Hello Jim & List,

how would you go about doing model selection using two-color data?  (since your response variable is actually a ratio)

I'm actually surprised that no "formal/mathematical" linear model is written in the limma Users Guide... any comments?

Kind regards,
Yannick



On 2009-09-03 13:33:0, Jim Macdonald wrote:
> Hi Andre,
> 
> If you want to do model selection, then limma is probably not the tool 
> for the job.
> 
> Instead, what I would do would be to choose some (one, five, ten, 
> whatever) genes and use lm() for the model selection process. That way 
> you can do all the conventional model selection steps, and once you are 
> satisfied with the model you have chosen you can go back to limma and 
> fit the model on all the genes.
> 
> Best,
> 
> Jim
> 
> Andre J. Aberer wrote:
> > Dear list members,
> > 
> > short version of my question: 
> > How can I determine, whether it improves the model quality of a linear
> > model (in limma), when I introduce additional explanatory variables? Is
> > there an equivalent to feature selection (as in machine learning) for
> > choosing the explanatory variables?
> >  
> > The complete story: 
> > We analyse a dataset of about ninety single channel microarray chips and
> > we want to search for differentially expressed genes and enriched gene
> > sets. The chips are annotated with information (at least 20 factors,
> > could be extended to 50) like the organ from which the RNA was
> > extracted, the experimenter that did the lab work, the labelling kit she
> > used and a huge amount of features describing e.g. the genotype of the
> > individual or different aspects of the disease.
> > 
> > We would like to build one linear model (resp. one design matrix) with
> > all of the factors of interest mentioned above as explanatory variables
> > in order to test various contrasts. Of course, we have to include all
> > the variables that we possibly want to test in the linear model. But
> > what about the ``technical'' factors like the ``labelling kit'' that was
> > used? One never might want to test a contrast using this explanatory
> > variable, however the net chip intensity could be influenced by a
> > technical factor like this. So how can I determine, if it makes sense to
> > include this variable? 
> > 
> > I am using the standard procedure as described in the limma guide: 
> >  designMatrix <- model.matrix(~0 + var1 + var2, data=someTable)
> >  fitBoth <- lmFit(eset, designMatrix)
> > where var1 and var2 are variables like ``diseaseOutcome'' and
> > ``labellingKit''. 
> > 
> > We thought, that maybe an anova table could help us here, showing us the
> > influences of var1 and var2. As far as I read
> > (e.g. http://data.princeton.edu/R/linearModels.html) the anvoa function
> > can be simply applied to a lm object or can be used to compare two lm
> > instances. Of course, in that case it is only applied to one linear model
> > and not one per gene as in the limma setting. 
> > So, if I try anova for one or two limma fit objects (MArrayLM), R
> > complains that there is no applicable method and other anova variants
> > (like anova.lm) do not work neither. This holds as well, when I want to
> > do an anova for just one extracted linear model for one gene
> > (like anova(lmFit[1,])).
> > 
> > Our ultimo ratio so far is, to build a design matrix with and another
> > one without a certain explanatory variable. Then we would determine the
> > top DEGs and compare for each DEG their fitted linear models in an anova
> > table. Finally we could check for how many of the top DEGs the
> > additional variable would make a difference.  
> > However, this does not seem to be the golden path...or are we completely
> > on the wrong track?
> > 
> 
> -- 
> James W. MacDonald, M.S.
> Biostatistician
> Douglas Lab
> University of Michigan
> Department of Human Genetics
> 5912 Buhl
> 1241 E. Catherine St.
> Ann Arbor MI 48109-5618
> 734-615-7826


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