Repeat Measurement with Limma Fw: limma matrix desgin question?
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Entering edit mode
Xiaokuan Wei ▴ 230
@xiaokuan-wei-4052
Last seen 6.9 years ago
United States
Dear List, I think I know how to do this with limma. I first calculate the correlation treating each strain as a block block<-rep(1:3,c(4,3,2)) biocor<-duplicateCorrelation(eset,design,block=block) then fit<-lmFit(eset,design=design,block=block,correlation=biocor$consensus ) then create contrast matrix and extract coef of each comparison. Is this right? However, I have a further question. In fact, each chip has 3 technical replicates. In order to simply the anlaysis, I just average the replicates and then use limma to do the job. How could I include such technical replicate information and repeat measurement information together with using Limma. Could Gordon or someone give me some hints or example? Thank you very much. replicate Strain Day Treatment 1 B6 0 t1 2 B6 0 t1 3 B6 0 t1 1 B6 14 t1 2 B6 14 t1 3 B6 14 t1 1 B6 0 t2 2 B6 0 t2 3 B6 0 t2 1 B6 14 t2 2 B6 14 t2 3 B6 14 t2 1 Balbc 0 t1 2 Balbc 0 t1 3 Balbc 0 t1 1 Balbc 14 t1 2 Balbc 14 t1 3 Balbc 14 t1 1 Balbc 0 t2 2 Balbc 0 t2 3 Balbc 0 t2 1 J129 0 t1 2 J129 0 t1 3 J129 0 t1 1 J129 0 t2 2 J129 0 t2 3 J129 0 t2 1 J129 14 t2 2 J129 14 t2 3 J129 14 t2 -Xiaokuan ----- Forwarded Message ---- From: Xiaokuan Wei <weixiaokuan@yahoo.com> To: bioconductor <bioconductor@stat.math.ethz.ch> Sent: Wed, June 9, 2010 11:53:57 AM Subject: limma matrix desgin question? Dear List, I have an experiment trying to evaluate two treatment for mice. I have 3 normal strains, two treatment and two time points. The goal is to compare t2 vs t1 Day14 vs Day0. All these mice considered normal. So I can create factor such as day0_t1, day0_t2, day14_t1, and day14_t2. and make contrasts, such as day0_t2-day0_t1, day14_t2-day14_t1, day14_t2-day14_t1... But how can I include strain information into the comparison? Since each strain's data will be correlated? Thank you. Xiaokuan Strain Day Treatment B6 0 t1 B6 14 t1 B6 0 t2 B6 14 t2 Balbc 0 t1 Balbc 14 t1 Balbc 0 t2 J129 0 t1 J129 0 t2 J129 14 t2 [[alternative HTML version deleted]] limma limma • 1.2k views ADD COMMENT 0 Entering edit mode Xiaokuan Wei ▴ 230 @xiaokuan-wei-4052 Last seen 6.9 years ago United States Mark, Thank you for your prompt response. In this experiment, I consider all these 3 strains are almost the same (wild type). And there are only 3 mice, each received different treatment at diffierent time. (Sorry, forgetting to mention this important info, even 0 t1 and 0 t2 are received at different time). the repeated treatment to each mouse need to be included in the model. That's why I average the technical replicates and then used each mouse as block. I did try to use technical replicates as block. But I am not sure how to capture the repeated measurements. The data frame I have here is: V1 V2 V3 V4 1 1 B6 0 t1 2 2 B6 0 t1 3 3 B6 0 t1 4 1 B6 14 t1 5 2 B6 14 t1 6 3 B6 14 t1 7 1 B6 0 t2 8 2 B6 0 t2 9 3 B6 0 t2 10 1 B6 14 t2 11 2 B6 14 t2 12 3 B6 14 t2 13 1 Balbc 0 t1 14 2 Balbc 0 t1 15 3 Balbc 0 t1 16 1 Balbc 14 t1 17 2 Balbc 14 t1 18 3 Balbc 14 t1 19 1 Balbc 0 t2 20 2 Balbc 0 t2 21 3 Balbc 0 t2 22 1 J129 0 t1 23 2 J129 0 t1 24 3 J129 0 t1 25 1 J129 0 t2 26 2 J129 0 t2 27 3 J129 0 t2 28 1 J129 14 t2 29 2 J129 14 t2 30 3 J129 14 t2 create factors (I only care time and treatment): f<-paste(df$V3,"_",df$V4,sep="") thus, we