dye effects stronger than dye-swaps?
3
0
Entering edit mode
Jenny Drnevich ★ 2.2k
@jenny-drnevich-382
Last seen 7.1 years ago
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
Microarray Normalization Microarray Normalization • 660 views
ADD COMMENT
0
Entering edit mode
Paquet, Agnes ▴ 500
@paquet-agnes-807
Last seen 7.1 years ago
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@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 _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
ADD COMMENT
0
Entering edit mode
@sean-davis-490
Last seen 6 weeks ago
United States
On Monday 30 April 2007 14:36, Jenny Drnevich wrote: > 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...) Jenny, Just a quick question--were the samples amplified? Sean
ADD COMMENT
0
Entering edit mode
Hi Sean, I don't know - I'm trying to find out. How would that affect things - if they were amplified, then dye-effects could be very strong? Thanks, Jenny >Jenny, > >Just a quick question--were the samples amplified? > >Sean 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
ADD REPLY
0
Entering edit mode
On Monday 30 April 2007 15:04, Jenny Drnevich wrote: > Hi Sean, > > I don't know - I'm trying to find out. How would that affect things - > if they were amplified, then dye-effects could be very strong? I could be off-base here, but I think there is at least a theoretical possibility that the effects you are seeing aren't dye effects, but due to differential amplification of one sample source over another. This would lead to one sample source always showing some bias over the other at some probes, regardless of the dye. I have no idea how common such an effect occurs, though. Sean
ADD REPLY
0
Entering edit mode
Naomi Altman ★ 6.0k
@naomi-altman-380
Last seen 6 months ago
United States
And then again, there is the dye degradation problem. If one dye severely degrades, then you will get a strong positive correlation. Try plotting R vs R and G vs G, instead of M vs M. --Naomi At 03:56 PM 4/30/2007, Paquet, Agnes wrote: >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 > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor >Search the archives: >http://news.gmane.org/gmane.science.biology.informatics.conductor > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor >Search the archives: >http://news.gmane.org/gmane.science.biology.informatics.conductor Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111
ADD COMMENT

Login before adding your answer.

Traffic: 501 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6