Hi Gordon thanks for the many good points. Literature: I don't have an overview over uses or citations of vsn, and benchmarking normalisation methods is as we know a complex topic, but below I paste some recent references where vsn was used for multiple (dozens - hundred) single colour arrays. Affine linear: vsn transforms each array's data x with the transformation glog(a*x+b) with array specific parameters a and b, and an overall (same for all arrays) function glog(y)=log2( (y+sqrt(y^2+1) /2). The array specific part is affine linear. Cyclic loess is an iterative algorithm (as is vsn), and its implementation in limma by default stops after 3 iterations regardless of whether convergence was reached. While I concur that this is numerically robust, isn't the lack of data-dependent convergence diagnostic a reason to worry? I also have a question to you: there is nothing intrinsically bivariate (1D regressor, 1D response) about local regression, multivariate approaches have been proposed (e.g. Keppler, Crosby, Morgan in Genome Biology 2002), and good implementations exist in R (e.g. locfit package), why is it so popular to do this pair-wise (with the obvious drawback of n^2 complexity)? Best wishes Wolfgang Zhenyu Xu*, Wu Wei*, Julien Gagneur, Fabiana Perocchi, Sandra Clauder- Muenster, Jurgi Camblong, Elisa Guffanti, Francoise Stutz, Wolfgang Huber, and Lars M. Steinmetz. Bidirectional promoters generate pervasive transcription in yeast. Nature, 457(7232):1033-1037, 2009. Zhenyu Xu*, Wu Wei*, Julien Gagneur*, Sandra Clauder-M?nster, Miosz Smolik, Wolfgang Huber, and Lars M. Steinmetz. Antisense expression increases gene expression variability and locus interdependency. Molecular Systems Biology, 7, 2011. E. Benito, L. M. Valor, M. Jimenez-Minchan, W. Huber, and A. Barco. cAMP response element-binding protein is a primary hub of activity- driven neuronal gene expression. Journal of Neuroscience, 31:18237-18250, 2011. Ramona Schmid, Patrick Baum, Carina Ittrich, Katrin Fundel-Clemens, Wolfgang Huber, Benedikt Brors, Roland Eils, Andreas Weith, Detlev Mennerich, and Karsten Quast. Comparison of normalization methods for Illumina BeadChip(R) HumanHT-12 v3. BMC Genomics, 11:349, 2010. Il giorno Nov 4, 2012, alle ore 1:05 AM, Gordon K Smyth <smyth at="" wehi.edu.au=""> ha scritto: > 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 >>> >>> -- > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:6}}