[BioC] normalization for custom chip

M Inmaculada Barrasa ibarrasa at mail.med.upenn.edu
Mon Nov 15 17:18:48 CET 2004

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


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.
>_-----Original Message-----
>_From: bioconductor-bounces at stat.math.ethz.ch
>_[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Deng, Shibing
>_Sent: Thursday, November 11, 2004 10:08 PM
>_To: 'bioconductor at stat.math.ethz.ch'
>_Subject: [BioC] normalization for custom chip
>_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.
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