On 2/28/06 7:30 AM, "michael watson (IAH-C)" <michael.watson at="" bbsrc.ac.uk="">
wrote:
> 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 would say that this is only one of several ways of analyzing time-
course
data, and perhaps not the best one for all situations. In fact,
sometimes I
have the best solution to be simple filtering of genes followed by
clustering and display, but I think the "correct" solution depends on
the
experimental design (numbers of time points, for example) and goals
(for
example, it doesn't help a biologist to have 2000 genes in a list if
the
goal is to find 10 transcription factors that seem to be affected at
any
time point).
For limma, you could use decideTests, for example, to give you some
sense of
genes that are changing in the experiment. Or you could filter for
those
genes that are changing at the "maximal" time point and then show
those
genes for all the timepoints on a heatmap--this will allow the
biologist to
quickly focus on genes of interest.
Sean
To complete my original mail, I guess I was referring to this
approach:
http://bioinf.wehi.edu.au/marray/jsm2005/lab5/lab5.html
-----Original Message-----
From: Sean Davis [mailto:sdavis2@mail.nih.gov]
Sent: 28 February 2006 13:22
To: michael watson (IAH-C); Bioconductor
Subject: Re: [BioC] Limma and time-course data
On 2/28/06 7:30 AM, "michael watson (IAH-C)"
<michael.watson at="" bbsrc.ac.uk="">
wrote:
> 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 would say that this is only one of several ways of analyzing
time-course data, and perhaps not the best one for all situations. In
fact, sometimes I have the best solution to be simple filtering of
genes
followed by clustering and display, but I think the "correct" solution
depends on the experimental design (numbers of time points, for
example)
and goals (for example, it doesn't help a biologist to have 2000 genes
in a list if the goal is to find 10 transcription factors that seem to
be affected at any time point).
For limma, you could use decideTests, for example, to give you some
sense of genes that are changing in the experiment. Or you could
filter
for those genes that are changing at the "maximal" time point and
then
show those genes for all the timepoints on a heatmap--this will allow
the biologist to quickly focus on genes of interest.
Sean
Dear Michael,
You might want to have a look at the package maSigPro for the analysis
of
microarry time-course data. The method evaluates differences in gene
expression along time and between experimetal conditions without
focussing
on specific time points, but analyzing gene expression trends.
Ana
On Tue, 28 Feb 2006 12:30:47 -0000, michael watson \(IAH-C\) wrote
> 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
>
> [[alternative HTML version deleted]]
>
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