have factor: 0_t1,0_t2,14_t1,14_t2. create block using technical replecates: block<-rep(1:10,each=3) then make the design: design<-model.matrix(~ -1+factor(f, levels=unique(f)) colnames(design)<-unique(f) biocor<-duplicateCorrelation(eset,desgin,block=block) fit<-lmFit(eset,design=design,block=block,correlation=biocor$consensus ) contrast.matrix<-makeContrasts(0_t2-0_t1, 14_t2-14_t1, 14_t2-14_t1, 0_t2-0_t1) As you can see from the above analysis, I just treat each set experiment independently after considering technical replicates, and did not include repeated measurement effects for each mouse into the model. The goal here is to compare the t2 and t1 at day0 and day14. These 3 mice are considered pathologically same even they have some different genetic background. Do you have any suggestion to include repeated measurement of each mouse into the model? Thank you very much. -Xiaokuan ________________________________ From: Mark Cowley <m.cowley0@gmail.com> Cc: bioconductor <bioconductor@stat.math.ethz.ch> Sent: Wed, June 9, 2010 8:23:09 PM Subject: Re: [BioC] Repeat Measurement with Limma Fw: limma matrix desgin question? hi Xiaokuan, i'd be treating the technical replicates as the block, and strain, day and treatment as the biological effects of interest block <- rep(1:10, each=3) that way limma has triple the number of arrays to use in its variance estimation you have a few options for your design matrix, such as strain + day + treatment + any interaction effects you're interested in or you can simply treat these samples as 10 different groups and then setup contrasts to identify the effects of interest cheers, mark ----------------------------------------------------- Mark Cowley, PhD Peter Wills Bioinformatics Centre Garvan Institute of Medical Research, Sydney, Australia ----------------------------------------------------- On 10/06/2010, at 6:39 AM, Xiaokuan Wei wrote: > > > Dear List, > > I think I know how to do this with limma. > I first calculate the correlation treating each strain as a block > block<-rep(1:3,c(4,3,2)) > biocor<-duplicateCorrelation(eset,design,block=block) > then > fit<-lmFit(eset,design=design,block=block,correlation=biocor$consens us) > then create contrast matrix and extract coef of each comparison. > > Is this right? > > However, I have a further question. In fact, each chip has 3 technical replicates. In order to simply the anlaysis, I just average the replicates and then use limma to do the job. > How could I include such technical replicate information and repeat measurement information together with using Limma. Could Gordon or someone give me some hints or example? > Thank you very much. > > > replicate Strain Day Treatment > 1 B6 0 t1 > 2 B6 0 t1 > 3 B6 0 t1 > 1 B6 14 t1 > 2 B6 14 t1 > 3 B6 14 t1 > 1 B6 0 t2 > 2 B6 0 t2 > 3 B6 0 t2 > 1 B6 14 t2 > 2 B6 14 t2 > 3 B6 14 t2 > 1 Balbc 0 t1 > 2 Balbc 0 t1 > 3 Balbc 0 t1 > 1 Balbc 14 t1 > 2 Balbc 14 t1 > 3 Balbc 14 t1 > 1 Balbc 0 t2 > 2 Balbc 0 t2 > 3 Balbc 0 t2 > 1 J129 0 t1 > 2 J129 0 t1 > 3 J129 0 t1 > 1 J129 0 t2 > 2 J129 0 t2 > 3 J129 0 t2 > 1 J129 14 t2 > 2 J129 14 t2 > 3 J129 14 t2 > > > > > > > -Xiaokuan > > > > > > > > > > > > > ----- Forwarded Message ---- > To: bioconductor <bioconductor@stat.math.ethz.ch> > Sent: Wed, June 9, 2010 11:53:57 AM > Subject: limma matrix desgin question? > > > Dear List, > > I have an experiment trying to evaluate two treatment for mice. I have 3 normal strains, two treatment