[BioC] Use of duplicateCorrelation

[Gordon K Smyth]

Dear Erika,

With a dye-effect and only 4 arrays you just don't have enough data to 
estimate the within-block correlation.  There's no right way around not 
having enough data.  I'd be inclined to simplify the model by dropping the 
blocking.  You can't have everything.

Best wishes
Gordon

On Mon, 26 Jan 2009, Erika Melissari wrote:

> However, I did not manage to fix the problem of duplicateCorrelation()
>
> When I define the design including the dye effect and I use this design in 
> duplicateCorrelation(), the calculated consensus correlation is NaN, where 
> without including the dye effect is
>
> -0.55 (I have a dye swap design).
>
> I have tryed to "unswap" the design also (as you suggested in a previous 
> message), but I have obtained the same result, that is NaN.
>
> I have used a different dataset with the same experimental design also and 
> the result was the same: NaN
>
> I have tryed to use the simple dye swap design for duplicateCorrelation 
> computation
>
>> design<-c(-1,1-1,1)
>
>> biolrep<-c(1,1,2,2)
>> corfit<-duplicateCorrelation(MA,design,ndups=1,block=biolrep)
>
> then I have defined a new design
>
>> design<-cbind(Dye=1,design)
>
> and I have used this together with the correlation previously calculated by 
> duplicateCorrelation (-0.55) for model computing
>
>> fit<-lmFit(MA,design, block=biolrep,cor=corfit$consensus,weights=NULL)
>
> Is this a right way of mouving aroud the problem?
>
> Any suggestion will be appreciated.
>
>
>
> Erika