At 03:12 AM 6/08/2004, Richard Friedman wrote:
>Fellow Expressionists,
>
> Does Limma automatically perform a multiple
>comparison adjustment for non-orthogonal contrasts?
Yes. classifyTestsF() is a classification which takes full account of
non-orthogonality. Unpublished method though. Also "holm" and some
other
options are valid even across non-orthogonal contrats.
> If not, can you recommend another program that can
>be used in conjunction with Limma to do this?
>
> Also, I find the Bayesian theory in the paper
>by Gordon Smyth ("Linear Models and Empirical
>Bayes Methods..." tough going) Can anyone
>please recommend a book or books on Bayesian methods
>that can bridge the gap between basic statistics texts
>(e.g. Hoel, "An Introduction to Mathematical Statistics" and
>Zar "Biostatistical Analysis" and this article)?
I don't know of any accessible refs, even for stat majors I'm afraid.
And
unfortunately Bayes refs may not be much help. The empirical Bayes
arithmetic requires different quantities to be computed compared to
full Bayes.
I hope that the final formula in the paper make intuitive sense even
though
the math derivation might be hard.
Gordon
>Thanks and best wishes,
>Rich
Gordon,
Thank you for answering my questions. The last equation in
your paper
makes intuitive sense to me.
I'm wondering if you can take the time to answer two more
questions:
1. Say I have the following case:
Level A (3 replicates)
Level B (2 replicates)
Level C(1 replicate)
Level D(1 replicate)
Can I legitimately calculate a P value for the contrast Level A to
level C in the linear model even though I have only one replicate on
Level C. I am not talking about just Limma here. I am talking about
the
linear model in general. Also,
I realize that one replicate is poor experimental design. This is what
I was given to analyze.
2. If I wished to apply a multiple test correction to the pvalues from
non-orthogonal contrasts, would the following procedure be
legitimate::
1. Generate a pvalue for each contrast in the set of
nonothogonal
contrasts for each gene using classifyTestsF().
2. Correct the pvalues using a multiple test correction such
as FDR.
I realize that no multiple-test correction is entirely satisfactory, I
just want to get an approximate estimate of the p-values for each
contrast as a guide to further experimentation and literature
searching.
Best wishes,
Rich
On Aug 5, 2004, at 9:26 PM, Gordon Smyth wrote:
> At 03:12 AM 6/08/2004, Richard Friedman wrote:
>> Fellow Expressionists,
>>
>> Does Limma automatically perform a multiple
>> comparison adjustment for non-orthogonal contrasts?
>
> Yes. classifyTestsF() is a classification which takes full account
of
> non-orthogonality. Unpublished method though. Also "holm" and some
> other options are valid even across non-orthogonal contrats.
>
>> If not, can you recommend another program that can
>> be used in conjunction with Limma to do this?
>>
>> Also, I find the Bayesian theory in the paper
>> by Gordon Smyth ("Linear Models and Empirical
>> Bayes Methods..." tough going) Can anyone
>> please recommend a book or books on Bayesian methods
>> that can bridge the gap between basic statistics texts
>> (e.g. Hoel, "An Introduction to Mathematical Statistics" and
>> Zar "Biostatistical Analysis" and this article)?
>
> I don't know of any accessible refs, even for stat majors I'm
afraid.
> And unfortunately Bayes refs may not be much help. The empirical
Bayes
> arithmetic requires different quantities to be computed compared to
> full Bayes.
>
> I hope that the final formula in the paper make intuitive sense even
> though the math derivation might be hard.
>
> Gordon
>
>> Thanks and best wishes,
>> Rich
>
>
------------------------------------------------------------
Richard A. Friedman, PhD
Associate Research Scientist
Herbert Irving Comprehensive Cancer Center
Oncoinformatics Core
Lecturer
Department of Biomedical Informatics
Box 95, Room 130BB or P&S 1-420C
Columbia University Medical Center
630 W. 168th St.
New York, NY 10032
(212)305-6901 (5-6901) (voice)
friedman@cancercenter.columbia.edu
http://cancercenter.columbia.edu/~friedman/
In Memoriam, Francis Crick
At 11:36 PM 6/08/2004, Richard Friedman wrote:
>Gordon,
>
> Thank you for answering my questions. The last equation in
your
> paper makes intuitive sense to me.
