Hi Tom
Yes, you can use limma by hand. You should build the data matrix by hand and
also the design matrix. I already did it and it works fine. The use of Cy2
is highly controversial, some people sais that it add noise but most of the
use the Cy2 information, I recommend to do different approaches and use a
consensus between all of them.
Above you will find some good references. The first one uses limma

Bes regard
Elmer


Kultima K, Scholz B. Alm H. Sköld K. Svensson M. Crossman AR. Bezard E.
Andrén PE. Lönnstedt I. : (2006) Normalization and expression changes in
predefined sets of proteins using 2D gel electrophoresis: A proteomic study
of
L-DOPA induced dyskinesia in an animal model of Parkinson’s disease using
DIGE. BMC Bioinformatics 7:475

*Improving 2D-DIGE protein expression analysis by two-stage linear mixed
models: Assessing experimental effects in a melanoma cell study** *Fernández
Elmer A, Girotti María R., López Juan A, Llera Andrea S., Podhajcer Osvaldo
L, Cantet Rodolfo J. C. and Balzarini Mónica* **Bioinformatics** *Advance
Access published on September 25, 2008; doi:
doi:10.1093/bioinformatics/btn508* [
http://www.ncbi.nlm.nih.gov/pubmed/18818217?ordinalpos=1&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DefaultReportPanel.Pubmed_RVDocSum
]*

On Wed, Oct 21, 2009 at 8:33 AM, Tobias Straub
<tstraub@med.uni-muenchen.de>wrote:

> hi tom,
>
> you can easily call limma without having to construct complex objects such
> as MAList or alike
> have a look at ?lmFit
>
> if you are able to construct a matrix of either cy3/cy5 ratios or simply
> the individual channels you are on the right way. if you set the rownames of
> your matrix to your protein names you will even get back a meaningful output
> from topTable.
>
> another alternative would be to use a wrapper around lmFit such as provided
> in the 'st' package. advantage here is that you can easily switch to other t
> statistics such as studentt, efront, sam etc.
>
> why you think that moderated t is 'better' than student t. any evidences?
>
> best
> T.
>
>
>
> On Oct 18, 2009, at 2:05 PM, Tom Wenseleers wrote:
>
>  Dear all,
>> I am interested in using Limma/LimmaGUI for the analysis of proteomics (2D
>> DIGE) datasets. I have had a try with LimmaGUI, however, I seem to keep on
>> getting the message "limmaGUI was unable to read in the gene list from the
>> raw (image analysis) files." (although the Cy3 and Cy5 data are read in
>> fine) - any idea what I could be doing wrong? Main thing is I don't know how
>> I should call the gene (well protein) list column... I attach a couple of
>> the .spot files and the targets file for your info. I chose
>> File...New...selected my targets file, then Type of Image processing
>> file...Other Red foreground: Cy5, Red background: Cy5_b (I just put in zeros
>> - hope that is OK?), Green foreground: Cy3, Green background: Cy3_b.
>> I have the latest Windows version of R and all the bioconductor packages
>> installed. (Incidentally, during installation it complained about the sma
>> package not being there - that appears to be no longer supported - but is
>> still used in e.g. Limma, so this I guess needs sorting out - I installed an
>> older archived version).
>>
>> In proteomics we also have the Cy2 channel which is an internal control
>> based on a pooled sample that is identical for all gels - but I think I do
>> not have to use that since I am interested in the Cy3/Cy5 Log ratios and
>> (Cy3/Cy2) / (Cy5/Cy2)=Cy3/Cy5, ie I think the Cy2 channel would only
>> introduce additional noise.
>>
>> Anyway if any of you would have experience in using Limma for the analysis
>> of proteomics datasets, please let me know... Right now in the proteomics
>> community people are mostly using simple t-tests etc, but using moderated t
>> statistics would obviously be much better...
>> Maybe there would be some scope for writing a dedicated Bioconductor
>> package for the analysis of proteomics 2D DIGE data, based on Limma or
>> LimmaGUI, I don't know... I think most of the code would hardly need any
>> changing, only the input of the data would need changing and maybe dealing
>> with missing values could be better too (which is more of an issue in
>> proteomics than in microarrays) (eg using a few preprocessing and analysis
>> options, such as to leave them out, substitute by 0 or impute missing values
>> using k nearest neighbours). I think this could greatly benefit the
>> proteomics community.
>>
>> cheers,
>> Tom Wenseleers
>>
>>
>> Dr. T. Wenseleers
>> Dept. of Biology
>> Zoological Institute
>> K.U.Leuven
>> Naamsestraat 59
>> B-3000 Leuven
>> Belgium
>> tel. +32 (0)16 32 39 64
>> mobile +32 (0)472 40 45 96
>> e-mail tom.wenseleers@bio.kuleuven.be
>> web http://bio.kuleuven.be/ento/wenseleers/twenseleers.htm <Brain_kol56_targets2.txt><brain_56_g1.spot><brain_56_g2.spot>_______________________________________________
>> 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
>>
>
> ----------------------------------------------------------------------
> Tobias Straub   ++4989218075439   Adolf-Butenandt-Institute, München D
>
> _______________________________________________
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>



-- 
Elmer A. Fernández (Bioing. PhD)
Investigador Asistente CONICET - Research Assistant CONICET
Porf. Inteligencia Artificial -UCC - Prof. Artificial Intelligence @ UCC
tel: +54-(0)351-4938000 int 145
Fax: +54-(0)351-4938081
web page : http://www.uccor.edu.ar/modelo.php?param=3.8.5.15
mail address: Camino Alta Gracia Km 7.1/2- Córdoba-5000-Argentina

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