I have checked limmaGUI and I'm not sure why it is not reading your data files correctly when you use the Other format option. I shall look into that later. However, I suggest, as Elmer and Tobias do, that you read the limma Users Guide and use limma at the command line. I have read your data files that you attached using the code below. You should be able to proceed on from there, though I will leave it to others to advise on the statistical approach, as I'm a programmer rather than a statistician.
>library(limma) >setwd("wherever you stored your data") >targets <- readTargets("Brain_kol56_targets2.txt") >RG <- read.maimages(columns=list(R="Cy3",Rb="Cy3_b",G="Cy5",Gb="Cy5_b"),ext="spot")
and looking at your RGlist I get:
>RG An object of class "RGList" $R brain_56_g1 brain_56_g2 [1,] NA NA [2,] NA NA [3,] NA NA [4,] NA NA [5,] NA NA 3379 more rows ... $Rb brain_56_g1 brain_56_g2 [1,] 0 0 [2,] 0 0 [3,] 0 0 [4,] 0 0 [5,] 0 0 3379 more rows ... $G brain_56_g1 brain_56_g2 [1,] NA NA [2,] NA NA [3,] NA NA [4,] NA NA [5,] NA NA 3379 more rows ... $Gb brain_56_g1 brain_56_g2 [1,] 0 0 [2,] 0 0 [3,] 0 0 [4,] 0 0 [5,] 0 0 3379 more rows ... $targets FileName brain_56_g1 brain_56_g1 brain_56_g2 brain_56_g2 $source  "generic"
The Walter and Eliza Hall Institute of Medical Research
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
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;
Elmer A. Fernández (Bioing. PhD)
Investigador Asistente CONICET - Research Assistant CONICET
Porf. Inteligencia Artificial -UCC - Prof. Artificial Intelligence @
tel: +54-(0)351-4938000 int 145
web page : http://www.uccor.edu.ar/modelo.php?param=22.214.171.124
mail address: Camino Alta Gracia Km 7.1/2- Córdoba-5000-Argentina
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?
Tobias Straub ++4989218075439 Adolf-Butenandt-Institute, Munchen D