Search
Question: limma: paired + multiple comparisons + technical replication?
0
gravatar for Gordon Smyth
4.1 years ago by
Gordon Smyth32k
Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
Gordon Smyth32k wrote:
Dear Sarah, Just put Pairs in the design matrix, for example model.matrix(~Groups+Pairs) and use duplicateCorrelation() for the biolreps. Best wishes Gordon > Date: Tue, 1 Oct 2013 17:55:34 +0200 > From: Sarah Bonnin <sarah.bonnin at="" crg.eu=""> > To: "bioconductor at r-project.org" <bioconductor at="" r-project.org=""> > Subject: [BioC] limma: paired + multiple comparisons + technical > replication ? > > Dear list, > > This question might be a bit redundant and I apologize for it, if it is, > but I can't find a clear answer to what I'm trying to do. > > I am working on a set of 12 expression one-channel arrays. > > My target file is basically as follows: > FileName Pairs Groups > Sample1 1 Group1 > Sample2 1 Group1 > Sample3 1 Group2 > Sample4 1 Group2 > Sample5 1 Group3 > Sample6 1 Group3 > Sample7 2 Group1 > Sample8 2 Group2 > Sample9 2 Group3 > Sample10 3 Group1 > Sample11 3 Group2 > Sample12 3 Group3 > > There are several parameters to take into account: > > - I want to produce all possible pairwise comparisons (Group3-Group2, > Group2-Group1, Group3-Group1): "Groups" column > > - I want my design to take into account the paired samples: "Pairs" > column > > - The last thing is that some samples are technical replicates (Sample1 > with Sample2, Sample3 with Sample4, Sample5 with Sample6) and I would > also like to take this into account. > > I've read the "8.7 Multi-level experiments" chapter of limma user guide, which guided me into combining paired data and multiple comparisons, in which case I would do: >> design <- model.matrix(~0+factor(targets$Groups)) >> colnames(design) <- unique(targets$Groups) >> corfit <- duplicateCorrelation(eset,design,block=targets$Pairs) >> fit <- lmFit(eset,design,block=targets$Pairs,correlation=corfit$consensus) > > In theory to take into account technical replicates I would use: >> biolrep <- c(1,1,2,2,3,3,4,5,6,7,8,9) >> corfit <- duplicateCorrelation(eset, block = biolrep) >> fit <- lmFit(eset, block = biolrep, cor = corfit$consensus) > > But how can I combine all of this? > > Is there a way to somehow pass both paired and technical replication > information into the duplicateCorrelation step? Or should I modify the > design instead to take into account the paired design? > > It is getting quite confusing for me. > > Any help greatly appreciated! > > Thanks in advance! > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
ADD COMMENTlink modified 4.1 years ago by sbcn50 • written 4.1 years ago by Gordon Smyth32k
0
gravatar for sbcn
4.1 years ago by
sbcn50
Spain
sbcn50 wrote:
Dear Gordon, Thank you for your answer. The only thing is that I also want to compare all groups (1 to 1, all possibilities), and use the "makeContrasts" command - so as to automate better - and I'm not sure if my way of doing it is correct! Here is my code, which actually works now, but I was wondering if it was really taking into account pairs as it should. targets (a bit more informative than the previous one; TR is technical replicate) FileName Groups Pairs Sample1 Control 1 Sample1_TR Control 1 Sample2 Control 2 Sample3 Control 3 Sample4 Control 4 Sample5 Treat1 1 Sample5_TR Treat1 1 Sample6 Treat1 2 Sample7 Treat1 3 Sample8 Treat1 4 Sample9 Treat2 1 Sample9_TR Treat2 1 Sample10 Treat2 2 Sample11 Treat2 3 Sample12 Treat2 4 techrep <- c(1,1,2,3,4,5,5,6,7,8,9,9,10,11,12) # technical replication info design <- model.matrix(~0+targets$Groups+targets$Pairs) colnames(design) <- c(unique(targets$Groups),"Pairs") corfit <- duplicateCorrelation(xx, design, ndups = 1, block = techrep) fit <- lmFit(xx, design, block = techrep, cor = corfit$consensus) contrast.matrix <- makeContrasts(contrasts=c("Treat1-Control", "Treat2-Control", "Treat2-Treat1"), levels=design) fit <- contrasts.fit(fit, contrast.matrix) fit <- eBayes(fit) Thank you! Sarah -----Original Message----- From: Gordon K Smyth [mailto:smyth@wehi.EDU.AU] Sent: Thursday, October 03, 2013 1:04 AM To: Sarah Bonnin Cc: Bioconductor mailing list Subject: limma: paired + multiple comparisons + technical replication? Dear Sarah, Just put Pairs in the design matrix, for example model.matrix(~Groups+Pairs) and use duplicateCorrelation() for the biolreps. Best wishes Gordon > Date: Tue, 1 Oct 2013 17:55:34 +0200 > From: Sarah Bonnin <sarah.bonnin at="" crg.eu=""> > To: "bioconductor at r-project.org" <bioconductor at="" r-project.org=""> > Subject: [BioC] limma: paired + multiple comparisons + technical > replication ? > > Dear list, > > This question might be a bit redundant and I apologize for it, if it > is, but I can't find a clear answer to what I'm trying to do. > > I am working on a set of 12 expression one-channel arrays. > > My target file is basically as follows: > FileName Pairs Groups > Sample1 1 Group1 > Sample2 1 Group1 > Sample3 1 Group2 > Sample4 1 Group2 > Sample5 1 Group3 > Sample6 1 Group3 > Sample7 2 Group1 > Sample8 2 Group2 > Sample9 2 Group3 > Sample10 3 Group1 > Sample11 3 Group2 > Sample12 3 Group3 > > There are several parameters to take into account: > > - I want to produce all possible pairwise comparisons (Group3-Group2, > Group2-Group1, Group3-Group1): "Groups" column > > - I want my design to take into account the paired samples: "Pairs" > column > > - The last thing is that some samples are technical replicates > (Sample1 with Sample2, Sample3 with Sample4, Sample5 with Sample6) and > I would also like to take this into account. > > I've read the "8.7 Multi-level experiments" chapter of limma user guide, which guided me into combining paired data and multiple comparisons, in which case I would do: >> design <- model.matrix(~0+factor(targets$Groups)) >> colnames(design) <- unique(targets$Groups) corfit <- >> duplicateCorrelation(eset,design,block=targets$Pairs) >> fit <- >> lmFit(eset,design,block=targets$Pairs,correlation=corfit$consensus) > > In theory to take into account technical replicates I would use: >> biolrep <- c(1,1,2,2,3,3,4,5,6,7,8,9) corfit <- >> duplicateCorrelation(eset, block = biolrep) fit <- lmFit(eset, block >> = biolrep, cor = corfit$consensus) > > But how can I combine all of this? > > Is there a way to somehow pass both paired and technical replication > information into the duplicateCorrelation step? Or should I modify the > design instead to take into account the paired design? > > It is getting quite confusing for me. > > Any help greatly appreciated! > > Thanks in advance! > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:6}}
ADD COMMENTlink written 4.1 years ago by sbcn50
On Thu, 3 Oct 2013, Sarah Bonnin wrote: > Dear Gordon, > > Thank you for your answer. > > The only thing is that I also want to compare all groups (1 to 1, all > possibilities), and use the "makeContrasts" command - so as to automate > better - and I'm not sure if my way of doing it is correct! > > Here is my code, which actually works now, but I was wondering if it was > really taking into account pairs as it should. Yes. Gordon > targets (a bit more informative than the previous one; TR is technical replicate) > FileName Groups Pairs > Sample1 Control 1 > Sample1_TR Control 1 > Sample2 Control 2 > Sample3 Control 3 > Sample4 Control 4 > Sample5 Treat1 1 > Sample5_TR Treat1 1 > Sample6 Treat1 2 > Sample7 Treat1 3 > Sample8 Treat1 4 > Sample9 Treat2 1 > Sample9_TR Treat2 1 > Sample10 Treat2 2 > Sample11 Treat2 3 > Sample12 Treat2 4 > > techrep <- c(1,1,2,3,4,5,5,6,7,8,9,9,10,11,12) # technical replication info > > design <- model.matrix(~0+targets$Groups+targets$Pairs) > colnames(design) <- c(unique(targets$Groups),"Pairs") > > corfit <- duplicateCorrelation(xx, design, ndups = 1, block = techrep) > fit <- lmFit(xx, design, block = techrep, cor = corfit$consensus) > > contrast.matrix <- makeContrasts(contrasts=c("Treat1-Control", "Treat2-Control", "Treat2-Treat1"), levels=design) > fit <- contrasts.fit(fit, contrast.matrix) > fit <- eBayes(fit) > > > Thank you! > > Sarah > > > > > -----Original Message----- > From: Gordon K Smyth [mailto:smyth at wehi.EDU.AU] > Sent: Thursday, October 03, 2013 1:04 AM > To: Sarah Bonnin > Cc: Bioconductor mailing list > Subject: limma: paired + multiple comparisons + technical replication? > > Dear Sarah, > > Just put Pairs in the design matrix, for example > > model.matrix(~Groups+Pairs) > > and use duplicateCorrelation() for the biolreps. > > Best wishes > Gordon > >> Date: Tue, 1 Oct 2013 17:55:34 +0200 >> From: Sarah Bonnin <sarah.bonnin at="" crg.eu=""> >> To: "bioconductor at r-project.org" <bioconductor at="" r-project.org=""> >> Subject: [BioC] limma: paired + multiple comparisons + technical >> replication ? >> >> Dear list, >> >> This question might be a bit redundant and I apologize for it, if it >> is, but I can't find a clear answer to what I'm trying to do. >> >> I am working on a set of 12 expression one-channel arrays. >> >> My target file is basically as follows: >> FileName Pairs Groups >> Sample1 1 Group1 >> Sample2 1 Group1 >> Sample3 1 Group2 >> Sample4 1 Group2 >> Sample5 1 Group3 >> Sample6 1 Group3 >> Sample7 2 Group1 >> Sample8 2 Group2 >> Sample9 2 Group3 >> Sample10 3 Group1 >> Sample11 3 Group2 >> Sample12 3 Group3 >> >> There are several parameters to take into account: >> >> - I want to produce all possible pairwise comparisons (Group3-Group2, >> Group2-Group1, Group3-Group1): "Groups" column >> >> - I want my design to take into account the paired samples: "Pairs" >> column >> >> - The last thing is that some samples are technical replicates >> (Sample1 with Sample2, Sample3 with Sample4, Sample5 with Sample6) and >> I would also like to take this into account. >> >> I've read the "8.7 Multi-level experiments" chapter of limma user guide, which guided me into combining paired data and multiple comparisons, in which case I would do: >>> design <- model.matrix(~0+factor(targets$Groups)) >>> colnames(design) <- unique(targets$Groups) corfit <- >>> duplicateCorrelation(eset,design,block=targets$Pairs) >>> fit <- >>> lmFit(eset,design,block=targets$Pairs,correlation=corfit$consensus) >> >> In theory to take into account technical replicates I would use: >>> biolrep <- c(1,1,2,2,3,3,4,5,6,7,8,9) corfit <- >>> duplicateCorrelation(eset, block = biolrep) fit <- lmFit(eset, block >>> = biolrep, cor = corfit$consensus) >> >> But how can I combine all of this? >> >> Is there a way to somehow pass both paired and technical replication >> information into the duplicateCorrelation step? Or should I modify the >> design instead to take into account the paired design? >> >> It is getting quite confusing for me. >> >> Any help greatly appreciated! >> >> Thanks in advance! ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
ADD REPLYlink written 4.1 years ago by Gordon Smyth32k
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 2.2.0
Traffic: 199 users visited in the last hour