Hi Inma, you can check the list of released packages on the Bioconductor webpage, and have a look at the package called vsn. In there, you'll also find a vignette and some literature references. library(reposTools) install.packages2("vsn", develOK=TRUE) library("vsn") openVignette("vsn") Best wishes Wolfgang ------------------------------------- Wolfgang Huber European Bioinformatics Institute European Molecular Biology Laboratory Wellcome Trust Genome Campus Cambridge CB10 1SD England Phone: +44 1223 494642 Http: www.dkfz.de/abt0840/whuber ------------------------------------- M Inmaculada Barrasa wrote: > Hi Hinnerk, > > I am also tryying to normalize a custom chip. > Could you please tell me what VSN stands for and where can I find that > method for normalization and a reference for it? > > Thanks a lot > > Inma > > > > > > On Fri, 12 Nov 2004, Hinnerk Boriss wrote: > > >>_Dear Shibing, >>_ >>_I do not recommend using house keeping genes for normalization. In several >>_experiments they turn out being differentially expressed. A better approach >>_would be to use a normalization method that searches for an invariant set of >>_genes in the sample. "VSN" and Li & Wong's "invariant set" do that. The >>_methods have limits though regarding the minimum proportion of not >>_differentially expressed genes. Below 30% things become difficult. Another >>_aspect you should be aware of is that a bias in the treatment effect, i.e. >>_treatment causes either mostly up- or down-regulation of genes, will distort >>_your normalization. VSN is most robust against this bias. >>_ >>_Just an idea for you chip design: make a list of all genes that you think >>_could react to the planned treatment for 70-80% of your probe sets, then >>_take a random sample from all the remaining genes (of which you have no >>_prior evidence for differential expression) to design the remaining 20-30% >>_of the chip. This should get you a way out your normalization problem >>_typical for custom chips. In fact, you could restrict the invariant set >>_algorithm to search only in the random selection of genes. >>_ >>_Cheers, >>_Hinnerk >>_ >>_ >>_-----Original Message----- >>_From: bioconductor-bounces@stat.math.ethz.ch >>_[mailto:bioconductor-bounces@stat.math.ethz.ch] On Behalf Of Deng, Shibing >>_Sent: Thursday, November 11, 2004 10:08 PM >>_To: 'bioconductor@stat.math.ethz.ch' >>_Subject: [BioC] normalization for custom chip >>_ >>_Hi, >>_We are designing a custom Affymetrix chip with about 1700 genes. By design, >>_a large number of genes on the chip will be differentially expressed between >>_our treatment and control samples. The assumption for quantile normalization >>_and other distribution-based normalization methods will not hold for these >>_chips. To normalize them, we plan to put some "house-keeping" genes or >>_"invariant" genes covering a wide range of intensities on the chip. We are >>_not sure how many house-keeping genes we should have to get a good >>_normalization? I will appreciate your input on this issue. >>_ >>_Shibing