[BioC] technical and biological replicates in the same Exprset - Agi4x44

[Gordon K Smyth]

Dear Paola,

A warning is not necessarily a problem.

The call to lmFit should be:

fit <- lmFit(esetPROC, design,block=targets$Repl,correlation=dupcor$consensus)

Have you looked at the value of dupcor$consensus, to check that is a 
reasonable value?

Best wishes
Gordon

---------------------------------------------
Professor Gordon K Smyth,
Bioinformatics Division,
Walter and Eliza Hall Institute of Medical Research,
1G Royal Parade, Parkville, Vic 3052, Australia.
Tel: (03) 9345 2326, Fax (03) 9347 0852,
smyth at wehi.edu.au
http://www.wehi.edu.au
http://www.statsci.org/smyth

On Thu, 24 May 2012, Paola Sgadò wrote:

> 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 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
>>
>>
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>
> --
> Paola Sgadò, PhD
> COFUND-Marie Curie Research Fellow
>
> Laboratory of Molecular Neuropathology, Centre for Integrative Biology (CIBIO), University of Trento
> Via delle Regole 101, 38060 Mattarello, Trento (TN)
> phone (office)	+39-0461-282746
> fax     			+39-0461-283937
> email: sgado at science.unitn.it
>
>
>
>
>
>
>
>

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