[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?
>Norwegian University of Life Sciences
>Department of Animal and Aquacultural Sciences
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch
Juan Pedro Steibel
Department of Animal Science
Michigan State University
1261 Anthony Hall
East Lansing, MI
E-mail: steibelj at msu.edu
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