and two time points. > The goal is to compare t2 vs t1 Day14 vs Day0. > All these mice considered normal. So I can create factor such as day0_t1, day0_t2, day14_t1, and day14_t2. > and make contrasts, such as day0_t2-day0_t1, day14_t2-day14_t1, day14_t2-day14_t1... > > But how can I include strain information into the comparison? Since each strain's data will be correlated? > > Thank you. > > Xiaokuan > > > > > > > Strain Day Treatment > B6 0 t1 > B6 14 t1 > B6 0 t2 > B6 14 t2 > Balbc 0 t1 > Balbc 14 t1 > Balbc 0 t2 > J129 0 t1 > J129 0 t2 > J129 14 t2 > > > > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor [[alternative HTML version deleted]] ADD COMMENT 0 Entering edit mode hi, you could still treat each strain x time x treatment combo as a separate group, ie 10 groups, and then use contrasts to compute the average 0_t1 effect over the 3 mice, eg: # i've reformatted you contrast matrix creation line -- i don't know why you've duplicated your contrasts here contrast.matrix<-makeContrasts( 0_t2-0_t1, 14_t2-14_t1, 14_t2-14_t1, 0_t2-0_t1 ) # alternative contrast.matrix<-makeContrasts( time0=(B6_0_t2+Balbc_0_t2+J129_0_t2)/3 - (B6_0_t1+Balbc_0_t1+J129_0_t1)/3, time14=(B6_14_t2+Balbc_14_t2+J129_14_t2)/3 - (B6_14_t1+Balbc_14_t1+J129_14_t1)/3 ) i've just noticed your design is unbalanced with no Balbc_14_t2 or J129_14_t1 groups. i'll leave it to you to adjust the relevant contrasts to be the average of 2 groups. cheers, Mark On 10/06/2010, at 12:06 PM, Xiaokuan Wei wrote: > Mark, > > Thank you for your prompt response. > In this experiment, I consider all these 3 strains are almost the same (wild type). And there are only 3 mice, each received different treatment at diffierent time. (Sorry, forgetting to mention this important info, even 0 t1 and 0 t2 are received at different time). > the repeated treatment to each mouse need to be included in the model. That's why I average the technical replicates and then used each mouse as block. > > I did try to use technical replicates as block. But I am not sure how to capture the repeated measurements. The data frame I have here is: > V1 V2 V3 V4 > 1 1 B6 0 t1 > 2 2 B6 0 t1 > 3 3 B6 0 t1 > 4 1 B6 14 t1 > 5 2 B6 14 t1 > 6 3 B6 14 t1 > 7 1 B6 0 t2 > 8 2 B6 0 t2 > 9 3 B6 0 t2 > 10 1 B6 14 t2 > 11 2 B6 14 t2 > 12 3 B6 14 t2 > 13 1 Balbc 0 t1 > 14 2 Balbc 0 t1 > 15 3 Balbc 0 t1 > 16 1 Balbc 14 t1 > 17 2 Balbc 14 t1 > 18 3 Balbc 14 t1 > 19 1 Balbc 0 t2 > 20 2 Balbc 0 t2 > 21 3 Balbc 0 t2 > 22 1 J129 0 t1 > 23 2 J129 0 t1 > 24 3 J129 0 t1 > 25 1 J129 0 t2 > 26 2 J129 0 t2 > 27 3 J129 0 t2 > 28 1 J129 14 t2 > 29 2 J129 14 t2 > 30 3 J129 14 t2 > > create factors (I only care time and treatment): > > f<-paste(df$V3,"_",df$V4,sep="") > > thus, we have factor: 0_t1,0_t2,14_t1,14_t2. > > create block using technical replecates: > block<-rep(1:10,each=3) > > then make the design: > design<-model.matrix(~ -1+factor(f, levels=unique(f)) > colnames(design)<-unique(f) > > biocor<-duplicateCorrelation(eset,desgin,block=block) > fit<-lmFit(eset,design=design,block=block,correlation=biocor$consens us) > > > contrast.matrix<-makeContrasts(0_t2-0_t1, 14_t2-14_t1, 14_t2-14_t1, 0_t2-0_t1) > > As you can see from the above analysis, I just treat each set experiment independently after considering technical replicates, and did not include repeated measurement effects for each mouse into the model. > The goal here is to compare the t2 and t1 at day0 and day14. These 3 mice are considered