Separating channels of a two-color microarray
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@january-weiner-3999
Last seen 9.6 years ago
Dear all, first, I would like to thank all who answered my questions in the past. I am attempting a meta-analysis of several microarray studies, with limma as my working horse. I plan to throw all the microarrays together, creating one large data set with one of the factors in the analysis being the data set. Preliminary analyses with a limited number of studies are encouraging; on one hand, I am able to reproduce the results of the single studies, while at the same time finding robust differences between the studies (and their respective cohorts). However, I hit a wall with one of these studies, which involved two-color Agilent chips, not with a common reference, but each chip corresponding to two different individuals from two experimental groups. For some of the analyses that I plan I need separate intensities for each experimental group -- fold changes won't cut it (for example, in case of machine learning in which I use the intensities to construct a model for predicting the group assignments). I tried to directly use the R and G channels, and the results are actually quite good. Of course, this is not an optimal approach. Normally, when faced with two-color arrays and a complex experimental design I use intraspotCorrelation and lmscFit. Question: is there a way to use the results of intraspotCorrelation to correct the R and G channels? Kind regards, j. -- -------- Dr. January Weiner 3 --------------------------------------
Microarray Microarray • 1.1k views
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Naomi Altman ★ 6.0k
@naomi-altman-380
Last seen 3.0 years ago
United States
After single channel normalization, I usually use MA.RG to transform back to R and G. It has worked remarkably well. However, I retain the array effect in the model. Regards, Nasomi Altman At 03:55 AM 2/8/2012, January Weiner wrote: >Dear all, > >first, I would like to thank all who answered my questions in the past. > >I am attempting a meta-analysis of several microarray studies, with >limma as my working horse. I plan to throw all the microarrays >together, creating one large data set with one of the factors in the >analysis being the data set. Preliminary analyses with a limited >number of studies are encouraging; on one hand, I am able to reproduce >the results of the single studies, while at the same time finding >robust differences between the studies (and their respective cohorts). > >However, I hit a wall with one of these studies, which involved >two-color Agilent chips, not with a common reference, but each chip >corresponding to two different individuals from two experimental >groups. For some of the analyses that I plan I need separate >intensities for each experimental group -- fold changes won't cut it >(for example, in case of machine learning in which I use the >intensities to construct a model for predicting the group >assignments). > >I tried to directly use the R and G channels, and the results are >actually quite good. Of course, this is not an optimal approach. >Normally, when faced with two-color arrays and a complex experimental >design I use intraspotCorrelation and lmscFit. > >Question: is there a way to use the results of intraspotCorrelation to >correct the R and G channels? > >Kind regards, >j. > >-- >-------- Dr. January Weiner 3 -------------------------------------- > >_______________________________________________ >Bioconductor mailing list >Bioconductor at r-project.org >https://stat.ethz.ch/mailman/listinfo/bioconductor >Search the archives: >http://news.gmane.org/gmane.science.biology.informatics.conductor
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Dear Naomi, > After single channel normalization, I usually use MA.RG to transform back to > R and G. ?It has worked remarkably well. Yes, this is also my usual procedure. However, as you noticed, this does not help us with getting rid of the intra-array correlation. I was hoping that there might me a standard procedure to correct for this. While normally one does not really need the R & G channels, I have particular situations where I would like to have them, like the abovementioned meta-study. In that particular case it would be extremely important to get rid of any correlations between the channels in one array. Kind regards, January However, I retain the array effect > in the model. > > Regards, > Nasomi Altman > > > > At 03:55 AM 2/8/2012, January Weiner wrote: >> >> Dear all, >> >> first, I would like to thank all who answered my questions in the past. >> >> I am attempting a meta-analysis of several microarray studies, with >> limma as my working horse. I plan to throw all the microarrays >> together, creating one large data set with one of the factors in the >> analysis being the data set. Preliminary analyses with a limited >> number of studies are encouraging; on one hand, I am able to reproduce >> the results of the single studies, while at the same time finding >> robust differences between the studies (and their respective cohorts). >> >> However, I hit a wall with one of these studies, which involved >> two-color Agilent chips, not with a common reference, but each chip >> corresponding to two different individuals from two experimental >> groups. For some of the analyses that I plan I need separate >> intensities for each experimental group -- fold changes won't cut it >> (for example, in case of machine learning in which I use the >> intensities to construct a model for predicting the group >> assignments). >> >> I ?tried to directly use the R and G channels, and the results are >> actually quite good. Of course, this is not an optimal approach. >> Normally, when faced with two-color arrays and a complex experimental >> design I use intraspotCorrelation and lmscFit. >> >> Question: is there a way to use the results of intraspotCorrelation to >> correct the R and G channels? >> >> Kind regards, >> j. >> >> -- >> -------- Dr. January Weiner 3 -------------------------------------- >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > -- -------- Dr. January Weiner 3 -------------------------------------- Max Planck Institute for Infection Biology Charit?platz 1 D-10117 Berlin, Germany Web?? : www.mpiib-berlin.mpg.de Tel? ?? : +49-30-28460514 Fax ? ?: +49-30-28450505
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