[BioC] segmentation aCGH data

[Sean Davis]

jhs1jjm at leeds.ac.uk wrote:
> Hi list,
> 
> I've been looking at 3*44k and 2*244k agilent CGH arrays. To date I've
> used limma to read in the processed signals (no background correction
> or normalization as this has been done), then the DNAcopy package for
> segmentation as well as the snapCGH package to employ other
> segmentation methods rather than use each segmentation package
> individually.
> 
> Firstly using the DNAcopy segmentation I can see a significant pattern
> across my 3*44k arrays which disappears when I perform the step to
> remove unnecessary change points due to trends in the data. As these
> are in the same locations across the 3 arrays then is it likely that
> this is biologically significant rather than being a trend? Obviously
> others do not have a definitive answer for this but I wondered if
> anyone had seen similar results in a different scenario.

What you are describing could be technical in nature or
copy-number-variants.  You will probably need to review those regions
for known copy-number-variants and also look at the quality control
metrics for those probes.  Unfortunately, segmentation is not "the final
answer" to CGH analysis--there has to be some curation (either manual or
automated) to find the regions of greatest interest and remove the
regions that are likely not associated with the disease state.

> Additionally I'm wondering what segmentation methods people have tended
> to employ. The heterogeneous nature of my data means that I need to
> identify  single probe as well as larger region aberrations and I'd
> read that the CBS algorithm is not particular suited to doing this?
> Apologies   if this is a bit vague.

Single probes are problematic and require validation using another
technology or array platform, in my opinion.