[BioC] Limma vs Maanova, and use of covariates
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so perhaps I am out of date here.)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?
>Norwegian University of Life Sciences
>Department of Animal and Aquacultural Sciences
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch
Naomi S. Altman 814-865-3791 (voice)
Dept. of Statistics 814-863-7114 (fax)
Penn State University 814-865-1348 (Statistics)
University Park, PA 16802-2111
More information about the Bioconductor