Hi everyone, Thanks for all the responses about what could be causing positive correlations between dye-swaps. I've talked with the PI, and it looks like degradation of the Cy5 dye is the most likely culprit. Another thing to add to my 'bizarre microarray results' category... Cheers, Jenny At 03:09 PM 4/30/2007, Naomi Altman wrote: >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 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

There is an ozone protectorant that can be added to the arrays that reduces degradation of Cy5. I do not know the details. --Naomi At 11:36 AM 5/1/2007, Jenny Drnevich wrote: >Hi everyone, > >Thanks for all the responses about what could be causing positive >correlations between dye-swaps. I've talked with the PI, and it >looks like degradation of the Cy5 dye is the most likely culprit. >Another thing to add to my 'bizarre microarray results' category... > >Cheers, >Jenny > >At 03:09 PM 4/30/2007, Naomi Altman wrote: >>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 > >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 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