[BioC] dye swap vs. control channel

[Naomi Altman]

I do not think this is a research question any more.  If you need to 
compare with the reference sample, then dye-swaps are necessary.
If the reference sample is used only for normalization, then it is better 
NOT to dye-swap, since you lose information (i.e. 1 d.f.) from having a 
dye-effect.

Loop designs are more efficient, but more complicated to analyze.  They 
have dye-swap built into the loop (so it is not necessary to run the loop 
in both directions, as some investigators are doing).

--Naomi Altman

p.s. I also strongly suspect that if you do dye-swaps, you should do it on 
biological replicates. (Is there any evidence for biological rep x dye 
interaction?)  If the limiting factor is arrays (not RNA samples) then 
biological replicates are more effective than technical 
replicates.  Introducing technical replication requires a random effect in 
the ANOVA that is not handled (or handled incorrectly) by most of the 
software for microarray ANOVA.  Because of this, I use LME when there are 
technical replicates, and this is slow, slow, slow.  Also, if I want to 
"borrow strength" across genes, I have to write my own routines, which is a 
bit tougher to do, and to document for papers.

At 12:48 AM 10/23/2003, Isaac Mehl wrote:
>this design brings up a question i always have.  is it "better" to do
>all experiments in one channel (Cy5) and compare every sample to a
>standard (cy3)?  this way you can use less arrays or do more biological
>replicates.  IMHO getting repeated measurements of biological variation
>is more important than dye swap.
>
>very interested to hear what people think about this topic since it is
>integral to experimental design.
>
>-isaac
>
>
>
>DIF1,2 and 3 are different but similar drugs...............
> >>
> >> Slides 1-6 are treatment 1 (DIF1) Vs No treatment
> >> Slide1 Cy5/Cy3 (DIF1/no treatment)
> >> Slide2 Cy3/Cy5 (DIF1/no treatment)
> >> Slide3 Cy5/Cy3 (DIF1/no treatment)
> >> Slide4 Cy3/Cy5 (DIF1/no treatment)
> >> Slide5 Cy3/Cy5 (DIF1/no treatment)
> >> Slide6 Cy5/Cy3 (DIF1/no treatment)
> >>
> >> Slides 7-12 are treatment 2 (DIF2) Vs No treatment
> >> Slide7 Cy5/Cy3 (DIF2/no treatment)
> >> Slide8 Cy3/Cy5 (DIF2/no treatment)
> >> Slide9 Cy5/Cy3 (DIF2/no treatment)
> >> Slide10 Cy3/Cy5 (DIF2/no treatment)
> >> Slide11 Cy3/Cy5 (DIF2/no treatment)
> >> Slide12 Cy5/Cy3 (DIF2/no treatment)
> >>
> >> Slides 13-18 are treatment 3(DIF3) Vs No treatment
> >> Slide13 Cy5/Cy3 (DIF3/no treatment)
> >> Slide14 Cy3/Cy5 (DIF3/no treatment)
> >> Slide15 Cy5/Cy3 (DIF3/no treatment)
> >> Slide16 Cy3/Cy5 (DIF3/no treatment)
> >> Slide17 Cy3/Cy5 (DIF3/no treatment)
> >> Slide18 Cy5/Cy3 (DIF3/no treatment)
> >>
> >> I'd obviously like to compare across the different treatments DIF1,2
> >> and 3
>--
>-isaac mehl
>gene expression lab (gele)
>salk institute
>10010 n. torrey pines rd.
>la jolla ca. 92037
>http://genex.salk.edu
>
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Bioinformatics Consulting Center
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111