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At 06:43 AM 9/06/2004, Naomi Altman wrote:
>The problem remains, but I have added a few lines of code that were
>missing in the original posting.
>
>I have just run limma and am getting p-values after eBayes that are
>smaller than the p-values before, leading to 100% of my genes being
>declared significant at any value of FDR you care to use.
It seems very surprising to get 100% of genes significant, but nothing
in
the output that you give below suggests that anything is wrong. It all
seems as it should be. You should tend to get smaller p-values after
eBayes
than before because the degrees of freedom increase, but not uniformly
so
because many of the residual standard deviations also increase.
>The design is a 1-way ANOVA with 6 treatments and 2 reps/treatment
(which
>I know is not great but ...)
>
>I thought that the denominator adjustment would make the posterior
>sigma^2 > unadjusted MSE, but this is not the case.
Empirical Bayes methods, like all shrinkage methods, shrink estimators
towards a common value. This means that some values will go up, and
some
will go down. The help page says that eBayes() "uses an empirical
Bayes
method to shrink the gene-wise sample variances towards a common
value". What is happening is that the precisions (the inverse sample
variances) are being set to their posterior means. You can see the
complete, pretty simple, formula by following the URL for the
reference
given on the help page.
Gordon
> Here are the commands I used to fit the model and do the ebayes
> adjustment.
>
>design=model.matrix(~-1+factor(c(1,1,2,2,3,3,4,4,5,5,6,6)))
>colnames(design)=c("trt1","trt2","trt3","trt4","trt5","trt6")
>
>fitRMA=lmFit(RMAdata,design)
>
>contrast.matrix
>
> c1 c2 c3 c4 c5
>trt1 1 1 1 1 1
>trt2 -1 1 1 1 1
>trt3 0 -2 1 1 1
>trt4 0 0 -3 1 1
>trt5 0 0 0 -5 1
>trt6 0 0 0 0 -5
>
>fitCont=contrasts.fit(fitRMA,contrast.matrix)
>fitAdj=eBayes(fitCont)
>
>ls.print(lsfit(fitRMA$sigma^2,fitAdj$s2.post))
>Residual Standard Error=0
>R-Square=1
>F-statistic (df=1, 22744)=1.632754e+35
>p-value=0
>
> Estimate Std.Err t-value Pr(>|t|)
>Intercept 0.0093 0 1.026963e+17 0
>X 0.5628 0 4.040735e+17 0
>
>mean(fitAdj$s2.post)
>[1] 0.02991697
>
>mean(fitRMA$sigma^2)
>[1] 0.03656270
>
>fitAdj$s2.prior
>[1] 0.02136298
>
>
>Naomi S. Altman 814-865-3791 (voice)
>Associate Professor
>Bioinformatics Consulting Center
>Dept. of Statistics 814-863-7114 (fax)
>Penn State University 814-865-1348
(Statistics)
>University Park, PA 16802-2111
0
Entering edit mode
Excuse me if I missed something here, but should not
>
>contrast.matrix
>
> c1 c2 c3 c4 c5
>trt1 1 1 1 1 1
>trt2 -1 1 1 1 1
>trt3 0 -2 1 1 1
>trt4 0 0 -3 1 1
>trt5 0 0 0 -5 1
>trt6 0 0 0 0 -5
(Note '-5' in c4)
Be
contrast.matrix
c1 c2 c3 c4 c5
trt1 1 1 1 1 1
trt2 -1 1 1 1 1
trt3 0 -2 1 1 1
trt4 0 0 -3 1 1
trt5 0 0 0 -4 1
trt6 0 0 0 0 -5
Is this merely a typo in your email or is this the source of the
problem?
Chuck
On Wed, 9 Jun 2004, Gordon Smyth wrote:
> At 06:43 AM 9/06/2004, Naomi Altman wrote:
> >The problem remains, but I have added a few lines of code that were
> >missing in the original posting.
> >
> >I have just run limma and am getting p-values after eBayes that are
> >smaller than the p-values before, leading to 100% of my genes being
> >declared significant at any value of FDR you care to use.
