[BioC] Limma vs Maanova, and use of covariates

Naomi Altman naomi at stat.psu.edu
Thu Dec 8 15:36:01 CET 2005

Limma can be used for the 2nd approach.  See the 
section in the manual on Single Channel analysis.

In my opinion, even if you use the MAANOVA 
approach, you need to normalize before analysis, 
because normalization takes care of intensity 
dependence of differential expression, which is 
not handled by the linear model.

I prefer the Limma approach because of the clever 
modeling of the array random effect, which seems 
preferable to the gene-by-gene model of 
MAANOVA.  (I have not used MAANOVA for 2 years, 
so perhaps I am out of date here.)  The only 
possible problem with the Limma approach is that 
you cannot have other random effects, so e.g. if 
you have both technical and biological 
replicates, you need to fit the biological 
replicates as fixed blocks.  In the experiments I 
have analyzed so far, this effect has not been large.

I am not sure whether MAANOVA allows an unlimited 
number of random effects, currently.  When I last 
used it, MAANOVA required balanced data, which 
meant that flagged spots were problematic.  Limma 
handles flagged spots by weighting - but not in the Single Channel Analysis.


At 08:00 AM 12/8/2005, 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

Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111

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