Dear Sabrina,

I'm very glad that you contacted me about our Bioconductor package 
'plgem'. I will be happy to help you in using it in the best possible 
way. Since your questions are quite general, I'm CCing this answer to 
the BioC mailing list, in case somebody else had the same doubts or 
would like to comment on some the features of this package.

1) In our hands, the minimum number of arrays for fitting the model 
(PLGEM) is 3. But of course everything depends on the data themselves: 
are these technical or biological replicates? In any case, every time 
you would like to try 'plgem' on a new data set, be sure to always set 
the parameter 'fitting.eval' in the 'run.plgem' wrapper function to 
'TRUE' (this is the default setting, actually). This will produce a 
series of diagnostic plots, that you can optionally choose to save to a 
png file, setting the parameter 'plotFile=TRUE'. To judge if the model 
fits well to your data, you should see the following:

- The majority of your data points should indeed be scattered along the 
fitted straight line in the ln(sd) vs. ln(mean) plot.
- The residuals vs. rank plot should produce a sort of regular 'band', 
centered around a y-axis value of zero and with a constant width along 
the x-axis.
- The histogram of the residuals should be approximately normal, which 
you can easily judge by checking if the 'Normal Q-Q plot' produces an 
approximately diagonal line.

Just run the example provided in the vignette to have an idea of a 
typical plot:

library(plgem) # loads the package and all its dependencies
data(LPSeset) # loads the example data set
LPS.DEGlist <- run.plgem(LPSeset) # runs the wrapper using defaults 
settings

You will notice that the above requirements are not perfectly met in 
this particular data set. With more replicates the model usually fits 
better. Anyway, in a situation like the one above, the performance of 
the method is still very good!

2) If your question is "Do you have a way to explicitly CONTROL the 
FDR?", the answer in NO.
Actually there are very few methods that automatically do this. Also 
SAM (Significance Analysis of Microarrays, Tusher VG et al., PNAS 
2001), which is said to do this, actually only provides you an estimate 
of the FDR on your output result. It's up to you and your choice of the 
input 'Delta' parameter to fine-tune the output FDR. The problem there 
is that the relationship between the input 'Delta' and the output 'FDR' 
is not predictable a priori and very much data set-dependent.

What we provide in our method is an input parameter (signLev) that we 
have shown to be a good direct predictor of the number of false 
positives that you expect to observe when actually not a single gene 
should be differentially expressed. So, all you have to do is choose 
the maximum number of false positives you accept to observe in your 
output, e.g. 10, divide that number by the total number of genes in 
your chip, e.g. 10/10000=0.001, use that number directly as the input 
parameter 'signLev=0.001', and finally see how many genes you get with 
those settings, e.g. 500. Then simply divide your chosen number of 
false positives by the number of selected genes, e.g. 10/500=0.02, and 
your actual FDR should be less than or equal to that number.

Of course, also with our method, you will have to do different trials 
if your goal is to get a particular FDR, because the number of selected 
genes cannot be predicted a priori.

Hope that helps!
Norman

Norman Pavelka
Department of Biotechnology and Bioscience
University of Milano-Bicocca
Piazza della Scienza, 2
20126 Milan, Italy

Phone: +39 02 6448 3556
Fax: +39 02 6448 3552

On 20 Jul 2005, at 16:14, Sabrina Carpentier wrote:

> Hi Norman,
>  
> I tried your package plgem and I asked me if it is possible to apply 
> it on my data set: I have only 4 arrays per groups, what do you think?
> Moreover, do you include in your algorithm a multiple test correction? 
> I think that you include FDR , don't you?
>  
> Thanks a lot for your precisions
>  
> Sincerely,
>  
> Sabrina

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