Jo?o Fadista wrote:
> Dear all,
>
> snapCGH -> Does anyone know how to display regions of gain or loss
across multiple samples in the same plot? I am asking this with the
purpose of finding common breakpoints and regions of common gain or
loss between samples.
>
>
The "standard" way of doing this is a "frequency plot". Simply sum
across samples all the probes that are greater (less) than a certain
threshold and plot them along the chromosome. I don't personally like
them very much, but it is easy to produce. Also, you might look at
the
package cghMCR and also at the java executable called STAC.
> CGH method evaluation -> Does anyone knows if there is any CGH
package that estimate the statistical significance of the detected
copy number changes and then rank them accordingly?
>
This is a very challenging problem. You, as the researcher, will
probably need to decide what you think is important (short segments,
long segments, high-copy number gains, homozygous deletions, copy
number
polymorphisms, etc.) and design your own methods for pulling these
out.
A simple statistical test does not do the trick, in my experience, for
pulling out all the relevant information. If you really want
something
that gives statistical significance, you might want to look at an
article by Lipson et al., in RECOMB 2005 (don't have the exact
reference
at hand).
Sean
On Monday 04 December 2006 13:01, Jo?o Fadista wrote:
> Dear all,
>
> snapCGH -> Does anyone know how to display regions of gain or loss
across
> multiple samples in the same plot? I am asking this with the purpose
of
> finding common breakpoints and regions of common gain or loss
between
> samples.
>
> CGH method evaluation -> Does anyone knows if there is any CGH
package that
> estimate the statistical significance of the detected copy number
changes
> and then rank them accordingly?
Dear Joao,
My answers do not refer to snapCGH, but to our package RJaCGH
(http://cran.r-project.org/src/contrib/Descriptions/RJaCGH.html) which
might
provide some of what you want.
For the first, if you use an "array" object, the default plot shows
the
frequency with which each alteration is present over the set of
samples.
For the second, we use a Bayesian approach via MCMC, so we return the
posterior probabilities of alteration for every gene. You can then
rank them,
or select those above a certain threshold, etc, however you want.
Best,
R.
P.S. A new version of the package with a vignette should be available
soon
(about 10 days?)
>
>
> Best regards
>
> Jo?o Fadista
> Ph.d. student
>
>
>
>
> Danish Institute of Agricultural Sciences
> Research Centre Foulum
> Dept. of Genetics and Biotechnology
> Blichers All? 20, P.O. BOX 50
> DK-8830 Tjele
>
> Phone: +45 8999 1900
> Direct: +45 8999 8999
> E-mail: Joao.Fadista at agrsci.dk <mailto:joao.fadista at="" agrsci.dk="">
> Web: www.agrsci.org <http: www.agrsci.org=""/>
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