multiple comparison adjustment of p values in LIMMA
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Shi, Tao ▴ 720
@shi-tao-199
Last seen 7.1 years ago
Dear Gordon and list, Could you please explain why the multiple comparison adjustment procedure implemented in limma (i.e. decideTests) only do adjustment across genes but not across treatment contrasts within a gene??? I found one of your earlier replies related to this here ( https://stat.ethz.ch/pipermail/bioconductor/2012-November/049385.html ): "Post hoc tests are done in limma using decideTests(), and many options are offered. You won't find classical methods like TukeyHSD though, because limma isn't doing classical Anova and because methods like TukeyHSD don't generalize well to high-dimensional datasets like microarrays." Could you please elaborate on this?? Take a 1000-gene data set with 3 treatment groups as an example, if you're doing all 3 pair-wise comparisons, it's 3000 tests.? One would think that controlling FDR for all 3000 tests is quite different from controlling FDR for 1000 tests per comparison.? If I only have a dataset with 50 genes, does this change your statement I cited earlier? Thank you very much! Tao
limma limma • 1.3k views
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@gordon-smyth
Last seen 4 minutes ago
WEHI, Melbourne, Australia
Dear Tao, Would you please consider reading the documentation for decideTests? If you did that, you would know that limma does in fact offer options for doing multiple comparison adjustments across contrasts within a gene as well across genes. I have also previously explained on this list why adjustments within a gene might not be needed when controlling FDR. Best wishes Gordon On Tue, 17 Sep 2013, Shi, Tao wrote: > Dear Gordon and list, > Could you please explain why the multiple comparison adjustment > procedure implemented in limma (i.e. decideTests) only do adjustment > across genes but not across treatment contrasts within a gene??? I found > one of your earlier replies related to this here ( > https://stat.ethz.ch/pipermail/bioconductor/2012-November/049385.html ): > "Post hoc tests are done in limma using decideTests(), and many options > are offered. You won't find classical methods like TukeyHSD though, > because limma isn't doing classical Anova and because methods like > TukeyHSD don't generalize well to high-dimensional datasets like > microarrays." > Could you please elaborate on this?? Take a 1000-gene data set with 3 > treatment groups as an example, if you're doing all 3 pair-wise > comparisons, it's 3000 tests.? One would think that controlling FDR for > all 3000 tests is quite different from controlling FDR for 1000 tests > per comparison.? If I only have a dataset with 50 genes, does this > change your statement I cited earlier? > Thank you very much! > Tao > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:5}}
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Dear Gordon, Thank you very much for your quick reply!? Somehow I have missed the glaring statements on this issue in the decideTests help file.? My bad! In both the help file and the user guide, you recommend to use method="global" when there are a few related contrasts.? However, from your 2nd statement below: "adjustments within a gene might not be needed when controlling FDR", are you suggesting to just use method="separate" instead?? I'm a bit confused.? I tried hard searching for your original post on this in BioC list, but didn't find it. Thanks! Tao ? ----- Original Message ----- From: Gordon K Smyth <smyth@wehi.edu.au> To: "Shi, Tao" <shidaxia at="" yahoo.com=""> Cc: "bioconductor at r-project.org" <bioconductor at="" r-project.org=""> Sent: Tuesday, September 17, 2013 5:16 PM Subject: Re: multiple comparison adjustment of p values in LIMMA Dear Tao, Would you please consider reading the documentation for decideTests?? If you did that, you would know that limma does in fact offer options for doing multiple comparison adjustments across contrasts within a gene as well across genes. I have also previously explained on this list why adjustments within a gene might not be needed when controlling FDR. Best wishes Gordon On Tue, 17 Sep 2013, Shi, Tao wrote: > Dear Gordon and list, > Could you please explain why the multiple comparison adjustment procedure implemented in limma (i.e. decideTests) only do adjustment across genes but not across treatment contrasts within a gene??? I found one of your earlier replies related to this here ( https://stat.ethz.ch/pipermail/bioconductor/2012-November/049385.html ): > "Post hoc tests are done in limma using decideTests(), and many options are offered.? You won't find classical methods like TukeyHSD though, because limma isn't doing classical Anova and because methods like TukeyHSD don't generalize well to high-dimensional datasets like microarrays." > Could you please elaborate on this?? Take a 1000-gene data set with 3 treatment groups as an example, if you're doing all 3 pair-wise comparisons, it's 3000 tests.? One would think that controlling FDR for all 3000 tests is quite different from controlling FDR for 1000 tests per comparison.? If I only have a dataset with 50 genes, does this change your statement I cited earlier? > Thank you very much! > Tao > ______________________________________________________________________ The information in this email is confidential and intended solely for the addressee. You must not disclose, forward, print or use it without the permission of the sender. ______________________________________________________________________
