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}}

