Dear members,
I am currently learning how to analyse micro array data and I am having problems designing a matrix for paired anova using limma.
The experiment consists in 4 animals. From each animal, 4 cell cultures where prepared. Each cell culture was treated with 4 conditions.

animalCulture <- c(rep("A1", 4), rep("A2", 4), rep("A3", 4), rep("A4", 4))
treatm <- rep(c("Cond1", "Cond2", "Cond3", "Cond4"), 4)
df <- data.frame(animalCulture = animalCulture, treatm = treatm)

To construct a model matrix, I was thinking blocking by the animalCulture variable

model.ma <- model.matrix(~0+df$animalCulture+df$treatm)

Then variances and contrasts will be defined as follow:

fit <- lmFit(eset, model.ma)
contrastmatrix <- makeContrasts(Cond1-Cond2,
                                Cond1-Cond3,
                                Cond1-Cond4,
                                Cond2-Cond3,
                                Cond2-Cond4,
                                cond3-Cond4,
                                levels=model.ma)
fit2 <- contrasts.fit(fit, contrastmatrix)
ebayes <- eBayes(fit2) 

Is this the correct way to model the experimental design? 

Is this the correct way to extract contrasts between conditions accounting for samples that comefrom the same animal?

The output of the model moatrix excludes the Cond1. What is the statistical meaning of this?

Thanks for the help

You're almost there. If you look at the column names for model.ma, you should get something like:

[1] "df$animalCultureA1" "df$animalCultureA2" "df$animalCultureA3"
[4] "df$animalCultureA4" "df$treatmCond2"     "df$treatmCond3"    
[7] "df$treatmCond4"    

The first four coefficients are blocking factors for each animal culture, and are largely uninteresting. (Specifically, they represent the log-average expression for condition 1 in each animal.) The last three coefficients represent the log-fold change for each corresponding condition against condition 1. Obviously, condition 1 doesn't have its own term, because there's no point computing a log-fold change against itself.

To test for DE between conditions 1 and 2, all you would need to do is to drop the df$treatmCond2 term. This can be done by specifying coef=5 argument in topTable, without requiring makeContrasts. The same applies for the coefficient corresponding to each other condition against condition 1. Of course, if you then want to test, e.g., conditions 2 and 3, you would then need to use makeContrasts and contrasts.fit before running eBayes and topTable:

con <- makeContrasts(df.treatmCond2 - df.treatmCond3, levels=model.ma) # renamed '$' to '.'
fit <- contrasts.fit(fit, contrasts=con)

Note that you'll have to fiddle with your column names to get rid of the $, makeContrasts doesn't like them.

If you want to do an ANOVA-like contrast, the null hypothesis would be that there is no DE between any of the conditions. In that case, you don't need to manually specify all pairwise comparisons between conditions. Instead, you just need to specify comparisons to one "reference" condition. For example, if condition 1 is equal to condition 2, and condition 1 is equal to condition 3, then obviously, conditions 2 and 3 will also be equal. With this in mind, if we set condition 1 as the reference (note that the idea of a reference is just for demonstration purposes, limma doesn't really care), then we can do an ANOVA-like contrast by comparing all other conditions to condition 1. This is equivalent to dropping the last three coefficients, i.e., set coef=5:7 in topTable.

I very much appreciate the help in this thread. However, one question is unanswered: how to make the contrasts?

After defining the design matrix and run lmFit in the expression set, I want to retrieve all contrasts by designing a contrast matrix with contrastmatrix:

The problem is that Cond1 is gone and cannot be used in the function. And if I take Cond1 out, I get a very strange matrix.

My question is: 

how to acount for the contrasts including Cond1 and how to model it so that the contrasts occur within individuals?

Thanks

Dear Aron,

So if the first four coefficients are uninteresting, are they still necessary?

I am interested to account for the within-individual variability.