[BioC] Missing Values after cyclic loess in limma

[Gordon K Smyth]

The problem had nothing to do with the loess function.

I do not know of any objective grounds by which one could claim vsn to be 
more numerically robust than loess.  The former requires iterative 
parameter estimation whereas loess is a closed-form calculation requiring 
nothing more complex than linear regression.

The literature does indeed suggest that cyclic loess would an obvious 
choice in high DE situations, which is the context here.  There is no 
literature than I know of supporting vsn in this context.

Affine functions are linear transformations with an intercept.  Vsn is not 
a linear transformation while, ironically, the local polynomials used by 
loess are.

Gordon

> Date: Fri, 2 Nov 2012 19:45:45 +0100
> From: Wolfgang Huber <whuber at embl.de>
> To: "Claus Mayer [guest]" <guest at bioconductor.org>
> Cc: bioconductor at r-project.org
> Subject: Re: [BioC] Missing Values after cyclic loess in limma
>
> Hi Claus
>
> if there is a chance that affine functions might already do a good 
> enough job for you, compared to loess' local polynomials, then "vsn" 
> might be an option for you, which is intended to be more numerically 
> robust.
>
> 	Best wishes
> 	Wolfgang
>
> Il giorno Nov 2, 2012, alle ore 6:45 PM, "Claus Mayer [guest]" <guest at bioconductor.org> ha scritto:
>
>>
>> Hello,
>>
>> I am just working on my first ever single channel Agilent array data 
>> set. Because I do expect large changes in differential expression I 
>> wanted to use the cyclic loess normalisation within limma rather than 
>> quantile normalisation. I used the default settings i.e.
>>
>> y<-normalizeBetweenArrays(x,method="cyclicloess")
>>
>> where x is the ELlistRaw object. As expected this took a while but to 
>> my surprise produced hundreds of missing values for each array as 
>> indicated by the message
>>
>> Warning message:
>> In log2(Recall(object$E, method = method, ...)) : NaNs produced
>>
>> I checked the raw values which are all well above 0 and include no NAs. 
>> I also did not use any background correction, so I don't quite 
>> understand why logging should produce any missing values. I had assumed 
>> that the method would first log and then apply the cyclic loess 
>> algorithm, which in itself shouldn't produce any NAs either. Have I 
>> misunderstood something basic here?
>>
>> Thanks,
>>
>> Claus
>>
>>
>>
>> -- output of sessionInfo():
>>
>> R version 2.13.0 (2011-04-13)
>> Platform: i386-pc-mingw32/i386 (32-bit)
>>
>> other attached packages:
>> [1] limma_3.8.2
>>
>> --

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