[BioC] dye effects stronger than dye-swaps?

[Paquet, Agnes]

Hi Jenny,
 
There are a couple of other things you can check to make sure you have the correct orientation:
- if this is a mutant vs. control, the investigator probably know about some upregulated/downregulated genes which caracterize the mutant. You can check the sign of the M values for probes corresponding to these genes to determine the correct orientation of the arrays.
-  If you have access to the actual image of the arrays, differentially expressed probes should show up with different colors on dye-swapped arrays. 
- Also, if the investigator already checked the expression of some of the genes using another method (like taqman), you could use this as a "true" value and check which arrays have the correct dye orientation.
 
Regards,
 
Agnes 

________________________________

From: bioconductor-bounces at stat.math.ethz.ch on behalf of Jenny Drnevich
Sent: Mon 4/30/2007 11:36 AM
To: bioconductor at stat.math.ethz.ch
Subject: [BioC] dye effects stronger than dye-swaps?



Hi everyone,

I have an interesting phenomenon in some microarray data, and
wondered if anyone else has seen anything like it. It's 2-color data,
comparing mutant vs. wildtype, 2 replicates plus dye-swaps for a
total of 4 arrays. The 'dye-swaps', instead of being negatively
correlated in M-values are instead strongly positively correlated,
even after within-array normalization. I triple checked to make sure
I didn't have the phenotypic info wrong, but all of the arrays are
positively correlated, which leads me to believe that dye-swapping
wasn't actually done. If you analyze as if it were a dye-swap
experiment, several thousands of genes still show a dye-effect,
whereas only dozens of genes show a MUvWT effect.

My question: is it possible that any dye-effects could be so strong,
even after within-array normalization, and treatment differences so
small that the arrays could be dye-swaps but still show a positive
correlation in M-values? Or is it more likely that dye-swapping
wasn't actually done?  I've tried to look at other dye-swapped data,
but everything I have has large treatment differences. The PI already
has the manuscript written, and just came to me to 'confirm' their
analysis, so I want to be pretty positive before I tell them their
work may have been wasted (of course, they may still decide to ignore me...)

Thanks,
Jenny

Jenny Drnevich, Ph.D.

Functional Genomics Bioinformatics Specialist
W.M. Keck Center for Comparative and Functional Genomics
Roy J. Carver Biotechnology Center
University of Illinois, Urbana-Champaign

330 ERML
1201 W. Gregory Dr.
Urbana, IL 61801
USA

ph: 217-244-7355
fax: 217-265-5066
e-mail: drnevich at uiuc.edu

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