[BioC] Using Limma for proteomics (2D DIGE) datasets

[Tobias Straub]

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 at bio.kuleuven.be
> web http://bio.kuleuven.be/ento/wenseleers/twenseleers.htm   
> < 
> Brain_kol56_targets2 
> .txt 
> > 
> < 
> brain_56_g1 
> .spot 
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----------------------------------------------------------------------
Tobias Straub   ++4989218075439   Adolf-Butenandt-Institute, München D