Q-value question
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Guido Leoni ▴ 200
@guido-leoni-3328
Last seen 9.4 years ago
European Union
Dear List I'm new to microarrays analysis. I'm analyzing an experiment with 2 condtions and 3 microarrays for condition. My data are succesfully normalized and I have a list of gene modulated with aFC, a raw p-value and a q-value. My q-value are never lower than 0,1. If I well understand my q-value is the minimum false discovery rate for which my FC are statistically significant. So have I set a threshold similar to p-value to select my differentially regulated genes? if yes (what does mean the fact that my q-value is never lower than 0,1?) Sorry for my confused question and Thank you for every tips you could give to me -- Guido Leoni National Research Institute on Food and Nutrition (I.N.R.A.N.) via Ardeatina 546 00178 Rome Italy tel + 39 06 51 49 41 (operator) + 39 06 51 49 4519 (direct) [[alternative HTML version deleted]]
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@james-w-macdonald-5106
Last seen 3 hours ago
United States
Hi Guido, Guido Leoni wrote: > Dear List > I'm new to microarrays analysis. I'm analyzing an experiment with 2 > condtions and 3 microarrays for condition. My data are succesfully > normalized and I have a list of gene modulated with aFC, a raw p-value and a > q-value. My q-value are never lower than 0,1. If I well understand my > q-value is the minimum false discovery rate for which my FC are > statistically significant. So have I set a threshold similar to p-value to > select my differentially regulated genes? if yes (what does mean the fact > that my q-value is never lower than 0,1?) > Sorry for my confused question and > Thank you for every tips you could give to me The FDR is an estimate of the maximum percentage of 'significant' results that are false positives. So for instance, if you accept all the genes (reporters) with a q-value =< 0.1, then the assumption is that somewhere around 10% of those are false positives. So when you go to validate those genes (if you use that FDR level), you can expect about 10% or so to fail to validate. This of course assumes that all the assumptions have been met blah blah statistics statistics statistics... ;-D Best, Jim > -- James W. MacDonald, M.S. Biostatistician Douglas Lab University of Michigan Department of Human Genetics 5912 Buhl 1241 E. Catherine St. Ann Arbor MI 48109-5618 734-615-7826 ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues
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Dear James, Guido and Bioconductor helpers, I always thought, q-values control pFDR, which is similar to FDR almost all the time but theoretically not similar. Please correct me if I am wrong. Thanks and Best Regards, Sanvesh On Wed, Feb 3, 2010 at 5:14 PM, James W. MacDonald <jmacdon@med.umich.edu>wrote: > Hi Guido, > > > Guido Leoni wrote: > >> Dear List >> I'm new to microarrays analysis. I'm analyzing an experiment with 2 >> condtions and 3 microarrays for condition. My data are succesfully >> normalized and I have a list of gene modulated with aFC, a raw p-value and >> a >> q-value. My q-value are never lower than 0,1. If I well understand my >> q-value is the minimum false discovery rate for which my FC are >> statistically significant. So have I set a threshold similar to p-value to >> select my differentially regulated genes? if yes (what does mean the fact >> that my q-value is never lower than 0,1?) >> Sorry for my confused question and >> Thank you for every tips you could give to me >> > > The FDR is an estimate of the maximum percentage of 'significant' results > that are false positives. So for instance, if you accept all the genes > (reporters) with a q-value =< 0.1, then the assumption is that somewhere > around 10% of those are false positives. > > So when you go to validate those genes (if you use that FDR level), you can > expect about 10% or so to fail to validate. This of course assumes that all > the assumptions have been met blah blah statistics statistics statistics... > ;-D > > Best, > > Jim > > > >> > -- > James W. MacDonald, M.S. > Biostatistician > Douglas Lab > University of Michigan > Department of Human Genetics > 5912 Buhl > 1241 E. Catherine St. > Ann Arbor MI 48109-5618 > 734-615-7826 > ********************************************************** > Electronic Mail is not secure, may not be read every day, and should not be > used for urgent or sensitive issues > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > 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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