[BioC] Limma vs Maanova, and use of covariates

Ingunn Berget ingunn.berget at umb.no
Fri Dec 9 08:10:46 CET 2005

Thanks to both Naomi and and JP for informative answers.

----- Original Message ----- 
From: "Juan Pedro Steibel" <steibelj at msu.edu>
To: "Ingunn Berget" <ingunn.berget at umb.no>
Cc: <bioconductor at stat.math.ethz.ch>
Sent: Thursday, December 08, 2005 3:51 PM
Subject: Re: [BioC] Limma vs Maanova, and use of covariates

> The difference between maanova and limma  goes beyond what you mention.
> It is true that limma originally analized log-ratios and maanova fits 
> intensity models. However, the main difference is that limma resorts to an 
> empirical bayes procedure to assess significance in differential 
> expression. Maanova, on the other hand, fits a gene by gene model on the 
> intensities and allows to include fixed and random effects. For testing 
> purposes, maanova provides parametric tests (assuming normality) or 
> permutation based tests.
> The ratio versus intensity dicotomy is not the important thin in this 
> case, for it can be shown that a gene-by-gene mixed model can be fit for 
> ratios or intensities and still obtain the same result (the only 
> restriction is that the array effect in the intensity model should be a 
> fixed effect). Also, the limma package may fit intensity models in some 
> cases (see Ch. 9 of limma user's guide).
> So the main question here is if we should use an EB procedure after the 
> gene-by-gene linear model or not...
> I really don't have (a convincing) answer to that, but I'm partial to the 
> idea of "borrowing information" across genes that EB procedures provide.
> The problem we have in practice is that the EB procedure implemented in 
> limma only considers a single variance component. Anything else should be 
> treated as a fixed effects (Even the biological subjects in some 
> layouts!). And that may not be a good assumption for some experimental 
> designs.
> JP
> Ingunn Berget wrote:
>>I believe there are two approaches for using ANOVA with microarrays,
>>1) Calculate logratios, do normalisation and then fit the experimental 
>>conditions by an ANOVA model, or
>>2) Use the intensities of each channel, transformed with appropriate 
>>transformation (log, linlog.logshift,...), and use array, dye, spot effect 
>>and so in the ANOVA model in addition to the experimental conditions. 
>>Which means that the normalisation is done by factors in the ANOVA model
>>limma is much used for the first approach, whereas I think Maanova is more 
>>used for the second approach.
>>Does anybody have any experience on both approaches? WHat is recommended?
>>Can the limma package be used for the second approach?
>>Additional question: Can continuous covariates be fitted with limma?
>>Ingunn Berget
>>Norwegian University of Life Sciences
>>Department of Animal and Aquacultural Sciences
>>Boks 5003
>>1432 Ås
>>Bioconductor mailing list
>>Bioconductor at stat.math.ethz.ch
> -- 
> =============================
> Juan Pedro Steibel
> Graduate Student
> Department of Animal Science Michigan State University
> 1261 Anthony Hall
> East Lansing, MI
> 48823 USA Phone: 1-517-432-0671
> E-mail: steibelj at msu.edu
> Web: http://www.msu.edu/~steibelj
> =============================

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