Hello, I was wondering if there was any (easy) way to handle a nested design in limma. I looked in the Bioconductor archives, but the only references to nested designs weren't really nested - one was just a factorial design, and the other was a repeated measurement design, which could be done in limma as a blocking variable. In this experiment design, the treatments (infected and control) were made on the dams, but the effects were measured on multiple offspring per dam; hence dam is nested within treatment. In SAS terminology (forgive me...), the model would look like this: log2_expression = treatment + dam(treatment) , with dam as a random variable. The test statistic for treatment should now be formed using the variance due to dam(treatment) and not the error variance. Can limma be made to handle this sort of design? 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

Hi Gordon, I didn't know a nested design would be handled the same as duplicate spots, since duplicate spots are technical replicates but multiple offspring are independent replicates. I guess when I have some free time I'll look into the math of how the block and correlation are used in lmFit... Unfortunately, this solution doesn't help me in this case because there are also both duplicate spots and technical replicates of arrays! If duplicateCorrelation can only be used once, I was going to average the duplicate spots, use duplicateCorrelation for the dye-swapped tech reps, fit a coefficient for each dam, and then extract the difference between sets of dams as a contrast. I know this will treat dam as a fixed effect, rather than as a random effect, but I'm not sure if there's a better way to do it. Cheers, Jenny At 01:37 AM 2/21/2006, Gordon K Smyth wrote: >Hi Jenny, > >This design is qualitatively the same as the "duplicate spot" situation, >where the treatment is >applied at the array level but the measurements are made on multiple spots >per array. In your >case, treatments are applied to dams but measurements are made on multiple >offspring. > >Hence you can use the duplicateCorrelation() function in limma with dam as >the block. > >Best wishes >Gordon > >On Tue, February 21, 2006 6:03 am, Jenny Drnevich wrote: > > Hello, > > > > I was wondering if there was any (easy) way to handle a nested design in > > limma. I looked in the Bioconductor archives, but the only references to > > nested designs weren't really nested - one was just a factorial design, and > > the other was a repeated measurement design, which could be done in limma > > as a blocking variable. In this experiment design, the treatments (infected > > and control) were made on the dams, but the effects were measured on > > multiple offspring per dam; hence dam is nested within treatment. In SAS > > terminology (forgive me...), the model would look like this: > > log2_expression = treatment + dam(treatment) , with dam as a random > > variable. The test statistic for treatment should now be formed using the > > variance due to dam(treatment) and not the error variance. Can limma be > > made to handle this sort of design? > > > > 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 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

Hi Jenny, This design is qualitatively the same as the "duplicate spot" situation, where the treatment is applied at the array level but the measurements are made on multiple spots per array. In your case, treatments are applied to dams but measurements are made on multiple offspring. Hence you can use the duplicateCorrelation() function in limma with dam as the block. Best wishes Gordon On Tue, February 21, 2006 6:03 am, Jenny Drnevich wrote: > Hello, > > I was wondering if there was any (easy) way to handle a nested design in > limma. I looked in the Bioconductor archives, but the only references to > nested designs weren't really nested - one was just a factorial design, and > the other was a repeated measurement design, which could be done in limma > as a blocking variable. In this experiment design, the treatments (infected > and control) were made on the dams, but the effects were measured on > multiple offspring per dam; hence dam is nested within treatment. In SAS > terminology (forgive me...), the model would look like this: > log2_expression = treatment + dam(treatment) , with dam as a random > variable. The test statistic for treatment should now be formed using the > variance due to dam(treatment) and not the error variance. Can limma be > made to handle this sort of design? > > 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

