
Hi,
I am new to this list, so apologies if this has been discussed in the past. Unfortunately I could not find an answer to my question in the limma user guide.
 
I have 3 different factors in my gene expression data, 1. parasite 2. tissue and 3. time, with 2, 3 and 4 levels respectively.
I want to get contrasts between these levels within each factor using limma.
 
I have a problem with the following code:
library("limma")design <- model.matrix(~0+factor(eset$parasite) + factor(eset$tissue) + factor(eset$time))This first model produces a design matrix with one column missing for tissue (2 levels only) and for time (3 levels only) but not for parasite (ie., both 2 levels are present). A total of 7 columns in the design matrix.
  
Column names in the design matrix are:
factor(eset$parasite)HC  factor(eset$parasite)TC  factor(eset$tissue)B  factor(eset$tissue)G  factor(eset$time)03  factor(eset$time)07  factor(eset$time)21
 
The second model without '0' produces a column for intercept and one level less for all factors (total of 7 columns in the design matrix). 
 
Column names in the design matrix are:
(Intercept)  factor(eset$parasite)TC  factor(eset$tissue)B  factor(eset$tissue)G  factor(eset$time)03  factor(eset$time)07  factor(eset$time)21
 
Then I fit the linear model to obtain results and later on to get contrast of interest.
fit <- lmFit(eset, design)ebayes <- eBayes(fit)
 
My questions are as follows:
1. In the first model, I get the coefficients for each level of 'parasite' but not for tissue and for time. I like to know why '~0+ factor(....)' model gives mean for all levels in the first factor but not for other factors? What should I do to get the coefficients for all levels for other factors (tissue and time) in model.matrix syntax.
2. In the second model, what is the meaning of an intercept? Is this actually an estimate for the level which has been set to zero (due to linear dependancy). So that the actual estimates I get for each level in parasite, tissue and time are indeed the differences between the levels and the one which is missing (set to zero)? does it only apply to parasite or for all factors?
3. What is a meaning of contrasts built using actual results; for instance B versus G in tissue or time03 versus time 21 under both models?
 
Any help in your interpretation of this results would be great. Actually, I prefer a model where I get results for each level. I think such a model would be better to fit intercations between different factors in this model (which is what I evetually want to do ) and subsequent interpretation of results?
 
I would really appreciate your help.Many thanksHajaHaja Kadarmideen DVM MVSc PhDCSIRO Livestock IndustriesRockhampton, QLD 4701 Australia
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