jhs1jjm at leeds.ac.uk wrote: > Hi Sean, > > As its 2 colour so I'm looking at relative amounts wouldn't that mean I > wouldn't see copy number variants, would they not be in both my > samples? I was also pondering the advantages of using R and > bioconductor, vs say Agilent's z score, for the purposes of my > discussion. Is the simple answer simply a flexible approach to these > matters? Also if possible could you expand a bit in regards to the > single probes argument. If using Agilent CGHAnalytics, you will probably want to use ADM-1, not z-score. For the 44k arrays, a threshold of around 6 is probably appropriate. For the 244k arrays, something closer to 10 or 11 is more appropriate. ADM-1 is exquisitely sensitive to single probes that are extreme values. These may represent real signal, or may be noise. There is no way to tell without validation, in my opinion. However, If there are two or more probes behaving similarly, then you can be more assured of real biology. The real biology could be directly disease-related or not. The ones that are not are copy number variants (although there is now plenty of evidence that copy number variants can be disease-associated, as well). When using high-resolution oligo arrays, you will need to become familiar with copy number polymorphism and databases for annotating them. CGHAnalytics contains a catalog of those built-in. As for R/Bioc versus commercial packages, that will be dictated by the questions you want to ask. We find that we routinely need and want to ask questions that are not easily answered by commercial packages. That said, a good visualization tool for CGH is HIGHLY useful, and there are now several available. Sean

jhs1jjm at leeds.ac.uk wrote: > Hi Sean, > > As its 2 colour so I'm looking at relative amounts wouldn't that mean I > wouldn't see copy number variants, would they not be in both my > samples? I forgot to answer this question, directly. If the reference genome and the test genome contain the same number of copies of a CNV region, you will not see it, as you suggest. However, if your reference and test samples contain different numbers of copies, then this will potentially be evident in your data. Sean

On Wednesday 10 October 2007 17:04, jhs1jjm at leeds.ac.uk wrote: > Hi Ramon, > > Ah, of course, I'd forgotten I'd performed that step. I'm still getting > some segments with high means corresponding to single genes but this'll > be because they are represented by more than 1 probe I guess. The > DNAcopy document has a step in it to remove local trends in the data. > I'm undoing splits that are not at least 3 SDs apart as set out in the > document. > Ah, OK. I thought you were referring to other trends (I've heard people mention waves, and relations to CG content, etc ---the later, I think, commonly done in Affy). > To summarize then,I might use DNA copy to identify regions but in order > to look at single probe aberrations I'd want to use one of the other > methods i.e HMM > We often analyze data with four or five different methods (our own HMM in RJaCGH, Olshen's CBS, HMM as in Marioni et al., Piccard's et al CGHseg, and Hsu et al. wavelet-based smoothing) because different approaches are sensitive to different features of the data (or can be misled by different features of the data). (Of course, we do think our approach is the best overall performer, but this way we can keep learning about relative strengths of different methods and/or detect bugs in the code). Detecting single point aberrations might be trickier than, say, detecting a long alteration that involves tens of probes. But then, inability to detect single gene alterations can be very relevant in some studies (e.g., IIRC, Aguirre et al., in PNAS 2004, in their study of pancreatic adenocarcinoma, have some discussion not detecting the loss of the tumor supressor SMAD4). As for the need for validation, etc, if you have a gene covered by a bunch of probes and only a single probe is being called aberrant then I'd be more concerned; but you might be averaging over probes, or use platforms where some genes only have a probe, etc. In general, many/most of the current aCGH studies are really exploratory studies (i.e., they are in the "copy number differences discovery" stage, not "copy number association studies" stage) with results that need to be validated further (other aCGH platforms, other molecular techniques); there are several papers in the July 2007 issue of Nature Genetics (volume 39) that go into these issues. Best, R. > Thanks > John > > Quoting Ramon Diaz-Uriarte <rdiaz at="" cnio.es=""> on Wed 10 Oct 2007 15:22:22 > > BST: > > Dear John, > > > > On Wednesday 10 October 2007 15:52, 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 > > > > How exactly are you removing "unnecesary change points due to trends > > in the > > data"? > > > > > 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. > > > > > > 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? > > > > If you run the "smooth.CNA" function (in the DNAcopy package), as it > > is > > recommended in the documentation for DNAcopy (IIRC), then single > > probe > > aberrations are not detectable (you are smoothing them away). > > > > Single probe aberrations might be detected with the HMM model in > > snapCGH or > > our HMM model in RJaCGH, available from CRAN > > (http://cran.r-project.org/src/contrib/Descriptions/RJaCGH.html). > > (Details of > > the method available from the paper: > > http://compbiol.plosjournals.org/perlserv/?request=get- document&doi=10.1371 >%2Fjournal.pcbi.0030122). > > > Best, > > > > R. > > > > > Apologies if this is a bit vague. > > > > > > Thanks for any input, > > > > > > John > > > > > > _______________________________________________ > > > Bioconductor mailing list > > > Bioconductor at stat.math.ethz.ch > > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > Search the archives: > > > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > -- > > Ram?n D?az-Uriarte > > Statistical Computing Team > > Centro Nacional de Investigaciones Oncol?gicas (CNIO) > > (Spanish National Cancer Center) > > Melchor Fern?ndez Almagro, 3 > > 28029 Madrid (Spain) > > Fax: +-34-91-224-6972 > > Phone: +-34-91-224-6900 > > > > http://ligarto.org/rdiaz > > PGP KeyID: 0xE89B3462 > > (http://ligarto.org/rdiaz/0xE89B3462.asc) > > > > > > > > **NOTA DE CONFIDENCIALIDAD** Este correo electr?nico, y en su caso > > los ficheros adjuntos, pueden contener informaci?n protegida para el > > uso exclusivo de su destinatario. 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If you are not the > > intended recipient please contact the sender and delete all copies. -- Ram?n D?az-Uriarte Statistical Computing Team Centro Nacional de Investigaciones Oncol?gicas (CNIO) (Spanish National Cancer Center) Melchor Fern?ndez Almagro, 3 28029 Madrid (Spain) Fax: +-34-91-224-6972 Phone: +-34-91-224-6900 http://ligarto.org/rdiaz PGP KeyID: 0xE89B3462 (http://ligarto.org/rdiaz/0xE89B3462.asc) **NOTA DE CONFIDENCIALIDAD** Este correo electr?nico, y ...{{dropped:3}}