Removing genes from linear model fit advice sought
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@stephen-sefick-3929
Last seen 7.1 years ago
I have done this with all of the genes in a microarray experiment including blanks, negative controls, empties, hk genes, and spike ins, genes of interest 1. RG <- backgroundCorrect(RG, method="normexp", offset=50) 2. MA <- maNorm(as(RG, "marrayRaw"), norm="twoD") 3. WA <- normalizeBetweenArrays(as(MA, "MAList"), method="scale") This seems sensible to me. Is it? I am now thinking that I should remove everything except for the know differentially expressed genes and the genes of interest before fitting the linear model, contrasts, bayesian smotthing. Is this a sensible coarse of action? Thanks for all of your help in advance. kind regards, -- Stephen Sefick Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is puff us up and make us feel like gods. We are mammals, and have not exhausted the annoying little problems of being mammals. -K. Mullis
Microarray Bayesian Microarray Bayesian • 583 views
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Yong Li ▴ 190
@yong-li-3321
Last seen 7.1 years ago
Dear Stephen, I think a previous post by Gordon Smyth talking about filtering should answer most of your questions. You can find it at: https://stat.ethz.ch/pipermail/bioconductor/2009-January/025827.html Hope it helps. Yong stephen sefick wrote: > I have done this with all of the genes in a microarray experiment > including blanks, negative controls, empties, hk genes, and spike ins, > genes of interest > 1. RG <- backgroundCorrect(RG, method="normexp", offset=50) > > 2. MA <- maNorm(as(RG, "marrayRaw"), norm="twoD") > > 3. WA <- normalizeBetweenArrays(as(MA, "MAList"), method="scale") > > This seems sensible to me. Is it? > > I am now thinking that I should remove everything except for the know > differentially expressed genes and the genes of interest before > fitting the linear model, contrasts, bayesian smotthing. Is this a > sensible coarse of action? Thanks for all of your help in advance. > kind regards, >
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Thank you so much for all of your help. That did the trick. On Thu, Feb 11, 2010 at 3:00 AM, Yong Li <yong.li at="" zbsa.uni-="" freiburg.de=""> wrote: > Dear Stephen, > > I think a previous post by Gordon Smyth talking about filtering should > answer most of your questions. You can find it at: > > https://stat.ethz.ch/pipermail/bioconductor/2009-January/025827.html > > Hope it helps. > Yong > > stephen sefick wrote: >> >> I have done this with all of the genes in a microarray experiment >> including blanks, negative controls, empties, hk genes, and spike ins, >> genes of interest >> 1. RG <- backgroundCorrect(RG, method="normexp", offset=50) >> >> 2. MA <- maNorm(as(RG, "marrayRaw"), norm="twoD") >> >> 3. WA <- normalizeBetweenArrays(as(MA, "MAList"), method="scale") >> >> This seems sensible to me. ?Is it? >> >> I am now thinking that I should remove everything except for the know >> differentially expressed genes and the genes of interest before >> fitting the linear model, contrasts, bayesian smotthing. ?Is this a >> sensible coarse of action? ?Thanks for all of your help in advance. >> kind regards, >> > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > -- Stephen Sefick Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is puff us up and make us feel like gods. We are mammals, and have not exhausted the annoying little problems of being mammals. -K. Mullis
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