>
> It seems very surprising to get 100% of genes significant, but
nothing in
> the output that you give below suggests that anything is wrong. It
all
> seems as it should be. You should tend to get smaller p-values after
eBayes
> than before because the degrees of freedom increase, but not
uniformly so
> because many of the residual standard deviations also increase.
>
> >The design is a 1-way ANOVA with 6 treatments and 2 reps/treatment
(which
> >I know is not great but ...)
> >
> >I thought that the denominator adjustment would make the posterior
> >sigma^2 > unadjusted MSE, but this is not the case.
>
> Empirical Bayes methods, like all shrinkage methods, shrink
estimators
> towards a common value. This means that some values will go up, and
some
> will go down. The help page says that eBayes() "uses an empirical
Bayes
> method to shrink the gene-wise sample variances towards a
common
> value". What is happening is that the precisions (the inverse sample
> variances) are being set to their posterior means. You can see the
> complete, pretty simple, formula by following the URL for the
reference
> given on the help page.
>
> Gordon
>
> > Here are the commands I used to fit the model and do the ebayes
> > adjustment.
> >
> >design=model.matrix(~-1+factor(c(1,1,2,2,3,3,4,4,5,5,6,6)))
> >colnames(design)=c("trt1","trt2","trt3","trt4","trt5","trt6")
> >
> >fitRMA=lmFit(RMAdata,design)
> >
> >contrast.matrix
> >
> > c1 c2 c3 c4 c5
> >trt1 1 1 1 1 1
> >trt2 -1 1 1 1 1
> >trt3 0 -2 1 1 1
> >trt4 0 0 -3 1 1
> >trt5 0 0 0 -5 1
> >trt6 0 0 0 0 -5
> >
> >fitCont=contrasts.fit(fitRMA,contrast.matrix)
> >fitAdj=eBayes(fitCont)
> >
> >ls.print(lsfit(fitRMA$sigma^2,fitAdj$s2.post))
> >Residual Standard Error=0
> >R-Square=1
> >F-statistic (df=1, 22744)=1.632754e+35
> >p-value=0
> >
> > Estimate Std.Err t-value Pr(>|t|)
> >Intercept 0.0093 0 1.026963e+17 0
> >X 0.5628 0 4.040735e+17 0
> >
> >mean(fitAdj$s2.post)
> >[1] 0.02991697
> >
> >mean(fitRMA$sigma^2)
> >[1] 0.03656270
> >
> >fitAdj$s2.prior
> >[1] 0.02136298
> >
> >
> >Naomi S. Altman 814-865-3791 (voice)
> >Associate Professor
> >Bioinformatics Consulting Center
> >Dept. of Statistics 814-863-7114 (fax)
> >Penn State University 814-865-1348
(Statistics)
> >University Park, PA 16802-2111
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor@stat.math.ethz.ch
> https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>
Charles C. Berry (858) 534-2098
Dept of Family/Preventive
Medicine
E mailto:cberry@tajo.ucsd.edu UC San Diego
http://hacuna.ucsd.edu/members/ccb.html La Jolla, San Diego
92093-0717
ADD REPLY
• link
20.5 years ago
Charles Berry
▴
290
0
Entering edit mode
Thanks, Chuck.
I noticed this after my first set of e-mails and fixed it.
It was the source of the original problem (variance inflation instead
of
variance stabilization) but not the 2nd problem - 97% of the genes
appear
to differentially express (FDR=.01) and that percentage is HIGHER than
the
percentage of genes with ebayes or ordinary p-value less than .01.
--Naomi
At 06:14 PM 6/9/2004 +0000, you wrote:
>Excuse me if I missed something here, but should not
>
> >
> >contrast.matrix
> >
> > c1 c2 c3 c4 c5
> >trt1 1 1 1 1 1
> >trt2 -1 1 1 1 1
> >trt3 0 -2 1 1 1
> >trt4 0 0 -3 1 1
> >trt5 0 0 0 -5 1
> >trt6 0 0 0 0 -5
>
>(Note '-5' in c4)
>
>Be
>
>contrast.matrix
>
> c1 c2 c3 c4 c5
>trt1 1 1 1 1 1
>trt2 -1 1 1 1 1
>trt3 0 -2 1 1 1
>trt4 0 0 -3 1 1
>trt5 0 0 0 -4 1
>trt6 0 0 0 0 -5
>
>Is this merely a typo in your email or is this the source of the
>problem?
