Hi, 

First of all, I want to thank everyone for their kind answers. Thanks!
I tried the conventional t-test after non-specific filtering on my data using the command "rowttests" from the "genefilter" package and that gives significant genes (after BH-adjustment) with a smaller fold change close to 1.1, so I guess what I see is a consequence of using limma and a small (5 controls and 3 treatment) number of replicates... I also tried the volcano plot representation and that showed a clear dispersion on the sides of the 'V', as Mark suggested. I was just trying to understand why it is that I'm observing this particular effect on the fc on my data, even though I may end up filtering the list of significant genes to remove those with a very small change... but I was curious to know where and how that was happening. Thanks a lot for your help everyone!


Best regards,

  Eva   


On 14/11/2010, at 23:51, Mark Cowley wrote:

> Hi Eva,
> Volcano plots are very useful for showing the relationship between logFC and pvalue.
> 
> # logFC vs uncorrected p-values
> plot(sign$logFC, -log10(sign$p.value))
> # logFC vs corrected p-values
> plot(sign$logFC, -log10(sign$adj.p.val))
> 
> I suspect you'll see a distinct V shape, with large dispersion on either side of the V
> 
> cheers,
> Mark
> On 15/11/2010, at 4:43 AM, Wolfgang Huber wrote:
> 
>> 
>> Hi Eva
>> 
>> in the ordinary t-test, the t-statistic is proportional to the ratio of fold change 'fc' and estimated within-group standard deviation 's'; and the p-value depends on that through a monotonous transformation, ie. setting a p-value cutoff is equivalent to setting a cutoff on |t|. s_gene is simply, for each gene separately, the empirical standard deviation.
>> 
>> With limma/eBayes, you replace s by a cleverly calibrated compromise s_limma between s_gene and an overall pooled s_tot (across all genes). With a small number of replicates, that compromise is close to s_tot; with many replicates, close to s_gene; and somewhere inbetween in intermediate cases. Now, if (eBayes decides that) you have few replicates, then s_gene ~ s_tot is always the same for all genes, and your p-value cutoff is directly equivalent to a cutoff on fc. This, it appears, is what you see.
>> 
>> Have a look, in your 'eb' object, at the slots 'df.prior' and 'df.residual'. The manual page of eBayes says: "s2.post is the weighted average of s2.prior and sigma^2 with weights proportional to df.prior and df.residual respectively."
>> 
>> 	Best wishes
>> 	Wolfgang
>> 
>> 
>> Il Nov/13/10 10:44 PM, Sean Davis ha scritto:
>>> On Sat, Nov 13, 2010 at 1:56 PM, Eva Benito Garagorri<ebenito@umh.es>wrote:
>>> 
>>>> Dear list,
>>>> 
>>>> I have been working with some microarray data where I make simple
>>>> comparisons between a control and a treatment group. I used vsn to normalize
>>>> and limma to find differentially expressed genes. I used "topTable" with
>>>> number=Inf and p.value=0.05 to get all significant genes for a given
>>>> condition, regardless of their fold change. I seem to be getting significant
>>>> genes with a fold change systematically bigger than 1.3 (linear scale), both
>>>> up and downregulated. I was wondering whether this can happen and why or
>>>> whether I am missing something or making a mistake in the analysis. I guess
>>>> I expected that some genes would be significant even with a very small fold
>>>> change. I tried the same analysis with the ALL dataset and I found that
>>>> genes were significant with a fold change above ~1.1. Below is the code I
>>>> used for the analysis of the ALL dataset, which is essentially the same I
>>>> used for my own analysis. Thanks in advance!
>>>> 
>>>> 
>>> Hi, Eva.  I think what you are saying is that you are seeing genes that
>>> change by 30% and that you are concerned that you should see genes with a
>>> smaller fold change being significant?  The fold change that is significant
>>> will be dependent on a number of factors, but sample size and biologic
>>> factors such as the amount of heterogeneity in the two conditions are strong
>>> contributors.  I wouldn't say that there is anything wrong with your
>>> results, at least without knowing more detail.
>>> 
>>> Sean
>>> 
>>> 
>>> 
>>>> Eva
>>>> 
>>>> 
>>>> library(ALL)
>>>> data(ALL)
>>>> library(limma)
>>>> 
>>>> 
>>>> f = ALL$mol.biol
>>>> 
>>>> mat = model.matrix(~f, ALL)
>>>> lm = lmFit(ALL, mat)
>>>> eb = eBayes(lm)
>>>> 
>>>> sign = topTable(eb, coef=2,number=Inf,p.value=0.05)
>>>> dim(sign)
>>>> 
>>>> sort(2^sign$logFC)
>>>> 
>>>> 
>>>> 
>>>> 
>>>> 
>>>>> sessionInfo()
>>>> R version 2.11.1 (2010-05-31)
>>>> x86_64-apple-darwin9.8.0
>>>> 
>>>> locale:
>>>> [1] es_ES.UTF-8/es_ES.UTF-8/C/C/es_ES.UTF-8/es_ES.UTF-8
>>>> 
>>>> attached base packages:
>>>> [1] stats     graphics  grDevices utils     datasets  methods   base
>>>> 
>>>> other attached packages:
>>>> [1] limma_3.4.4   ALL_1.4.7     Biobase_2.8.0
>>>> 
>>>> loaded via a namespace (and not attached):
>>>> [1] tools_2.11.1
>>>> 
>>>> 
>>>> 
>>>> 
>>>> 
>>>> 
>>>> 
>>>> 
>>>> ----------
>>>> Eva Benito Garagorri
>>>> PhD program in Neurosciences
>>>> Institute for Neurosciences in Alicante
>>>> UMH-CSIC
>>>> San Juan de Alicante
>>>> 03550
>>>> Spain
>>>> ebenito@umh.es
>>>> (34) 965 91 92 33
>>>> 
>>>> 
>>>> 
>>>> 
>>>> 
>>>>       [[alternative HTML version deleted]]
>>>> 
>>>> _______________________________________________
>>>> Bioconductor mailing list
>>>> Bioconductor@stat.math.ethz.ch
>>>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>>>> Search the archives:
>>>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>>>> 
>>> 
>>> 	[[alternative HTML version deleted]]
>>> 
>>> _______________________________________________
>>> Bioconductor mailing list
>>> Bioconductor@stat.math.ethz.ch
>>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>>> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
>> 
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor@stat.math.ethz.ch
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
> 

----------
Eva Benito Garagorri
PhD program in Neurosciences
Institute for Neurosciences in Alicante
UMH-CSIC
San Juan de Alicante
03550
Spain
ebenito@umh.es
(34) 965 91 92 33





	[[alternative HTML version deleted]]

