[BioC] self-self hybridization and limma

Sean Davis sdavis2 at mail.nih.gov
Tue Mar 29 22:09:16 CEST 2005

On Mar 29, 2005, at 2:46 PM, Na, Ren wrote:

> hello,
> If we have many samples to be compared in an microarray experiment,
> for example,
> 	tissueType1	tissueType2	tissueType3
> age1	4		4		4
> age2	4		4		4
> age3	4		4		4
> each kind of sample has 4 biological replicates, primary interest are 
> differential
> expression among different age groups and among different tissueTypes. 
> We usually
> use common reference design. I am wondering if I can use self-self 
> hybridization design,
> in which two identical samples are labeled with different dyes and 
> hybridized to the
> same slide. maybe I don't need to worry about dye bias by using 
> log-intensity A-value
> for each spot, and use limma analyze like,
> MA<-normalizeWithinArrays(RG, method="none")
> MA<-normalizeBetweenArrays(MA, method="Aq")
> convert MA to exprSet, then replace M-value in exprSet with A-value, 
> then use the new
> exprSet to get significant genes using limma. I only know self-self 
> experiment to be
> used to show imbalance in red and green intensity, but I never found 
> it to be used to
> do real experiment. I think there must be some reasons that self-self 
> hybridization is
> not appropriate.
> Could anyone explain it, Thanks in advance!

These are some useful links for thinking about factorial designs.  Note 
that the limma user guide also contains examples of factorial design.


In practice, a direct design can cut the variance in half when 
comparing two samples on two arrays via a common reference versus the 
same two samples on a single array, so they can be very useful.  The 
dye bias is a real phenomenon, so needs to be accounted for in the 
design of the experiment (dye swaps).


More information about the Bioconductor mailing list