[BioC] Help on factorial experiment analysis using limma

[Yuk Fai Leung]

Hi there, 

I have an experiment that looks like factorial experiment and I want to give
it a try to use limma for its analysis. And I have a few questions

The experimental scheme looks like this:

0: not-treated with the respective factor
a: treated by factor a
b: treated by factor b
ab: treated by both factor a & b

00 -------> 0b
|		 |
|		 |
|		 |
|		 |
v		 v
a0 -------> ab

There are also two diagonal hybridizations which I can't easily draw here:

00 ------> ab
0b ------> a0

The arrow points towards RNA sample labeled with Cy5 and I have two
technical replications for each arrow. Therefore there are 6 experiments * 2
replicates = 12 arrays


Here are my questions:

1. Is my experimental design suitable for limma analysis? 

2. Should I combine the technical replicates before I calculate the liner
model by lm.series, or just treat the 12 arrays like individual experiment
and enter them to 12 different columns of the M matrix for lm.series? If I
should combine them beforehand, how should I do that?

3. Is the following design matrix correct for my experiment? If I can treat
the technical replicates as individual experiment, should I duplicate the
each row of the design matrix to reflect the replicated data in the M
matrix?

		b	a	ba	
00 -> a0 	0 	1	0
0b -> ab	0	1	1
00 -> 0b	1	0	0	
a0 -> ab	1	0	1
00 -> ab	1	1	1
0b -> a0	-1	1	0

4. Could someone give me some pointers on using heatdiagram to interpret my
data? For example, if I am interested in the physiological effect of factor
a, but the factor b is a potential confounding factor, what would the
expression level of those genes only being regulated by factor a look like
in the diagram? Besides, how can I interpret the interaction effect of a &
b? Or is there other method to do these?


Thanks in advance!

Best regards,
Fai
________
Yuk Fai Leung
Bauer Center for Genomics Research
Harvard University
7 Divinity Avenue
Cambridge, MA 02138
Tel: 617-496-7134
Fax: 617-495-2196
email: yfleung at cgr.harvard.edu; yfleung at genomicshome.com
URL: http://genomicshome.com