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Dear Tao, See this discussion: https://stat.ethz.ch/pipermail/bioconductor/2013-May/052666.html You could try both method="global" and method="separate". Best wishes Gordon On Tue, 17 Sep 2013, Shi, Tao wrote: > Dear Gordon, Thank you very much for your quick reply!? Somehow I have missed the glaring statements on this issue in the decideTests help file.? My bad! In both the help file and the user guide, you recommend to use method="global" when there are a few related contrasts.? However, from your 2nd statement below: "adjustments within a gene might not be needed when controlling FDR", are you suggesting to just use method="separate" instead?? I'm a bit confused.? I tried hard searching for your original post on this in BioC list, but didn't find it. Thanks! Tao ? ----- Original Message ----- From: Gordon K Smyth <smyth@wehi.edu.au> To: "Shi, Tao" <shidaxia at="" yahoo.com=""> Cc: "bioconductor at r-project.org" <bioconductor at="" r-project.org=""> Sent: Tuesday, September 17, 2013 5:16 PM Subject: Re: multiple comparison adjustment of p values in LIMMA Dear Tao, Would you please consider reading the documentation for decideTests?? If you did that, you would know that limma does in fact offer options for doing multiple comparison adjustments across contrasts within a gene as well across genes. I have also previously explained on this list why adjustments within a gene might not be needed when controlling FDR. Best wishes Gordon On Tue, 17 Sep 2013, Shi, Tao wrote: > Dear Gordon and list, > Could you please explain why the multiple comparison adjustment procedure implemented in limma (i.e. decideTests) only do adjustment across genes but not across treatment contrasts within a gene??? I found one of your earlier replies related to this here ( https://stat.ethz.ch/pipermail/bioconductor/2012-November/049385.html ): > "Post hoc tests are done in limma using decideTests(), and many options are offered.? You won't find classical methods like TukeyHSD though, because limma isn't doing classical Anova and because methods like TukeyHSD don't generalize well to high-dimensional datasets like microarrays." > Could you please elaborate on this?? Take a 1000-gene data set with 3 treatment groups as an example, if you're doing all 3 pair-wise comparisons, it's 3000 tests.? One would think that controlling FDR for all 3000 tests is quite different from controlling FDR for 1000 tests per comparison.? If I only have a dataset with 50 genes, does this change your statement I cited earlier? > Thank you very much! > Tao > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:11}}
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Dear Gordon, Thank you very much for the pointer! I have one more question.  Given the fact method="separate" can already properly control overall FDR for a few contrasts due to its scalability, what's the advantage of using method="global" besides the one you mentioned in the user guide: the same raw p value cutoff for different contrasts (but I guess one can argue against this too, right?)?  Plus, I'm reluctant to use method="global' as it treats all the tests equal, but in fact they're not (this is why you have the other two options): often time, the contrasts are highly correlated, e.g. in a longitudinal study. Best, Tao ________________________________ From: Gordon K Smyth <smyth@wehi.edu.au> Cc: "bioconductor@r-project.org" <bioconductor@r-project.org> Sent: Wednesday, September 18, 2013 12:32 AM Subject: Re: multiple comparison adjustment of p values in LIMMA Dear Tao, See this discussion: https://stat.ethz.ch/pipermail/bioconductor/2013-May/052666.html You could try both method="global" and method="separate". Best wishes Gordon On Tue, 17 Sep 2013, Shi, Tao wrote: > Dear Gordon, Thank you very much for your quick reply!  Somehow I have missed the [[elided Yahoo spam]] In both the help file and the user guide, you recommend to use method="global" when there are a few related contrasts.  However, from your 2nd statement below: "adjustments within a gene might not be needed when controlling FDR", are you suggesting to just use method="separate" instead?  I'm a bit confused.  I tried hard searching for your original post on this in BioC list, but didn't find it. Thanks! Tao ----- Original Message ----- From: Gordon K Smyth <smyth@wehi.edu.au> Cc: "bioconductor@r-project.org" <bioconductor@r-project.org> Sent: Tuesday, September 17, 2013 5:16 PM Subject: Re: multiple comparison adjustment of p values in LIMMA Dear Tao, Would you please consider reading the documentation for decideTests? If you did that, you would know that limma does in fact offer options for doing multiple comparison adjustments across contrasts within a gene as well across genes. I have also previously explained on this list why adjustments within a gene might not be needed when controlling FDR. Best wishes Gordon On Tue, 17 Sep 2013, Shi, Tao wrote: > Dear Gordon and list, > Could you please explain why the multiple comparison adjustment procedure implemented in limma (i.e. decideTests) only do adjustment across genes but not across treatment contrasts within a gene?   I found one of your earlier replies related to this here ( https://stat.ethz.ch/pipermail/bioconductor/2012-November/049385.html ): > "Post hoc tests are done in limma using decideTests(), and many options are offered.  You won't find classical methods like TukeyHSD though, because limma isn't doing classical Anova and because methods like TukeyHSD don't generalize well to high-dimensional datasets like microarrays." > Could you please elaborate on this?  Take a 1000-gene data set with 3 treatment groups as an example, if you're doing all 3 pair-wise comparisons, it's 3000 tests.  One would think that controlling FDR for all 3000 tests is quite different from controlling FDR for 1000 tests per comparison.  If I only have a dataset with 50 genes, does this change your statement I cited earlier? [[elided Yahoo spam]] > Tao > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:16}}
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