Dear Paola, I'm not sure why you say there's a problem. duplicateCorrelation() has no difficulty with technical and biological replicates in the same experiment. You might analysis your experiment by: targets$Treat <- factor(targets$Treat) design <- model.matrix(~Treat,data=targets) dupcor <- duplicateCorrelation(y,design,block=targets$Repl) fit <- lmFit(design,block=targets$Repl,correlation=dupcor$consensus) fit <- eBayes(fit) topTable(fit,coef=2) Best wishes Gordon > Date: Tue, 8 May 2012 16:02:12 +0200 > From: Paola Sgado <sgado at="" science.unitn.it=""> > To: bioc-devel at r-project.org > Subject: [Bioc-devel] technical and biological replicates in the same Exprset - Agi4x44 > > HI all, > I'm having some problem with microarray analysis. I am a biologist not > very good with R neither with statistics! > I'm using Agilent 4x44 arrays and the Agi4x44Processed package. I have > basically to compare WT vs KO data. The microarray was done first with 3 > true biological replicates and later with 4 technical replicates with a > pool of RNAs. > My design is the following: >> targets > FileName Treat GErep Subject Array Repl. > 549_1_4.txt KO 2 genotype 1 KO1 > 550_1_4.txt KO 2 genotype 2 KO2 > 551_1_4.txt KO 2 genotype 3 KO3 > 549_1_3.txt WT 1 genotype 1 WT1 > 550_1_3.txt WT 1 genotype 2 WT2 > 551_1_3.txt WT 1 genotype 3 WT3 > 385_1_1.txt WT 3 genotype 4 WT4 > 385_1_2.txt KO 4 genotype 4 KO4 > 385_1_3.txt WT 3 genotype 4 WT4 > 385_1_4.txt KO 4 genotype 4 KO4 > 386_1_2.txt WT 3 genotype 5 WT4 > 386_1_3.txt KO 4 genotype 5 KO4 > 386_1_4.txt WT 3 genotype 5 WT4 > I performed normalization and filtering with the entire set of arrays, > but when I started the statistical analysis using ebayes with limma I > realized I could not treat biological (WT1,2,3-KO1,2,3) and technical > replicates (WT4-KO4) the same way. > I tried to use the dupcor function, but it does not work with tech and > biol replicates in the same analysis. Is there a way to bypass the > problem? > Thanks for your help, I really cannot find the way out.... > Cheers > Paola ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}

Dear Gordon, thank you for your reply. I immediately tried the analysis you suggested but it doesn't seem to work: > targets$Treat <- factor(targets$Treat) > design <- model.matrix(~Treat,data=targets) > dupcor <- duplicateCorrelation(esetPROC,design,block=targets$Repl) #esetPROC is my expression set from Agi4x44Processed There were 50 or more warnings (use warnings() to see the first 50) > warnings() Warning messages: 1: In glmgam.fit(dx, dy, start = start, tol = tol, maxit = maxit, ... : Too much damping - convergence tolerance not achievable 2: In glmgam.fit(dx, dy, start = start, tol = tol, maxit = maxit, ... : Too much damping - convergence tolerance not achievable 3: In glmgam.fit(dx, dy, start = start, tol = tol, maxit = maxit, ... : Too much damping - convergence tolerance not achievable 4: In glmgam.fit(dx, dy, start = start, tol = tol, maxit = maxit, ... : Too much damping - convergence tolerance not achievable 5: In glmgam.fit(dx, dy, start = start, tol = tol, maxit = maxit, ... : Too much damping - convergence tolerance not achievable The warnings are all the same. I also tried to continue... > fit <- lmFit(design,block=targets$Repl,correlation=dupcor$consensus) Error in gls.series(y$exprs, design = design, ndups = ndups, spacing = spacing, : Length of block does not match number of arrays > targets$Repl [1] KO1 KO2 KO3 WT1 WT2 WT3 WT4 KO4 WT4 KO4 WT4 KO4 WT4 Levels: KO1 KO2 KO3 KO4 WT1 WT2 WT3 WT4 What did I do wrong? Thanks guys for your help, I really appreciate it! Cheers Paola On May 10, 2012, at 04:03 AM, Gordon K Smyth wrote: > Dear Paola, > > I'm not sure why you say there's a problem. duplicateCorrelation() has no difficulty with technical and biological replicates in the same experiment. > > You might analysis your experiment by: > > targets$Treat <- factor(targets$Treat) > design <- model.matrix(~Treat,data=targets) > dupcor <- duplicateCorrelation(y,design,block=targets$Repl) > fit <- lmFit(design,block=targets$Repl,correlation=dupcor$consensus) > fit <- eBayes(fit) > topTable(fit,coef=2) > > Best wishes > Gordon > >> Date: Tue, 8 May 2012 16:02:12 +0200 >> From: Paola Sgado <sgado@science.unitn.it> >> To: bioc-devel@r-project.org >> Subject: [Bioc-devel] technical and biological replicates in the same Exprset - Agi4x44 >> >> HI all, > >> I'm having some problem with microarray analysis. I am a biologist not very good with R neither with statistics! > >> I'm using Agilent 4x44 arrays and the Agi4x44Processed package. I have basically to compare WT vs KO data. The microarray was done first with 3 true biological replicates and later with 4 technical replicates with a pool of RNAs. > >> My design is the following: >>> targets >> FileName Treat GErep Subject Array Repl. >> 549_1_4.txt KO 2 genotype 1 KO1 >> 550_1_4.txt KO 2 genotype 2 KO2 >> 551_1_4.txt KO 2 genotype 3 KO3 >> 549_1_3.txt WT 1 genotype 1 WT1 >> 550_1_3.txt WT 1 genotype 2 WT2 >> 551_1_3.txt WT 1 genotype 3 WT3 >> 385_1_1.txt WT 3 genotype 4 WT4 >> 385_1_2.txt KO 4 genotype 4 KO4 >> 385_1_3.txt WT 3 genotype 4 WT4 >> 385_1_4.txt KO 4 genotype 4 KO4 >> 386_1_2.txt WT 3 genotype 5 WT4 >> 386_1_3.txt KO 4 genotype 5 KO4 >> 386_1_4.txt WT 3 genotype 5 WT4 > >> I performed normalization and filtering with the entire set of arrays, but when I started the statistical analysis using ebayes with limma I realized I could not treat biological (WT1,2,3-KO1,2,3) and technical replicates (WT4-KO4) the same way. > >> I tried to use the dupcor function, but it does not work with tech and biol replicates in the same analysis. Is there a way to bypass the problem? > >> Thanks for your help, I really cannot find the way out.... > >> Cheers >> Paola > > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:27}}