Question: limma design matrix

0

michael watson IAH-C •

**3.4k**wrote:Hi
First off, let me say that i think limma is a quite brilliant package
and I use it a lot. However, one of the biggest obstructions to using
limma for the lay biologist is an inability to understand the design
matrix. Although there is a lot of documentation, showing the design
matrix for a number of example problems, there is no discussion as to
how that design matrix was constructed i.e. the logical thought
processes that went into it.
For the two-sample experiment given in the UserGuide, I understand
that there must be one row per array in my design matrix and the
columns represent the coefficients I want to calculate. I represent
the differences in the factors with 1's and 0's. Great, this is
pretty similar to how I do it for aov().
But then, all of a sudden, for the factorial experiment the design
matrix not only has -1's in there, but also a column for the
interactions. How do I decide which array/factor combination gets a
1, a 0 or a -1?
Let me put this in perspective. I have a 3 factor experiment where
the factors are animal, infected/uninfected and time. All samples
were hybridised against a common reference. For analysis of variance,
all I do is set up a data.frame that looks like this for each gene:
data c b t
1 2.9 1 1 1
2 2.7 1 0 2
3 2.8 1 1 1
4 3.0 1 0 2
5 -3.0 0 1 1
6 -3.5 0 0 2
7 -4.0 0 1 1
8 -5.0 0 0 2
where data is my data, and c, b and t are my factors, and then feed in
something like:
(aov.aov <- aov(data ~ c*b*t, aov.data))
and I get F-statistics for c, b and t and all possible interactions.
Because of the limitations of analysis of variance for my microarray
data, I would like to use limma. Is there any *more* documentation I
can look at that will tell me the steps to take to work out what my
limma design matrix will look like?
Kind regards
Michael