>
>Chuck
>
>On Wed, 9 Jun 2004, Gordon Smyth wrote:
>
> > At 06:43 AM 9/06/2004, Naomi Altman wrote:
> > >The problem remains, but I have added a few lines of code that
were
> > >missing in the original posting.
> > >
> > >I have just run limma and am getting p-values after eBayes that
are
> > >smaller than the p-values before, leading to 100% of my genes
being
> > >declared significant at any value of FDR you care to use.
> >
> > It seems very surprising to get 100% of genes significant, but
nothing in
> > the output that you give below suggests that anything is wrong. It
all
> > seems as it should be. You should tend to get smaller p-values
after
> eBayes
> > than before because the degrees of freedom increase, but not
uniformly so
> > because many of the residual standard deviations also increase.
> >
> > >The design is a 1-way ANOVA with 6 treatments and 2
reps/treatment (which
> > >I know is not great but ...)
> > >
> > >I thought that the denominator adjustment would make the
posterior
> > >sigma^2 > unadjusted MSE, but this is not the case.
> >
> > Empirical Bayes methods, like all shrinkage methods, shrink
estimators
> > towards a common value. This means that some values will go up,
and some
> > will go down. The help page says that eBayes() "uses an empirical
Bayes
> > method to shrink the gene-wise sample variances towards a
common
> > value". What is happening is that the precisions (the inverse
sample
> > variances) are being set to their posterior means. You can see the
> > complete, pretty simple, formula by following the URL for the
reference
> > given on the help page.
> >
> > Gordon
> >
> > > Here are the commands I used to fit the model and do the
ebayes
> > > adjustment.
> > >
> > >design=model.matrix(~-1+factor(c(1,1,2,2,3,3,4,4,5,5,6,6)))
> > >colnames(design)=c("trt1","trt2","trt3","trt4","trt5","trt6")
> > >
> > >fitRMA=lmFit(RMAdata,design)
> > >
> > >contrast.matrix
> > >
> > > c1 c2 c3 c4 c5
> > >trt1 1 1 1 1 1
> > >trt2 -1 1 1 1 1
> > >trt3 0 -2 1 1 1
> > >trt4 0 0 -3 1 1
> > >trt5 0 0 0 -5 1
> > >trt6 0 0 0 0 -5
> > >
> > >fitCont=contrasts.fit(fitRMA,contrast.matrix)
> > >fitAdj=eBayes(fitCont)
> > >
> > >ls.print(lsfit(fitRMA$sigma^2,fitAdj$s2.post))
> > >Residual Standard Error=0
> > >R-Square=1
> > >F-statistic (df=1, 22744)=1.632754e+35
> > >p-value=0
> > >
> > > Estimate Std.Err t-value Pr(>|t|)
> > >Intercept 0.0093 0 1.026963e+17 0
> > >X 0.5628 0 4.040735e+17 0
> > >
> > >mean(fitAdj$s2.post)
> > >[1] 0.02991697
> > >
> > >mean(fitRMA$sigma^2)
> > >[1] 0.03656270
> > >
> > >fitAdj$s2.prior
> > >[1] 0.02136298
> > >
> > >
> > >Naomi S. Altman 814-865-3791
(voice)
> > >Associate Professor
> > >Bioinformatics Consulting Center
> > >Dept. of Statistics 814-863-7114
(fax)
> > >Penn State University 814-865-1348
(Statistics)
> > >University Park, PA 16802-2111
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor@stat.math.ethz.ch
> > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
> >
>
>Charles C. Berry (858) 534-2098
> Dept of Family/Preventive
Medicine
>E mailto:cberry@tajo.ucsd.edu UC San Diego
>http://hacuna.ucsd.edu/members/ccb.html La Jolla, San Diego
92093-0717
>
>
Naomi S. Altman 814-865-3791 (voice)
Associate Professor
Bioinformatics Consulting Center
Dept. of Statistics 814-863-7114 (fax)
Penn State University 814-865-1348
(Statistics)
University Park, PA 16802-2111
ADD REPLY
• link
20.5 years ago
Naomi Altman
★
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