“To make the preliminary test on variances is rather like putting to sea in
a rowing boat to find out whether conditions are sufficiently calm for an
ocean liner to leave port!” (Box, 1953)

>From an article on mixture models for simultaneously detecting differences
in the mean and variance, by Haim Bar and Jim Booth, which is by far the
best I've read.  I was going to keep this to myself but it appears that
there is no need, so, dig out the paper and get reading.  It is a good
paper with good examples and the code is (was?) available from Haim Bar.  I
don't know if he has made a package out of it yet.  The paper was published
in Statistical Applications in Genetics and Molecular Biology, a journal
which used to be fantastic (under the Berkeley Electronic Press) but under
deGruyter seems to be suffering rather a lot.  C'est la vie.






On Wed, Mar 27, 2013 at 4:43 AM, Pekka Kohonen <pkpekka@gmail.com> wrote:

> Hello,
>
> I also find this an interesting question. I don't have a solution
> handy but I would say that using linear models seems preferable
> because you can discount in the model other sources of variation. For
> instance batch effects, other confounding variables/covariates
> (smoking?, bmi?) and so on that are not age-related.
>
> Best Regards, Pekka
>
> 2013/3/26 Simone <enomis.bioc@gmail.com>:
> > Hi!
> >
> > My question is more of a general style, nevertheless I hope someone can
> > help.
> >
> > I am currently trying to analyze "differential variability" of gene
> > expression and, above all, methylation data (Illumina microarray data:
> 27K
> > and 450K BeadChip) in the context of aging, i.e. I would like to see if
> the
> > variability of methylation increases (or decreases) for (healthy)
> > individuals when they age. I would like to do this gene-wise, to see if
> and
> > which genes show increased/decreased variability.
> >
> > Several studies already published in this context employ different
> methods
> > for such kind of analyses:
> >
> > First of all there is the normal F-test. But since it requires data that
> > does not depart from normality I think it is not applicable in my case.
> For
> > one of my datasets (~ 500 samples after outlier removal) I performed
> > Shapiro-Wilk tests for the ~ 27.000 CpGs and found that more than 26.200
> > CpGs do not have normally distributed values (FDR 0.05). I think this is
> an
> > usual observation when working with methylation data.
> >
> > In other analyses investigating similar questions Bartlett's test was
> > employed. But it would require normal distributions as well. I also read
> > something about this right here or in the R mailing list, where Ansari's
> > test was proposed then for doing such kind of analyses. So maybe Ansari's
> > test would be a good choice, although so far I have not seen any
> > publication doing variability analyses by using Ansari's test.
> >
> > Another approach which was recommended to me was to not build age groups
> > and compare them to each other (I used two "extreme" age groups, so very
> > young vs. very old samples), but to create a kind of fixed-effect models
> > for analyzing variability with age. Maybe something like this would be
> the
> > best option as we have all the age information available (in years or
> even
> > months) and this way we do not loose any information we actually have
> got.
> > But I am not quite sure about how to model variability. How would one do
> > this?
> >
> > Recently there was also a study published where they say that they used
> > linear models and calculated "methylation deviance" as the squared
> distance
> > of the residuals of every marker from the population mean, but again I am
> > not sure about it, and the description of the methods part is quite
> short.
> >
> > Any suggestions about the "best" way to analyze changes in variability of
> > methylation (and gene expression) values?
> > Which strategy would you recommend?
> >
> > Best,
> > Simone
> >
> >         [[alternative HTML version deleted]]
> >
> > _______________________________________________
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> > Search the archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor
>
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>



-- 
*A model is a lie that helps you see the truth.*
*
*
Howard Skipper<http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>

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