in this case) is 
higher. A high negative t-statistic would have the same evidence of 
differential expression as a high positive t-statistic.

Francois

Abhilash Venu wrote:
> 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 at 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 at garvan.org.au
>> www.garvan.org.au
>> ----------------------------------------------------------------------
>>
>>
> 
>