[BioC] Nonorthogonal multiple comparisons in Limma/ Books on Bayesian Statistics

Richard Friedman friedman at cancercenter.columbia.edu
Fri Aug 6 15:36:22 CEST 2004


	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 

Best wishes,

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 Unpublished method though.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
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 at cancercenter.columbia.edu

In Memoriam, Francis Crick

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