Hi, I think that limma can handle both duplicate spots and dye-swaps simultaneously. My arrays have 9600 probes each spotted twice, i.e. the distance between the replicate spots is 9600. My experiment is designed basically as described in Limma User Guide (17. dec 2005), section 8.2 (the example that starts on page 36, the one with 3 wt and 3 mutant mice, 18 arrays in total), and is analyzed accordingly, except that I additionally handle duplicate spots in the call to lmFit: fit = lmFit(MA, design, ndups=2, spacing=9600) I have limited statistical expertice, so please tell me if this is totally wrong! Cheers Steen -----Original Message----- From: bioconductor-bounces@stat.math.ethz.ch [mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Jenny Drnevich Sent: 22. februar 2006 23:00 To: Gordon Smyth Cc: bioconductor at stat.math.ethz.ch Subject: Re: [BioC] nested design in limma? Hi Gordon, Thanks for your response. I started checking the correlations at each level: spot correlation is 0.81 and dye-swap pairs is weaker, -0.20, but perhaps not so weak as to be ignorable. The big problem occurred when trying to estimate correlations within dams as a block effect, because the arrays are direct comparisons, and of the three offspring from dam C1, one is compared to an offspring from dam T1, one to an offspring from dam T2 and one to an offspring from dam T3 - so there are no good blocking groups! Going to a separate channel analysis requires yet another level of correlation - intra-spot, so that's probably not an option either. >Just as an aside, I am continually amazed at how common technical >dye-swaps are. As far as I can see, they just complicate the analysis to >no advantage, yet they have captured the imagination of many biologists. >My guess is that this an attempt to balance the dyes, although this can be >better achieved without introducing technical replication. The sad thing (about me) is that I advised the researchers on the experimental design! I definitely agree now that technical dye-swaps are probably a waste of arrays. This was my first time handling spotted data, and I didn't appreciate all the intricacies that are involved; I had seen that limma had methods to handle duplicate spots and dye-swap technical reps, but I didn't realize that they could not be used simultaneously until starting to work with duplicateCorrelation and the ndups & block options within lmFit. I don't think this warning was in the vignette anywhere - perhaps a short sentence could be added to the technical replication section? Cheers, Jenny >Cheers >Gordon > >At 04:27 AM 22/02/2006, Jenny Drnevich wrote: >>Hi Gordon, >> >>I didn't know a nested design would be handled the same as duplicate >>spots, since duplicate spots are technical replicates but multiple >>offspring are independent replicates. I guess when I have some free time >>I'll look into the math of how the block and correlation are used in >>lmFit... Unfortunately, this solution doesn't help me in this case >>because there are also both duplicate spots and technical replicates of >>arrays! If duplicateCorrelation can only be used once, I was going to >>average the duplicate spots, use duplicateCorrelation for the dye-swapped >>tech reps, fit a coefficient for each dam, and then extract the >>difference between sets of dams as a contrast. I know this will treat dam >>as a fixed effect, rather than as a random effect, but I'm not sure if >>there's a better way to do it. >> >>Cheers, >>Jenny >> >>At 01:37 AM 2/21/2006, Gordon K Smyth wrote: >>>Hi Jenny, >>> >>>This design is qualitatively the same as the "duplicate spot" >>>situation, >>>where the treatment is >>>applied at the array level but the measurements are made on multiple >>>spots per array. In your >>>case, treatments are applied to dams but measurements are made on >>>multiple offspring. >>> >>>Hence you can use the duplicateCorrelation() function in limma with >>>dam >>>as the block. >>> >>>Best wishes >>>Gordon >>> >>>On Tue, February 21, 2006 6:03 am, Jenny Drnevich wrote: >>> > Hello, >>> > >>> > I was wondering if there was any (easy) way to handle a nested >>> > design in limma. I looked in the Bioconductor archives, but the >>> > only references to nested designs weren't really nested - one was >>> > just a factorial >>> design, and >>> > the other was a repeated measurement design, which could be done >>> > in limma as a blocking variable. In this experiment design, the >>> > treatments >>> (infected >>> > and control) were made on the dams, but the effects were measured >>> > on multiple offspring per dam; hence dam is nested within >>> > treatment. In SAS terminology (forgive me...), the model would >>> > look like this: log2_expression = treatment + dam(treatment) , >>> > with dam as a random variable. The test statistic for treatment >>> > should now be formed using the variance due to dam(treatment) and >>> > not the error variance. Can limma be made to handle this sort of >>> > design? >>> > >>> > 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 >> >>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

