Normalization for use of individual channels from 2-color arrays?
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Hari Easwaran ▴ 240
@hari-easwaran-3510
Last seen 8.8 years ago
United States
Dear BioC gurus, I am trying to use the individual channels from a 2-color array as a measure of gene expression level following the scripts in the Limma User Guide. The code the guide suggests is: MA <- normalizeBetweenArrays(MA, method="Aquantile") Throughout the guide, MA is an objected created by normalizing the RG values [for example using: MA <- normalizeWithinArrays(RG, method="loess")]. So for the command above, are the A values obtained from a 1st step of normalization (like loess in this case) re-normalized using Aquantile? Since the idea of using Aquantile is so that the log ratios are not changed but the samples are adjusted to have the same distribution of A values, my concern is that by using a 1st step of normalization the log-ratios do not remain untouched. Does the guide mean to do the following: MA <- normalizeBetweenArrays(RG, method="Aquantile") where RG is the raw data. I would really appreciate any input. Sincerely, Hari Easwaran (Johns Hopkins University) [[alternative HTML version deleted]]
Normalization limma Normalization limma • 1.1k views
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@sean-davis-490
Last seen 12 weeks ago
United States
On Tue, Sep 13, 2011 at 10:35 AM, Hari Easwaran <hariharan.pe at="" gmail.com=""> wrote: > Dear BioC gurus, > > I am trying to use the individual channels from a 2-color array as a measure > of gene expression level following the scripts in the Limma User Guide. > > The code the guide suggests is: > > MA <- normalizeBetweenArrays(MA, method="Aquantile") > > Throughout the guide, MA is an objected created by normalizing the RG values > [for example using: MA <- normalizeWithinArrays(RG, method="loess")]. > So for the command above, are the A values obtained from a 1st step of > normalization (like loess in this case) re-normalized using Aquantile? > > Since the idea of using Aquantile is so that the log ratios are not changed > but the samples are adjusted to have the same distribution of A values, my > concern is that by using a 1st step of normalization the log-ratios do not > remain untouched. Does the guide mean to do the following: > > MA <- normalizeBetweenArrays(RG, method="Aquantile") > > where RG is the raw data. Hi, Hari. normalizeBetweenArrays is often used AFTER normalizeWithinArrays for two-color arrays, so an MAList is often used as the object for normalizeBetweenArrays. Per the help page, the Aquantile method does leave the M-values unchanged. Sean
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Hi Sean, So it is fine to have the log ratios normalized before the data is subject to Aquantile normalization? Thanks for your help. Hari On Tue, Sep 13, 2011 at 10:43 AM, Sean Davis <sdavis2@mail.nih.gov> wrote: > On Tue, Sep 13, 2011 at 10:35 AM, Hari Easwaran <hariharan.pe@gmail.com> > wrote: > > Dear BioC gurus, > > > > I am trying to use the individual channels from a 2-color array as a > measure > > of gene expression level following the scripts in the Limma User Guide. > > > > The code the guide suggests is: > > > > MA <- normalizeBetweenArrays(MA, method="Aquantile") > > > > Throughout the guide, MA is an objected created by normalizing the RG > values > > [for example using: MA <- normalizeWithinArrays(RG, method="loess")]. > > So for the command above, are the A values obtained from a 1st step of > > normalization (like loess in this case) re-normalized using Aquantile? > > > > Since the idea of using Aquantile is so that the log ratios are not > changed > > but the samples are adjusted to have the same distribution of A values, > my > > concern is that by using a 1st step of normalization the log- ratios do > not > > remain untouched. Does the guide mean to do the following: > > > > MA <- normalizeBetweenArrays(RG, method="Aquantile") > > > > where RG is the raw data. > > Hi, Hari. > > normalizeBetweenArrays is often used AFTER normalizeWithinArrays for > two-color arrays, so an MAList is often used as the object for > normalizeBetweenArrays. Per the help page, the Aquantile method does > leave the M-values unchanged. > > Sean > [[alternative HTML version deleted]]
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You would normally be using aquantile if you are doing single-channel analysis. Otherwise, I'm not sure why you would use aquantile. Sean On Sep 13, 2011 10:59 AM, "Hari Easwaran" <hariharan.pe@gmail.com> wrote: > Hi Sean, > So it is fine to have the log ratios normalized before the data is subject > to Aquantile normalization? > > Thanks for your help. > > Hari > > > On Tue, Sep 13, 2011 at 10:43 AM, Sean Davis <sdavis2@mail.nih.gov> wrote: > >> On Tue, Sep 13, 2011 at 10:35 AM, Hari Easwaran <hariharan.pe@gmail.com> >> wrote: >> > Dear BioC gurus, >> > >> > I am trying to use the individual channels from a 2-color array as a >> measure >> > of gene expression level following the scripts in the Limma User Guide. >> > >> > The code the guide suggests is: >> > >> > MA <- normalizeBetweenArrays(MA, method="Aquantile") >> > >> > Throughout the guide, MA is an objected created by normalizing the RG >> values >> > [for example using: MA <- normalizeWithinArrays(RG, method="loess")]. >> > So for the command above, are the A values obtained from a 1st step of >> > normalization (like loess in this case) re-normalized using Aquantile? >> > >> > Since the idea of using Aquantile is so that the log ratios are not >> changed >> > but the samples are adjusted to have the same distribution of A values, >> my >> > concern is that by using a 1st step of normalization the log- ratios do >> not >> > remain untouched. Does the guide mean to do the following: >> > >> > MA <- normalizeBetweenArrays(RG, method="Aquantile") >> > >> > where RG is the raw data. >> >> Hi, Hari. >> >> normalizeBetweenArrays is often used AFTER normalizeWithinArrays for >> two-color arrays, so an MAList is often used as the object for >> normalizeBetweenArrays. Per the help page, the Aquantile method does >> leave the M-values unchanged. >> >> Sean >> > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor [[alternative HTML version deleted]]
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