Hi, Paul.

Thinking of methylation as a "black or white" affair might make sense for an
individual cell or, perhaps, a perfectly homogeneous pool of cells (which
probably does not exist), but from a tissue, I'm not sure that it is
possible to think of methylation measurements that way.  What you are
measuring is the aggregation of methylation profiles associated with
potentially different methylation states in the tissue pool; this could
certainly result in a fully continuous measure of methylation.  Therefore,
finding statistical differences is still probably a useful way to think of
the problem (though not the only one, obviously).  Just like for gene
expression, a statistically significantly result does not imply a
biologically important result, so you may want to stipulate a further filter
that the difference between your two groups pass some arbitrary threshold.

Sean


On Fri, Jul 23, 2010 at 1:16 PM, Paul Geeleher <paulgeeleher@gmail.com>wrote:

> Interesting. I'm not sure it'd make sense to use expression values
> (log ratios I assume) because while there might be a statistically
> significant difference between the expression levels in each of the
> phenotypes, that doesn't necessarily imply that the reporters are
> methylated in one phenotype and unmethylated in the other if you see
> what I mean?
>
> I'm assuming in the second case you are refering to a p-value for to
> the probability of methylation of each reporter. Maybe this makes more
> sense, but I think you still need one phenotype to have high
> probabilty of methylation and the other phenotype to have high
> probability of unmethylation, along with a statistically significant
> difference in the p-values between the phenotypes?
>
> Paul.
>
> On Fri, Jul 23, 2010 at 8:02 PM, Sean Davis <sdavis2@mail.nih.gov> wrote:
> >
> >
> > On Fri, Jul 23, 2010 at 12:51 PM, Paul Geeleher <paulgeeleher@gmail.com>
> > wrote:
> >>
> >> Thanks for the replies guys,
> >>
> >> Sean, we have 5 disease samples and 5 control samples. Each array has
> >> 244k reporters located in CpG islands, averaging about 8 reporters per
> >> CpG island.
> >>
> >
> > So, why not generate a 10 x 244k matrix or 10 x 30k matrix if you
> summarize
> > over CpG island and then apply a hypothesis test of your choice (which
> might
> > need to be nonparametric, even) to the data?  The value associated with
> each
> > probe per sample could be either a raw value (after "appropriate
> > normalization") or it could be derived from a number of ChIP-chip like
> > analysis packages (ACME, tilingarray, etc.).
> > Sean
> >
> >>
> >> Jinyan, doesn't MEDME require some kind of calibration experiment?
> >> Needless to say this hasn't been done and it's unlikely that there is
> >> money there to do it.
> >>
> >> Paul.
> >>
> >> On Fri, Jul 23, 2010 at 7:02 PM, Sean Davis <sdavis2@mail.nih.gov>
> wrote:
> >> > Hi, Paul.  How many samples do you have?  And what are the sizes of
> the
> >> > groups?
> >> >
> >> > It seems to me that you have for each probe a number.  You could do
> >> > probewise testing between groups, or you could do some summarization
> >> > first
> >> > and then hypothesis testing.  In any case, there are a number of ways
> to
> >> > arrive at an n x p matrix where standard statistical tools could be
> >> > used.
> >> >
> >> > Sean
> >> >
> >> > On Jul 23, 2010 11:54 AM, "Paul Geeleher" <paulgeeleher@gmail.com>
> >> > wrote:
> >> >
> >> > I understand your approach but the main problem I'd see with such a
> >> > thresholding approach is that you are highly likely to find regions
> >> > that are just below the cutoff to be called "methylated" in one
> >> > phenotype and just above the threshold in the other phenotype. Thus
> >> > most likely not differentially methylated at all. Do you see what I
> >> > mean?
> >> >
> >> > Perhaps some kind of approach that labels each reporter as having a
> >> > probability of methylation (and hence a probability of unmethylation),
> >> > which can be compared across samples of a given phenotype to give a
> >> > probability of all reporters being methylated/unmethylated in each
> >> > phenotype, then compares these probabilities between phenotypes to
> >> > give a probability of "differential methylation". That's just off the
> >> > top of my head, I think it makes sense, but I'm presuming nothing like
> >> > that has actually been implemented?
> >> >
> >> > Paul.
> >> >
> >> > On Fri, Jul 23, 2010 at 6:45 PM, Steve Lianoglou
> >> > <mailinglist.honeypot@gmail.com> wrote:
> >> >> Hi,
> >> >>
> >> >> ...
> >> >
> >> > --
> >> > Paul Geeleher
> >> > School of Mathematics, Statistics and Applied Mathematics
> >> > National University of I...
> >> >
> >> > Bioconductor mailing list
> >> > Bioconductor@stat.math.ethz.ch
> >> > https://stat.ethz.ch/mailman/listinfo/bioco...
> >>
> >>
> >>
> >> --
> >> Paul Geeleher
> >> School of Mathematics, Statistics and Applied Mathematics
> >> National University of Ireland
> >> Galway
> >> Ireland
> >> --
> >> www.bioinformaticstutorials.com
> >>
> >> _______________________________________________
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> >
> >
>
>
>
> --
> Paul Geeleher
> School of Mathematics, Statistics and Applied Mathematics
> National University of Ireland
> Galway
> Ireland
> --
> www.bioinformaticstutorials.com
>
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