It all depends on whether the dye-swaps are technical or biological reps. If they are biological reps, there is only one blocking factor (array) which handles the duplicate spots on the same array. If they are technical reps, there is a 2nd blocking factor (RNA source) which should be accounted for. --Naomi At 05:33 PM 2/22/2006, STKH (Steen Krogsgaard) wrote: >Hi, > >I think that limma can handle both duplicate spots and dye-swaps >simultaneously. My arrays have 9600 probes each spotted twice, i.e. the >distance between the replicate spots is 9600. My experiment is designed >basically as described in Limma User Guide (17. dec 2005), section 8.2 >(the example that starts on page 36, the one with 3 wt and 3 mutant >mice, 18 arrays in total), and is analyzed accordingly, except that I >additionally handle duplicate spots in the call to lmFit: > >fit = lmFit(MA, design, ndups=2, spacing=9600) > >I have limited statistical expertice, so please tell me if this is >totally wrong! > >Cheers >Steen > >-----Original Message----- >From: bioconductor-bounces at stat.math.ethz.ch >[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Jenny >Drnevich >Sent: 22. februar 2006 23:00 >To: Gordon Smyth >Cc: bioconductor at stat.math.ethz.ch >Subject: Re: [BioC] nested design in limma? > > >Hi Gordon, > >Thanks for your response. I started checking the correlations at each >level: spot correlation is 0.81 and dye-swap pairs is weaker, -0.20, but > >perhaps not so weak as to be ignorable. The big problem occurred when >trying to estimate correlations within dams as a block effect, because >the >arrays are direct comparisons, and of the three offspring from dam C1, >one >is compared to an offspring from dam T1, one to an offspring from dam T2 > >and one to an offspring from dam T3 - so there are no good blocking >groups! >Going to a separate channel analysis requires yet another level of >correlation - intra-spot, so that's probably not an option either. > > >Just as an aside, I am continually amazed at how common technical > >dye-swaps are. As far as I can see, they just complicate the analysis >to > >no advantage, yet they have captured the imagination of many >biologists. > >My guess is that this an attempt to balance the dyes, although this can >be > >better achieved without introducing technical replication. > >The sad thing (about me) is that I advised the researchers on the >experimental design! I definitely agree now that technical dye-swaps are > >probably a waste of arrays. This was my first time handling spotted >data, >and I didn't appreciate all the intricacies that are involved; I had >seen >that limma had methods to handle duplicate spots and dye-swap technical >reps, but I didn't realize that they could not be used simultaneously >until >starting to work with duplicateCorrelation and the ndups & block options > >within lmFit. I don't think this warning was in the vignette anywhere - >perhaps a short sentence could be added to the technical replication >section? > >Cheers, >Jenny > > > > >Cheers > >Gordon > > > >At 04:27 AM 22/02/2006, Jenny Drnevich wrote: > >>Hi Gordon, > >> > >>I didn't know a nested design would be handled the same as duplicate > >>spots, since duplicate spots are technical replicates but multiple > >>offspring are independent replicates. I guess when I have some free >time > >>I'll look into the math of how the block and correlation are used in > >>lmFit... Unfortunately, this solution doesn't help me in this case > >>because there are also both duplicate spots and technical replicates >of > >>arrays! If duplicateCorrelation can only be used once, I was going to > >>average the duplicate spots, use duplicateCorrelation for the >dye-swapped > >>tech reps, fit a coefficient for each dam, and then extract the > >>difference between sets of dams as a contrast. I know this will treat >dam > >>as a fixed effect, rather than as a random effect, but I'm not sure if > > >>there's a better way to do it. > >> > >>Cheers, > >>Jenny > >> > >>At 01:37 AM 2/21/2006, Gordon K Smyth wrote: > >>>Hi Jenny, > >>> > >>>This design is qualitatively the same as the "duplicate spot" > >>>situation, > >>>where the treatment is > >>>applied at the array level but the measurements are made on multiple > >>>spots per array. In your > >>>case, treatments are applied to dams but measurements are made on > >>>multiple offspring. > >>> > >>>Hence you can use the duplicateCorrelation() function in limma with > >>>dam > >>>as the block. > >>> > >>>Best wishes > >>>Gordon > >>> > >>>On Tue, February 21, 2006 6:03 am, Jenny Drnevich wrote: > >>> > Hello, > >>> > > >>> > I was wondering if there was any (easy) way to handle a nested > >>> > design in limma. I looked in the Bioconductor archives, but the > >>> > only references to nested designs weren't really nested - one was > >>> > just a factorial > >>> design, and > >>> > the other was a repeated measurement design, which could be done > >>> > in limma as a blocking variable. In this experiment design, the > >>> > treatments > >>> (infected > >>> > and control) were made on the dams, but the effects were measured > >>> > on multiple offspring per dam; hence dam is nested within > >>> > treatment. In SAS terminology (forgive me...), the model would > >>> > look like this: log2_expression = treatment + dam(treatment) , > >>> > with dam as a random variable. The test statistic for treatment > >>> > should now be formed using the variance due to dam(treatment) and > >>> > not the error variance. Can limma be made to handle this sort of > >>> > design? > >>> > > >>> > 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 > >> > >>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 > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor 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

Gordon Smyth <smyth at="" wehi.edu.au=""> writes: > > Just as an aside, I am continually amazed at how common technical > dye-swaps are. As far as I can see, they just complicate the analysis > to no advantage, yet they have captured the imagination of many > biologists. My guess is that this an attempt to balance the dyes, > although this can be better achieved without introducing technical replication. This is an interesting point, since I am going to design and analyse two-colour experiments soon and I am interested in what people think about it. Do you recommend dye-swapping with biological replicates? Or no dye-swapping at all? Best, Georg