[BioC] Limma vs Maanova, and use of covariates

Juan Pedro Steibel steibelj at msu.edu
Thu Dec 8 15:51:56 CET 2005

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 
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 


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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