To use CRLMM, you should install oligo, available at: http://www.bioconductor.org/packages/1.9/bioc/html/oligo.html You will also need a platform design environment, specific to the array you're using... Some can be downloaded from: http://www.biostat.jhsph.edu/~bcarvalh/research.html Best, b. On Aug 22, 2006, at 2:52 PM, Henrik Bengtsson wrote: > Hi. > > On 8/22/06, Amit Bahl <abahl at="" mail.med.upenn.edu=""> wrote: >> >> I have a custom Affy array which allows several applications >> (expression profiling, genotyping, etc...) on a single chip. I want >> to use RLMM to analyze our genotyping data, but have a couple of >> questions: >> >> 1) Instead of normalizing to the scale of the training set (which I >> don't have), does it make sense to normalize all arrays to each other >> using quantile normalization? > > Depending of what type of data, but most likely yes. If you work with > extreme data such as cancer data, the might be too many copy-number > differences for the assumptions behind quantile normalization to be > true. > >> If I do this, then instead of using a >> raw file intermediate, I could go from an abatch object directly to >> the norm files (what is the format of these files?). This is also >> appealing as gtype_cel_to_pq chokes on my CDF file, probably due to >> the mixed design. > > I can't tell you about 'abatch' objects, but I know that Affymetrix' > gtype_cel_to_pq tool is designed for the 100K SNP chips, which have > exactly 20PM and 20MM per SNP (probeset). This is not the case for > say the 500K chips. The simple reason for this assumption is that it > outputs a tab-delimited ASCII file (*.raw) with a table of rows of > equal lengths. Using tables to store CEL data with SNPs of different > lengths does not work well. > >> >> 2) Once I have norm files, I can create the theta file - but Is there >> a way to do unsupervised classification from the results in the theta >> file (that is, how do I avoid the internal regions file altogether >> or make a compatible uninformative one)? Of course, I could always >> define my own conservative decision regions in the unit square. >> >> 3) My genotyping probe-sets don't all have 20 PM probes, does RLMM >> explicitly require this? > > If you talk about the package RLMM, the answer is yes. The > method/algorithm RLMM itself works on a SNP-to-SNP bases and does not > require equally sized SNPs. > >> >> 4) I'm also interested in checking how much the various quartet >> offsets contribute to classification results. Are the 20 probes in >> the raw or norm file ordered by offset and strand? > > I did look at this many months ago and if I remember it correctly, the > answer is that the probes are ordered as they are ordered in the CDF > file and there all sense probes comes first and then the anti-sense. > However, just looking in the *.raw file, you do not know how many > sense and anti-sense probes a specific SNP has; it varies and it is > *not* the case that it is always 20-20. > > If you are going to do serious (long-term) investigation of SNP data, > I recommend you to move away from the *.raw file format; it was a > temporary solution and will soon be forgotten. It is also extremely > slow to work with ASCII files - much better to work with binary CEL > files directly. > > For low-level access to CDF and CEL data, I would recommend you to > look at the 'affxparser' package, but also the 'affyio' package. > Currently, they complement each other. The latter has been around > longer (hence probably less bugs), the former builds on top of > Affymetrix open-source libraries and also tries to minimize memory > usage by allowing you to work on a subset of probesets across > 100-1000s of CEL files. Both will allow you to pull information from > the CDF about probe distributions etc for the SNP. > > In the bigger picture, for doing RLMM and similar, I would recommend > you to look at the 'oligo' package which is under development but is > being designed for doing SNP analysis in R. You might also want to > look at the Affymetrix Power Tools (APT) (non R) which implements > BRLMM, which is an extension to RLMM that let SNPs borrow information > from other SNPs in order to get better genotype call regions. See > also CRLMM of 'oligo'. > > Cheers > > Henrik > >> >> -Amit >> >> _______________________________________________ >> 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 >> > > _______________________________________________ > 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