[BioC] Limma and time-course data

Fri Mar 3 16:23:47 CET 2006

Hi Guys

OK, so I have access to the F statistic, but the example at
http://bioinf.wehi.edu.au/marray/jsm2005/lab5/lab5.html simply uses the
F statistic to find the top 500 changing genes - somewhat arbitrary.  

Can I access p-values for these F statistics, so that I can look at
genes where time is a significant factor at, say, p<=0.05?

Many thanks
Mick

-----Original Message-----
From: Naomi Altman [mailto:naomi at stat.psu.edu] 
Sent: 02 March 2006 19:21
To: michael watson (IAH-C); Gordon Smyth
Cc: bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] Limma and time-course data

Usually you would fit a factorial design and compile the contrasts into
3 F-tests: time, strain and interaction.

--Naomi

At 12:25 PM 3/2/2006, michael watson (IAH-C) wrote:
>Thanks for your help, but one more question: if I have two factors, one

>of which is time, and the other is strain, and say I have used a common

>reference, there should be two F tests, one for time and one for 
>strain, no?
>
>________________________________
>
>From: Gordon Smyth [mailto:smyth at wehi.edu.au]
>Sent: Thu 02/03/2006 1:45 AM
>To: michael watson (IAH-C)
>Cc: bioconductor at stat.math.ethz.ch
>Subject: RE: [BioC] Limma and time-course data
>
>
>
>You will need at least as many contrasts as time points minus one. So 
>if you have 3 times, you need at least 2 contrasts. It is just like 
>oneway anova.
>
>Cheers
>Gordon
>
>At 09:39 AM 2/03/2006, michael watson \(IAH-C\) wrote:
> >Hi Gordon
> >
> >And a lightbulb goes on just above my head!  It was beginning to 
> >confuse me where the F test came in.
> >
> >So once I have fit the contrasts (given that they span the entire 
> >time course, so if I have endpoint-startpoint as default, I will be
> >OK) I can access the F statistic through the fitted model object?  
> >And this has a significance value associated with it?
> >
> >Thanks alot :)
> >
> >Mick
> >
> >________________________________
> >
> >From: Gordon K Smyth [mailto:smyth at wehi.EDU.AU]
> >Sent: Wed 01/03/2006 9:59 PM
> >To: michael watson (IAH-C)
> >Cc: bioconductor at stat.math.ethz.ch
> >Subject: [BioC] Limma and time-course data
> >
> >
> >
> >Dear Mike,
> >
> >limma does exactly what you want.  The approach outlined in the 
> >User's Guide (and in the workshop)
> >  finds genes which change over time without worrying about which  
> >particular time the genes differ at.  I think you may have missed the

> >fact that the approach uses the F-test, not the individual contrast 
> >p-values.  You will get the same F-test regardless of how you specify

> >the contrasts, as long as the contrasts span all the times.
> >
> >As far as verbosity is concerned, limma is a general purpose program,

> >not specifically for time courses.  So to create the F-test, you do 
> >need to explicitly setup a set of contrasts.  Strictly speaking, I 
> >could get limma to make a set of contrasts automatically if it is 
> >known that you want to do an F-test.  But making the contrasts takes 
> >only a few lines of code (as you show below), so I can live with 
> >that, at least for now.
> >
> >Cheers
> >Gordon
> >
> > > Date: Tue, 28 Feb 2006 12:30:47 -0000
> > > From: "michael watson \(IAH-C\)" <michael.watson at bbsrc.ac.uk>
> > > Subject: [BioC] Limma and time-course data
> > > To: <bioconductor at stat.math.ethz.ch>
> > >
> > > Hi
> > >
> > > Googling the list shows this up to be a rather hot topic, but I 
> > > just wanted to ask a few more questions.
> > >
> > > Firstly, it seems the plan for tackling time course data within 
> > > limma is to treat each time-point/treatment combination as a 
> > > coefficient to be estimated.  Thus, to ask "which genes are 
> > > changing over time", one must fit contrasts that compare every 
> > > single time point to every other time point, pairwise, and look 
> > > for any gene that is significant in one or more of those
comparisons.  Is that correct?
> > >
> > > I am also a tad confused by the documentation, which states (on 
> > > page
> > > 47):
> > >
> > > "> cont.wt <- makeContrasts(
> > > + "wt.6hr-wt.0hr",
> > > + "wt.24hr-wt.6hr",
> > > + levels=design)
> > >> fit2 <- contrasts.fit(fit, cont.wt)
> > >> fit2 <- eBayes(fit2)
> > >
> > > Any two contrasts between the three times would give the same
result.
> > > The same gene list
> > > would be obtained had "wt.24hr-wt.0hr" been used in place of 
> > > "wt.24hr-wt.6hr" for example."
> > >
> > > I'm confused why "wt.24hr-wt.0hr" and "wt.24hr-wt.6hr" would give 
> > > the same gene list.  The first looks for differences in wt between

> > > time points 0 and 24, and the second looks for differences between

> > > time points 6 and 24.
> > >
> > > I guess, to me, this all seems a bit verbose and difficult, 
> > > particularly for large time-course experiments where many 
> > > biologists want to subset their data to those genes that change 
> > > over time and thus want to ask the question "does time have an 
> > > effect on the expression of my gene?" and are not particularly 
> > > bothered, at this stage, which particular time points those genes
differ at.
> > >
> > > Thanks in advance
> > >
> > > Mick
> >
> >
> >
> >
>
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
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