[BioC] Limma nestedF

[noel0925 at sbcglobal.net]

Hi Jim,

Thanks so much for the helpful pointers and
explanation- it is much more clear to me now.

Thanks,
Noelle


--- "James W. MacDonald" <jmacdon at med.umich.edu>
wrote:

> Hi Noelle,
> 
> First, please don't take things off-list. The list
> archives are a good 
> resource for people to search, so taking
> questions/answers off-list 
> reduces the information content.
> 
> noel0925 at sbcglobal.net wrote:
> > Hi Jim,
> > 
> > Thank you very much for your response. I am only a
> novice at these
> > types of analyses and am trying to get a handle on
> it all.
> > 
> > So in this particular experiment there are six
> gentoypes (a wild-type
> > and 5 different mutants due to point mutations in
> the same gene). One
> > of the goals of this experiment is to be able to
> rank the mutants
> > according to their severity or the difference in
> their expression
> > profile compared to the wild-type in treatment 1
> and treatment 2. It
> > is also of interest to see how the various mutants
> respond to the two
> > treatments, and  finally it might also be
> interesting to consider the
> > interaction term (genotype*treatment).  Thus, in
> this study the two
> > main effects are of interest, as are the
> interaction effects.
> 
> Well, you don't have any classical main effects
> here. If you study 
> section 8.7 in the Limma User's Guide, you will see
> what I mean. In fact 
> the comparisons you show below are exactly what
> Gordon argues one should 
> be making. Since there are no classical main effects
> (i.e., treatment 
> without regard to WT/MT status, and WT vs MT without
> regard to 
> treatment), then you don't really have to worry
> about interactions 
> superceding main effects.
> 
> > 
> > Here is my contrast matrix:
> > 
> > cont.matrix<- makeContrasts( WT.Treat.Effect=WT.T2
> - WT.T1,
> > 
> > Mt1.Geno.Effect.T1=Mt1.T1 - WT.T1,
> Mt2.Geno.Effect.T1=Mt2.T1 - WT.T1,
> >  Mt3.Geno.Effect.T1=Mt3.T1 - WT.T1,
> Mt4.Geno.Effect.T1=Mt4.T1 -
> > WT.T1, Mt5.Geno.Effect.T1=Mt5.T1 - WT.T1,
> > 
> > Mt1.Geno.Effect.T2=Mt1.T2 - WT.T2,
> Mt2.Geno.Effect.T2=Mt2.T2 - WT.T2,
> >  Mt3.Geno.Effect.T2=Mt3.T2 - WT.T2,
> Mt4.Geno.Effect.T2=Mt4.T2 -
> > WT.T2, Mt5.Geno.Effect.T2=Mt5.T2 - WT.T2,
> > 
> > Mt1.Treat.Effect=Mt1.T2 - Mt1.T1,
> Mt2.Treat.Effect=Mt2.T2 - Mt2.T1, 
> > Mt3.Treat.Effect=Mt3.T2 - Mt3.T1,
> Mt4.Treat.Effect=Mt4.T2 - Mt4.T1, 
> > Mt5.Treat.Effect=Mt5.T2 - Mt5.T1,
> > 
> > Mt1.Int.Effect=(Mt1.T2 - Mt1.T1) - (WT.T2 -
> WT.T1), 
> > Mt2.Int.Effect=(Mt2.T2 - Mt2.T1) - (WT.T2 -
> WT.T1), 
> > Mt3.Int.Effect=(Mt3.T2 - Mt3.T1) - (WT.T2 -
> WT.T1), 
> > Mt4.Int.Effect=(Mt4.T2 - Mt4.T1) - (WT.T2 -
> WT.T1), 
> > Mt5.Int.Effect=(Mt5.T2 - Mt5.T1) - (WT.T2 -
> WT.T1), levels = design)
> > 
> > Does the fact that main effects are of interest in
> any way alter how
> > you would tackle this problem? I am eager to hear
> your opinion!
> 
> This experiment reminds me of a certain type of
> client that I see. When 
> we talk about their experiment and ask if a certain
> comparison is of 
> interest, they nod their head vigorously and say
> 'yes!'. Unfortunately, 
> they say the same thing regardless of the
> comparison, so I end up making 
> every possible comparison I could think of, and I
> send the results off 
> wondering how they are ever going to get anything
> useful from the 
> experiment (not because the comps are bad per se,
> but because of the 
> deluge of data I just buried them with).
> 
> This is the downside IMO of microarray experiments.
> They tend to be very 
> expensive, so people often want to wring every last
> bit of information 
> possible out of a given experiment, regardless of
> how interesting a bit 
> of information may be.
> 
> I suppose one could analyze the data once, output
> all the significant 
> terms, and then look at them at their leisure, but I
> would prefer a more 
> hypothesis/goal driven approach such as your first
> statement. If you 
> want to rank the mutants according to their
> severity, how would one do 
> that? Is severity a genotypic or phenotypic
> quantity? If genotypic, is 
> it measured by the number of differentially
> expressed genes when 
> compared to normal, or can you rank based on the
> particular genes that 
> are differentially expressed? Thinking about what
> you really want from 
> the data and how you will measure that quantity IMO
> is a better way to 
> go. Anyway, enough ranting ;-D
> 
> > 
> > As regards handling interaction terms, I am aware
> of the concept of
> > testing for interaction terms and dropping them if
> they are not
> > significant, but how is this done in Limma or for
> microarray analyses
> > in general- is there a package in BioC that does
> this? Also, if as
> > you say the interaction terms are usually the only
> "interesting"
> > contrasts in most microaray experiments like the
> knockout example you
> > gave- then what do analysts usually do to handle
> this? It seems the
> > examples I read in the literature never mention
> testing for the
> > significance of interaction terms. Is there a
> refrerence for this you
> > can point me to? I am eager to learn.
> 
> If you were to do something like this (as mentioned,
> it's not 
> necessary), you would have to do it by hand. I don't
> think there is any 
> functionality in limma for this, probably because a
> classical main 
> effect is usually not sensical in the context of a
> microarray analysis.
> 
> > 
> > I for example, looked at the Weaver mutant data a
> 2X2 factorial
> > experiment for 2 color data found on page 75 of
> the Limma Users
> > Manual. While my data is single channel, it is
> likewise a factorial
> > design (6 X 2) and hence similar from that
> standpoint as well as that
> > both studies consider main effects and interaction
> terms.
> 
> The factorial design in section 8.7 is probably a
> better example to look at.
> 
> > 
> > Also, you suggested using a stepwise approach and
> testing each
> > particular hypothesis separately- do you mean
> within Limma using
> > decideTests with the method "separate" or does
> this entail something
> > else? I am still uncertain how to implement this.
> > 
> > Finally, if it is not too much to ask how does
> "separate" differ from
> > "global"? The Limma manual says that global will
> treat the entire
> > matrix of t-statistics as a single vector of
> unrelated tests- I
> > assume this means independent tests. It seems that
> "separate" does
> > the same since it treats each coefficient
> separately- obviously they
> > are not the same, but I have missed out on
> recognizing the
> > difference.
> 
> 
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