pathologically same even they have some different genetic background. > > Do you have any suggestion to include repeated measurement of each mouse into the model? > > Thank you very much. > > -Xiaokuan > > > > > > > > > > > > > > > > ________________________________ > From: Mark Cowley <m.cowley0 at="" gmail.com=""> > > Cc: bioconductor <bioconductor at="" stat.math.ethz.ch=""> > Sent: Wed, June 9, 2010 8:23:09 PM > Subject: Re: [BioC] Repeat Measurement with Limma Fw: limma matrix desgin question? > > hi Xiaokuan, > i'd be treating the technical replicates as the block, and strain, day and treatment as the biological effects of interest > block <- rep(1:10, each=3) > that way limma has triple the number of arrays to use in its variance estimation > > you have a few options for your design matrix, such as strain + day + treatment + any interaction effects you're interested in > or you can simply treat these samples as 10 different groups and then setup contrasts to identify the effects of interest > > cheers, > mark > ----------------------------------------------------- > Mark Cowley, PhD > > Peter Wills Bioinformatics Centre > Garvan Institute of Medical Research, Sydney, Australia > ----------------------------------------------------- > > > On 10/06/2010, at 6:39 AM, Xiaokuan Wei wrote: > >> >> >> Dear List, >> >> I think I know how to do this with limma. >> I first calculate the correlation treating each strain as a block >> block<-rep(1:3,c(4,3,2)) >> biocor<-duplicateCorrelation(eset,design,block=block) >> then >> fit<-lmFit(eset,design=design,block=block,correlation=biocor$consen sus) >> then create contrast matrix and extract coef of each comparison. >> >> Is this right? >> >> However, I have a further question. In fact, each chip has 3 technical replicates. In order to simply the anlaysis, I just average the replicates and then use limma to do the job. >> How could I include such technical replicate information and repeat measurement information together with using Limma. Could Gordon or someone give me some hints or example? >> Thank you very much. >> >> >> replicate Strain Day Treatment >> 1 B6 0 t1 >> 2 B6 0 t1 >> 3 B6 0 t1 >> 1 B6 14 t1 >> 2 B6 14 t1 >> 3 B6 14 t1 >> 1 B6 0 t2 >> 2 B6 0 t2 >> 3 B6 0 t2 >> 1 B6 14 t2 >> 2 B6 14 t2 >> 3 B6 14 t2 >> 1 Balbc 0 t1 >> 2 Balbc 0 t1 >> 3 Balbc 0 t1 >> 1 Balbc 14 t1 >> 2 Balbc 14 t1 >> 3 Balbc 14 t1 >> 1 Balbc 0 t2 >> 2 Balbc 0 t2 >> 3 Balbc 0 t2 >> 1 J129 0 t1 >> 2 J129 0 t1 >> 3 J129 0 t1 >> 1 J129 0 t2 >> 2 J129 0 t2 >> 3 J129 0 t2 >> 1 J129 14 t2 >> 2 J129 14 t2 >> 3 J129 14 t2 >> >> >> >> >> >> >> -Xiaokuan >> >> >> >> >> >> >> >> >> >> >> >> >> ----- Forwarded Message ---- > >> To: bioconductor <bioconductor at="" stat.math.ethz.ch=""> >> Sent: Wed, June 9, 2010 11:53:57 AM >> Subject: limma matrix desgin question? >> >> >> Dear List, >> >> I have an experiment trying to evaluate two treatment for mice. I have 3 normal strains, two treatment and two time points. >> The goal is to compare t2 vs t1 Day14 vs Day0. >> All these mice considered normal. So I can create factor such as day0_t1, day0_t2, day14_t1, and day14_t2. >> and make contrasts, such as day0_t2-day0_t1, day14_t2-day14_t1, day14_t2-day14_t1... >> >> But how can I include strain information into the comparison? Since each strain's data will be correlated? >> >> Thank you. >> >> Xiaokuan >> >> >> >> >> >> >> Strain Day Treatment >> B6 0 t1 >> B6 14 t1 >> B6 0 t2 >> B6 14 t2 >> Balbc 