Hi everyone,
I am analyzing a Affymetrix microarray data set using the Limma
package. According to my experimental design, I understand that my
contrasts are closely related and that I should use the "global"
method for multiple testing. My question is: Is there a way to obtain
P-values using the "global" method for multiple testing? The
decideTest function allows to specify the method "global" but just
give the matrix of the results and not the P-values. And if I
understood correctly, the topTable function does not allow to specify
the method for multiple testing and uses the "separate" method as
default. Comparing the decideTest matrices, I realize that the results
of "separate" and "global" methods are slightly different and I am
concerned that this difference may change the final interpretation of
my study.
Thank you very much.
Best regards
Eduardo Ribeiro
PhD student
University of Florida
[[alternative HTML version deleted]]
If fit is the fitted model object that you used for decidedTests(),
then
Global.Adjusted.P <- fit$p.value
Global.Adjusted.P[] <- p.adjust(Global.Adjusted.P, method="BH")
Gordon
> Date: Sat, 21 Jul 2012 02:02:46 +0000
> From: "Ribeiro,Eduardo de Souza" <ribeiro.es at="" ufl.edu="">
> To: "bioconductor at r-project.org" <bioconductor at="" r-project.org="">
> Subject: [BioC] Limma: how to obtain P values using method "global"
> for multiple testing
>
> Hi everyone,
>
> I am analyzing a Affymetrix microarray data set using the Limma
package.
> According to my experimental design, I understand that my contrasts
are
> closely related and that I should use the "global" method for
multiple
> testing. My question is: Is there a way to obtain P-values using the
> "global" method for multiple testing? The decideTest function allows
to
> specify the method "global" but just give the matrix of the results
and
> not the P-values. And if I understood correctly, the topTable
function
> does not allow to specify the method for multiple testing and uses
the
> "separate" method as default. Comparing the decideTest matrices, I
> realize that the results of "separate" and "global" methods are
slightly
> different and I am concerned that this difference may change the
final
> interpretation of my study.
>
> Thank you very much.
>
> Best regards
>
> Eduardo Ribeiro
> PhD student
> University of Florida
______________________________________________________________________
The information in this email is confidential and
intend...{{dropped:4}}
Thank you very much Dr. Smyth. It worked fine for my model.
Eduardo de Souza Ribeiro
PhD student
University of Florida
________________________________________
From: Gordon K Smyth [smyth@wehi.EDU.AU]
Sent: Saturday, July 21, 2012 8:21 PM
To: Ribeiro,Eduardo de Souza
Cc: Bioconductor mailing list
Subject: Limma: how to obtain P values using method "global" for
multiple testing
If fit is the fitted model object that you used for decidedTests(),
then
Global.Adjusted.P <- fit$p.value
Global.Adjusted.P[] <- p.adjust(Global.Adjusted.P, method="BH")
Gordon
> Date: Sat, 21 Jul 2012 02:02:46 +0000
> From: "Ribeiro,Eduardo de Souza" <ribeiro.es at="" ufl.edu="">
> To: "bioconductor at r-project.org" <bioconductor at="" r-project.org="">
> Subject: [BioC] Limma: how to obtain P values using method "global"
> for multiple testing
>
> Hi everyone,
>
> I am analyzing a Affymetrix microarray data set using the Limma
package.
> According to my experimental design, I understand that my contrasts
are
> closely related and that I should use the "global" method for
multiple
> testing. My question is: Is there a way to obtain P-values using the
> "global" method for multiple testing? The decideTest function allows
to
> specify the method "global" but just give the matrix of the results
and
> not the P-values. And if I understood correctly, the topTable
function
> does not allow to specify the method for multiple testing and uses
the
> "separate" method as default. Comparing the decideTest matrices, I
> realize that the results of "separate" and "global" methods are
slightly
> different and I am concerned that this difference may change the
final
> interpretation of my study.
>
> Thank you very much.
>
> Best regards
>
> Eduardo Ribeiro
> PhD student
> University of Florida
______________________________________________________________________
The information in this email is confidential and
intend...{{dropped:6}}
