Subject: Re: Normalization of arrays where most of the genes change
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Last seen 6.9 years ago
Hi Yiwen, In cases where you know the subset of genes that are not changin you can use limma as outlined in: Cheers, Alicia Message-ID: <c2a9eb528d6c3d44ad457c54ad0c7cd545a85c49 at=""""> Sean, Thanks for getting back to me so quickly! I took a look at the VSN package and some threads in the mailing archive, it looks like the right tool to try. However, I am just wondering, if there are people fitting LOESS on a subset of unchanged genes and any comment on that. Thanks in advance! Yiwen From: Davis, Sean (NCI) Sent: Wednesday, May 13, 2009 9:01 AM To: He, Yiwen (NIH/CIT) [C] Cc: bioconductor at Subject: Re: [BioC] Normalization of arrays where most of the genes change On Wed, May 13, 2009 at 8:54 AM, He, Yiwen (NIH/CIT) [C] <heyiwen at=""<mailto:heyiwen="" at="""">> wrote: Hi, We have some arrays where most of the genes are turned on under certain conditions. This violates the assumption that most normalization methods make. What would be the best way to handle such arrays? We are using Agilent arrays but as I understand the platform should not matter. I'm wondering, if LOESS or Quntile normalization can be used on a (small) subset of invariable genes and expand to the whole array? If so, is there such a tool in BioC? Thank you very much! Hi, Yiwen. There has been some discussion on the list before (you might check the archives), but vsn might be a reasonable place to start. Sean [[alternative HTML version deleted]]
Normalization vsn limma Normalization vsn limma • 478 views

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