0 t1 >> Balbc 14 t1 >> Balbc 0 t2 >> J129 0 t1 >> J129 0 t2 >> J129 14 t2 >> >> >> >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> 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 > > > > [[alternative HTML version deleted]] > > _______________________________________________ > 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 REPLY 0 Entering edit mode Xiaokuan Wei ▴ 230 @xiaokuan-wei-4052 Last seen 6.9 years ago United States Hi Mark, You are right, it's a typo for my contrast.matrix. Thank you for your very helpful suggestions. -Xiaokuan ________________________________ From: Mark Cowley <m.cowley0@gmail.com> Cc: bioconductor <bioconductor@stat.math.ethz.ch> Sent: Wed, June 9, 2010 10:47:12 PM Subject: Re: [BioC] Repeat Measurement with Limma Fw: limma matrix desgin question? hi, you could still treat each strain x time x treatment combo as a separate group, ie 10 groups, and then use contrasts to compute the average 0_t1 effect over the 3 mice, eg: # i've reformatted you contrast matrix creation line -- i don't know why you've duplicated your contrasts here contrast.matrix<-makeContrasts( 0_t2-0_t1, 14_t2-14_t1, 14_t2-14_t1, 0_t2-0_t1 ) # alternative contrast.matrix<-makeContrasts( time0=(B6_0_t2+Balbc_0_t2+J129_0_t2)/3 - (B6_0_t1+Balbc_0_t1+J129_0_t1)/3, time14=(B6_14_t2+Balbc_14_t2+J129_14_t2)/3 - (B6_14_t1+Balbc_14_t1+J129_14_t1)/3 ) i've just noticed your design is unbalanced with no Balbc_14_t2 or J129_14_t1 groups. i'll leave it to you to adjust the relevant contrasts to be the average of 2 groups. cheers, Mark On 10/06/2010, at 12:06 PM, Xiaokuan Wei wrote: > Mark, > > Thank you for your prompt response. > In this experiment, I consider all these 3 strains are almost the same (wild type). And there are only 3 mice, each received different treatment at diffierent time. (Sorry, forgetting to mention this important info, even 0 t1 and 0 t2 are received at different time). > the repeated treatment to each mouse need to be included in the model. That's why I average the technical replicates and then used each mouse as block. > > I did try to use technical replicates as block. But I am not sure how to capture the repeated measurements. The data frame I have here is: > V1 V2 V3 V4 > 1 1 B6 0 t1 > 2 2 B6 0 t1 > 3 3 B6 0 t1 > 4 1 B6 14 t1 > 5 2 B6 14 t1 > 6 3 B6 14 t1 > 7 1 B6 0 t2 > 8 2 B6 0 t2 > 9 3 B6 0 t2 > 10 1 B6 14 t2 > 11 2 B6 14 t2 > 12 3 B6 14 t2 > 13 1 Balbc 0 t1 > 14 2 Balbc 0 t1 > 15 3 Balbc 0 t1 > 16 1 Balbc 14 t1 > 17 2 Balbc 14 t1 > 18 3 Balbc 14 t1 > 19 1 Balbc 0 t2 > 20 2 Balbc 0 t2 > 21 3 Balbc 0 t2 > 22 1 J129 0 t1 > 23 2 J129 0 t1 > 24 3 J129 0 t1 > 25 1 J129 0 t2 > 26 2 J129 0 t2 > 27 3 J129 0 t2 > 28 1 J129 14 t2 > 29 2 J129 14 t2 > 30 3 J129 14 t2 > > create factors (I only care time and treatment): > > f<-paste(df$V3,"_",df$V4,sep="") > > thus, we have factor: 0_t1,0_t2,14_t1,14_t2. > > create block using technical replecates: > block<-rep(1:10,each=3) > > then make the design: > design<-model.matrix(~ -1+factor(f, levels=unique(f)) > colnames(design)<-unique(f) > > biocor<-duplicateCorrelation(eset,desgin,block=block) > fit<-lmFit(eset,design=design,block=block,correlation=biocor$consens us) > > > contrast.matrix<-makeContrasts(0_t2-0_t1, 14_t2-14_t1, 14_t2-14_t1, 0_t2-0_t1) > > As you can see from the above analysis, I just treat each set experiment independently after considering technical replicates, and did not include repeated measurement effects for each