[BioC] GC-content sensitive normalization of Affymetrix tiling arrays for ChIP-chip

Christian Feller feller.christian at gmail.com
Thu Jul 10 15:14:15 CEST 2008

Hi Sean,

Thank you for your quick response! We successfully used MAT under Python for a dataset with 3 control arrays (hybridized with input) and 3 IP arrays (all biological replicates). In comparison with vsn2, probe standardization via MAT significantly increased the signal-to-noise ratio. However, we have still some doubts about the reliability of those results since the raw data seem to be very noisy, and the correlation of the biological replicates is not very strong.
Thanks again!


-----Original Message-----
From: seandavi at gmail.com [mailto:seandavi at gmail.com] On Behalf Of Sean Davis
Sent: Wednesday, July 09, 2008 2:04 AM
To: Christian Feller
Cc: bioconductor at stat.math.ethz.ch; bourgon at ebi.ac.uk
Subject: Re: [BioC] GC-content sensitive normalization of Affymetrix tiling arrays for ChIP-chip

On Tue, Jul 8, 2008 at 6:58 PM, Christian Feller
<feller.christian at gmail.com> wrote:
> Dear Richard Bourgon and list,
> I am a newbie in analyzing ChIP-chip Affymetrix tiling arrays (GeneChip
> Drosophila Tiling 1.0R Array).
> My question is how can I take into accound the GC-effect of single probes if
> I do not have expression sets (due to the nature of a tiling array)? We had
> the idea of taking a fixed window size, defining the probes within them as a
> "probeset", and using GCRMA for background correction/normalization. In
> addition, can we use this configuration (normalization via GCRMA) for
> profiles with broad ChIP-enriched regions (as it is the case for many
> histone modifications).
> If there are some additional advice especially for the pre-processing steps
> I would be very happy!
> Until now, we do the normalization using vsn2.

Hi, Christian.  Do you have the input DNA from which you are going to
form a ratio, or are you attempting to do a single-channel analysis?
If the latter, then you might look at MAT from Shirley Liu's group.  I
don't think it is available for R, but the algorithm could probably be
coded in R relatively easily.  There are likely other solutions.


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