Dear Chetan,

As already suggested in a previous mail, I kindly invite you once again 
to subscribe to the Bioconductor mailing list and to continue our 
discussion in that place.

If your analysis goal is to compare the 15 flare samples to the 15 
quiescence samples, then PLGEM is applicable. We have shown that PLGEM 
fits well not only on technical replicates, but also on biological 
replicates and even from samples obtained from distinct individuals 
(with a common phenotype). See for example Figure 2 (second row) of our 
BMC Bioinformatics paper, to see how PLGEM fits on the Leigh syndrome 
data set, which contained eight HG-U133A samples each from a distinct 
individual affected by this disease.

You may send me the diagnostic plots outputted by the following 
instruction (in the form of PNG files on your working directory), if 
you want me to help you deciding if the model fits well also on your 
data:

library(plgem)
plgem.fit(data = yourExprSet, fit.condition = yourCondition, 
fittingEval = TRUE, plot.file = TRUE)

where 'yourExprSet' will be your 'exprSet' object with the special 
'phenoData' slot (type ?plgem.fit for details), while 'yourCondition' 
will be a string that encodes one of your two sets of samples (e.g. one 
of "flare" or "quiescence"), depending on how you described your 
experiment in the 'phenoData'. Remember that yourExprSet must contain 
absolute (and linear) expression values, not log ratios.

If the plots look OK, you can use the 'run.plgem' wrapper function in 
the standard way. It will fit the model on both sets of 15 samples and 
decide on which one PLGEM fits better. Then it will automatically 
proceed until it outputs your list of significant DEG.

Hope that helps! Let me know how it works!

Best,
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 2 Sep 2005, at 00:17, chetan wrote:

> Thanks Norman. As I said earlier, we do not have replicates in this 
> dataset. As you suggested, I may find some dataset in public 
> repositories that are obtained under similar condition but I am not so 
> sure that it would solve our purpose.
>  
> There is one possibility which I am not sure if it will work or not. 
> We have total 32 samples from 15 patients in our dataset. Each patient 
> contributed 2 samples (Flare and Quiescence) except one patient where 
> he contributed 4 samples (2 flare and 2 quiescence). Can we use these 
> 2 flare samples from the same patient as replicates even though the 
> samples are collected at two different time points?
>  
> Can we use all the 15 flare or remission samples to fit the model and 
> then run the analysis to find DEG under flare condition? (This 
> probably is not a good idea but again just a thought!!)
>  
> Let me know.
>  
> Chetan
>  
> -----Original Message-----
> From: Norman Pavelka [mailto:norman.pavelka@unimib.it]
> Sent: Thursday, September 01, 2005 6:42 AM
> To: chetan
> Subject: Re: Microarray data analysis
>  
>
> Dear Chetan,
>
>  You will need a set of replicated samples in your data set, on which 
> you can fit the PLGEM model. You don't have to have replicates for all 
> your samples, but you need replicates for at least one of your 
> experimental conditions. If you have many different experimental 
> conditions you want to compare to a common baseline condition, you 
> could invest in obtaining a large number of replicates of your 
> baseline condition (at least 4). Then apply PLGEM in the standard 
> way...
>
>  If this is not available in your data set, the only thing I can think 
> of, is looking around in public repositories (GEO or ArrayExpress) for 
> a microarray data set obtained from a "similar" source (possibly the 
> same cell type under "similar" conditions). Then use that data set to 
> train the model, and use the parameters from the model on your data. I 
> already used this procedure in another situation and it seems to work 
> (unpublished data). But this would be a very "non-standard" protocol 
> that would need validation first. In this case you cannot use the 
> wrapper function inside the package, but you will have to use the 
> single functions to use them in a non-standard way. This will be more 
> tricky...
>
>  Let me know!
>  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 31 Aug 2005, at 01:42, chetan wrote:
> Thanks Norman. I have downloaded the bioconductor and PLGEM as you 
> described here. Please let me know what would be the next step.
>>
>>  We do not have replicate samples in our dataset. Is that something a 
>> mandatory requirement for this type analysis?
>>
>>   
>>
>>  Chetan
>>
>>   
>>
>>  -----Original Message-----
>>
>> From:Norman Pavelka [mailto:norman.pavelka@unimib.it]
>>
>> Sent:Saturday, August 27, 2005 11:30 AM
>>
>> To:chetan
>>
>> Subject:Re: Microarray data analysis
>>
>>   
>>
>>  Dear Chetan,
>>
>>  Thank you for contacting me regarding PLGEM. I just got back from my 
>> US trip, but I won't start working in my lab here in Italy until 
>> September 1.
>>
>>  The reference for the algorithm is the following: Pavelka N, 
>> Pelizzola M, Vizzardelli C, Capozzoli M, Splendiani A, Granucci F, 
>> Ricciardi-Castagnoli P. A power law global error model for the 
>> identification of differentially expressed genes in microarray data. 
>> BMC Bioinformatics. 2004 Dec 17;5(1):203. I advice you reading it as 
>> the first thing, in order to understand if it would be suited to your 
>> analysis.
>>
>>  Until now we only tried PLGEM on Affy chip data, but I would love to 
>> help you trying to apply it also on two-color arrays. I would be very 
>> happy to now that it works similarly :-)
>>
>>  There is an implementation of the algorithm available as an R 
>> package from the Bioconductor project. Are you familiar with 
>> R/Bioconductor? Then please skip the following lines and simply get 
>> the 'plgem' package using the 'install.packages2' function from the 
>> 'reposTools' library.
>>
>>  1) If don't have an R installation at all, download the latest from 
>> CRAN website (http://www.r-project.org/) and follow instructions on 
>> how to install it.
>>
>>  2) If you do have R, but still don't have the Bioconductor packages, 
>> start R and type the following lines in your R session. It will take 
>> a while, but you will get the most important packages in one step:
>>  source("http://www.bioconductor.org/getBioC.R")
>>  getBioC()
>>
>>  3) Once you have (or if you already had) a basic installation of 
>> Bioconductor packages, write the following in your R environment:
>>  library(reposTools)
>>  install.packages2("plgem")
>>
>>  4) To load the library and read the user's manual (also called 
>> "Vignette"), type:
>>  library(plgem)
>>  openVignette("plgem") # then answer "1" to the question.
>>
>>  You are welcome to get back to me again, once you have installed the 
>> package or if you encounter any problems during installation. If you 
>> intend using R/Bioconductor for the first time, I would highly 
>> recommend to bookmark the Bioconductor website 
>> (http://www.bioconductor.org/) and subscribe to the corresponding 
>> mailing list (https://stat.ethz.ch/mailman/listinfo/bioconductor). 
>> You will obtain a lot of information from the FAQ in the website. For 
>> general questions about R visit the CRAN website and/or subscribe to 
>> their mailing list. Post general questions about Bioconductor (not 
>> answered in the FAQ) to the mailing list.
>>
>>  Hope that helps! Look forward to read from you soon, though I most 
>> probably won't be able to reply until the end of the next week.
>>
>>  Best wishes,
>>  Norman

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