mouse into the model. > The goal here is to compare the t2 and t1 at day0 and day14. These 3 mice are considered pathologically same even they have some different genetic background. > > Do you have any suggestion to include repeated measurement of each mouse into the model? > > Thank you very much. > > -Xiaokuan > > > > > > > > > > > > > > > > ________________________________ > From: Mark Cowley <m.cowley0@gmail.com> > > Cc: bioconductor <bioconductor@stat.math.ethz.ch> > Sent: Wed, June 9, 2010 8:23:09 PM > Subject: Re: [BioC] Repeat Measurement with Limma Fw: limma matrix desgin question? > > hi Xiaokuan, > i'd be treating the technical replicates as the block, and strain, day and treatment as the biological effects of interest > block <- rep(1:10, each=3) > that way limma has triple the number of arrays to use in its variance estimation > > you have a few options for your design matrix, such as strain + day + treatment + any interaction effects you're interested in > or you can simply treat these samples as 10 different groups and then setup contrasts to identify the effects of interest > > cheers, > mark > ----------------------------------------------------- > Mark Cowley, PhD > > Peter Wills Bioinformatics Centre > Garvan Institute of Medical Research, Sydney, Australia > ----------------------------------------------------- > > > On 10/06/2010, at 6:39 AM, Xiaokuan Wei wrote: > >> >> >> Dear List, >> >> I think I know how to do this with limma. >> I first calculate the correlation treating each strain as a block >> block<-rep(1:3,c(4,3,2)) >> biocor<-duplicateCorrelation(eset,design,block=block) >> then >> fit<-lmFit(eset,design=design,block=block,correlation=biocor$consen sus) >> then create contrast matrix and extract coef of each comparison. >> >> Is this right? >> >> However, I have a further question. In fact, each chip has 3 technical replicates. In order to simply the anlaysis, I just average the replicates and then use limma to do the job. >> How could I include such technical replicate information and repeat measurement information together with using Limma. Could Gordon or someone give me some hints or example? >> Thank you very much. >> >> >> replicate Strain Day Treatment >> 1 B6 0 t1 >> 2 B6 0 t1 >> 3 B6 0 t1 >> 1 B6 14 t1 >> 2 B6 14 t1 >> 3 B6 14 t1 >> 1 B6 0 t2 >> 2 B6 0 t2 >> 3 B6 0 t2 >> 1 B6 14 t2 >> 2 B6 14 t2 >> 3 B6 14 t2 >> 1 Balbc 0 t1 >> 2 Balbc 0 t1 >> 3 Balbc 0 t1 >> 1 Balbc 14 t1 >> 2 Balbc 14 t1 >> 3 Balbc 14 t1 >> 1 Balbc 0 t2 >> 2 Balbc 0 t2 >> 3 Balbc 0 t2 >> 1 J129 0 t1 >> 2 J129 0 t1 >> 3 J129 0 t1 >> 1 J129 0 t2 >> 2 J129 0 t2 >> 3 J129 0 t2 >> 1 J129 14 t2 >> 2 J129 14 t2 >> 3 J129 14 t2 >> >> >> >> >> >> >> -Xiaokuan >> >> >> >> >> >> >> >> >> >> >> >> >> ----- Forwarded Message ---- > >> To: bioconductor <bioconductor@stat.math.ethz.ch> >> Sent: Wed, June 9, 2010 11:53:57 AM >> Subject: limma matrix desgin question? >> >> >> Dear List, >> >> I have an experiment trying to evaluate two treatment for mice. I have 3 normal strains, two treatment and two time points. >> The goal is to compare t2 vs t1 Day14 vs Day0. >> All these mice considered normal. So I can create factor such as day0_t1, day0_t2, day14_t1, and day14_t2. >> and make contrasts, such as day0_t2-day0_t1, day14_t2-day14_t1, day14_t2-day14_t1... >> >> But how can I include strain information into the comparison? Since each strain's data will be correlated? >> >> Thank you. >> >> Xiaokuan >> >> >> >> >> >> >> Strain Day Treatment >> B6 0 t1 >> B6 14 t1 >> B6 0 t2 >> B6 14 t2 >> Balbc 0 t1 >> Balbc 14 t1 >> Balbc 0 t2 >> J129 0 t1 >> J129 0 t2 >> J129 14 t2 >> >> >> >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor [[alternative HTML version deleted]] ADD COMMENT 0 Entering edit mode Mark Cowley ▴ 400 @mark-cowley-2858 Last seen 7.7 years ago Australia hi Xiaokuan, i'd be treating the technical replicates as the block, and strain, day and treatment as the biological effects of interest block <- rep(1:10, each=3) that way limma has triple the number of arrays to use in its variance estimation you have a few options for your design matrix, such as strain + day + treatment + any interaction effects you're interested in or you can simply treat these samples as 10 different groups and then setup contrasts to identify the effects of interest cheers, mark ----------------------------------------------------- Mark Cowley, PhD Peter Wills Bioinformatics Centre Garvan Institute of Medical Research, Sydney, Australia ----------------------------------------------------- On 10/06/2010, at 6:39 AM, Xiaokuan Wei wrote: > > > Dear List, > > I think I know how to do this with limma. > I first calculate the correlation treating each strain as a block > block<-rep(1:3,c(4,3,2)) > biocor<-duplicateCorrelation(eset,design,block=block) > then > fit<-lmFit(eset,design=design,block=block,correlation=biocor$consens us) > then create contrast matrix and extract coef of each comparison. > > Is this right? > > However, I have a further question. In fact, each chip has 3 technical replicates. In order to simply the anlaysis, I just average the replicates and then use limma to do the job. > How could I include such technical replicate information and repeat measurement information together with using Limma. Could Gordon or someone give me some hints or example? > Thank you very much. > > > replicate Strain Day Treatment > 1 B6 0 t1 > 2 B6 0 t1 > 3 B6 0 t1 > 1 B6 14 t1 > 2 B6 14 t1 > 3 B6 14 t1 > 1 B6 0 t2 > 2 B6 0 t2 > 3 B6 0 t2 > 1 B6 14 t2 > 2 B6 14 t2 > 3 B6 14 t2 > 1 Balbc 0 t1 > 2 Balbc 0 t1 > 3 Balbc 0 t1 > 1 Balbc 14 t1 > 2 Balbc 14 t1 > 3 Balbc 14 t1 > 1 Balbc 0 t2 > 2 Balbc 0 t2 > 3 Balbc 0 t2 > 1 J129 0 t1 > 2 J129 0 t1 > 3 J129 0 t1 > 1 J129 0 t2 > 2 J129 0 t2 > 3 J129 0 t2 > 1 J129 14 t2 > 2 J129 14 t2 > 3 J129 14 t2 > > > > > > > -Xiaokuan > > > > > > > > > > > > > ----- Forwarded Message ---- > From: Xiaokuan Wei <weixiaokuan at="" yahoo.com=""> > To: bioconductor <bioconductor at="" stat.math.ethz.ch=""> > Sent: Wed, June 9, 2010 11:53:57 AM > Subject: limma matrix desgin question? > > > Dear List, > > I have an experiment trying to evaluate two treatment for mice. I have 3 normal strains, two treatment and two time points. > The goal is to compare t2 vs t1 Day14 vs Day0. > All these mice considered normal. So I can create factor such as day0_t1, day0_t2, day14_t1, and day14_t2. > and make contrasts, such as day0_t2-day0_t1, day14_t2-day14_t1, day14_t2-day14_t1... > > But how can I include strain information into the comparison? Since each strain's data will be correlated? > > Thank you. > > Xiaokuan > > > > > > > Strain Day Treatment > B6 0 t1 > B6 14 t1 > B6 0 t2 > B6 14 t2 > Balbc 0 t1 > Balbc 14 t1 > Balbc 0 t2 > J129 0 t1 > J129 0 t2 > J129 14 t2 > > > > > [[alternative HTML version deleted]] > > _______________________________________________ > 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