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List: bioconductor
Subject: Re: [BioC] Nimblegen arrays/Limma package:duplicate correlation and
From: Sean Davis <sdavis2 () mail ! nih ! gov>
Date: 2007-03-27 18:23:07
Message-ID: 200703271423.07617.sdavis2 () mail ! nih ! gov
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On Tuesday 27 March 2007 14:11, r.athanasiadou wrote:
> Yes it is a tiling array with no duplicate spots.
> I have looked into the packages that deal with tiling arrays like
> "chIPchip"(is it available for windows yet?) but from what I could gather
> from the way such packages work, they rely on having random fractionation
> of the genome ie sonication. Unfortunately, my experiment required
> restriction endonuclease digestion to fractionate the genome (produces
> specific and predictable short fragments and ideally -no sequence bias- I
> expect a sharp rise and fall of a positive region rather than a normal
> distribution of the M-values) and I don't thing that the common algorithms
> to summarize the probe-data are applicable in my case.
>
> I am thinking to rely on how many probes (out of the total number of probes
> that should hybridize to each generated genomic fragment) give reproducible
> and comparable results to summarize my probe-level data.
Niki,
The package, ACME (previously known as chIPchip--thank one of my collaborators
for the interesting name) is available as a Bioconductor package in the
developer section. It does not rely on the specifics of R-devel, so it
should run just fine on post-2.4 versions of R (and probably even earlier).
It doesn't make particular assumptions about the distribution of the M-values
except that more than expected by chance are above a threshold within a
window of user-specified size (which I would think you could choose as your
mean fragment size, or slightly larger so that you have about 10-12 probes in
most windows). ACME is very insensitive to the actual distribution (data
need not be centered, or even log-transformed)
In your case, since you are also interested in differenced between two
conditions, you could try simply subtracting the values for condition1 from
condition2 and running ACME on the results. Doing so will likely be noisier
than a single array, but you could certainly hope to get something useful.
Sean
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