>
> I'm wondering if you can take the time to answer two more
questions:
>
>1. Say I have the following case:
>
>Level A (3 replicates)
>Level B (2 replicates)
>Level C(1 replicate)
>Level D(1 replicate)
>
>Can I legitimately calculate a P value for the contrast Level A to
level C
>in the linear model even though I have only one replicate on Level C.
I am
>not talking about just Limma here. I am talking about the linear
model in
>general.
Given assumption of common variance across levels, yes.
> Also,
>I realize that one replicate is poor experimental design. This is
what I
>was given to analyze.
>
>2. If I wished to apply a multiple test correction to the pvalues
from
>non-orthogonal contrasts, would the following procedure be
legitimate::
>
> 1. Generate a pvalue for each contrast in the set of
nonothogonal
> contrasts for each gene using classifyTestsF().
> 2. Correct the pvalues using a multiple test correction such
as FDR.
Nothing special about this design. All usual things, e.g. in limma,
apply.
Gordon
>I realize that no multiple-test correction is entirely satisfactory,
I
>just want to get an approximate estimate of the p-values for each
contrast
>as a guide to further experimentation and literature searching.
>
>Best wishes,
>Rich
Gordon,
I now believe that I understand your answer. In order to do
adjust for
both multiple comparisons and
multiple tests I use classifyTestsF() with method="fdr". If I
understand the documentation correctly, the fdr part
refers to the multiple test correction across genes on top of the
multiple comparison adjustment across levels performed
if no method were to be specified.
Do I have it straight?
Thanks and best wishes,
Rich
On Aug 6, 2004, at 9:57 AM, Gordon Smyth wrote:
> At 11:36 PM 6/08/2004, Richard Friedman wrote:
>> Gordon,
>>
>> Thank you for answering my questions. The last equation in
>> your paper makes intuitive sense to me.
>>
>> I'm wondering if you can take the time to answer two more
>> questions:
>>
>> 1. Say I have the following case:
>>
>> Level A (3 replicates)
>> Level B (2 replicates)
>> Level C(1 replicate)
>> Level D(1 replicate)
>>
>> Can I legitimately calculate a P value for the contrast Level A to
>> level C in the linear model even though I have only one replicate
on
>> Level C. I am not talking about just Limma here. I am talking about
>> the linear model in general.
>
> Given assumption of common variance across levels, yes.
>
>> Also,
>> I realize that one replicate is poor experimental design. This is
>> what I was given to analyze.
>>
>> 2. If I wished to apply a multiple test correction to the pvalues
>> from non-orthogonal contrasts, would the following procedure be
>> legitimate::
>>
>> 1. Generate a pvalue for each contrast in the set of
>> nonothogonal contrasts for each gene using classifyTestsF().
>> 2. Correct the pvalues using a multiple test correction
such
>> as FDR.
>
> Nothing special about this design. All usual things, e.g. in limma,
> apply.
>
> Gordon
>
>> I realize that no multiple-test correction is entirely
satisfactory,
>> I just want to get an approximate estimate of the p-values for each
>> contrast as a guide to further experimentation and literature
>> searching.
>>
>> Best wishes,
>> Rich
>
>
------------------------------------------------------------
Richard A. Friedman, PhD
Associate Research Scientist
Herbert Irving Comprehensive Cancer Center
Oncoinformatics Core
Lecturer
Department of Biomedical Informatics
Box 95, Room 130BB or P&S 1-420C
Columbia University Medical Center
630 W. 168th St.
New York, NY 10032
(212)305-6901 (5-6901) (voice)
friedman@cancercenter.columbia.edu
http://cancercenter.columbia.edu/~friedman/
In Memoriam, Francis Crick