Arkady wrote: > On Tue, Jul 22, 2008 at 6:18 PM, James MacDonald <jmacdon at="" med.umich.edu=""> > wrote: > >> John wrote: >> >>> 1. The CEL files contain the names of the original CDFs. How do I >>> translate those to the names of the custom CDFs? Is there some way to >>> establish a mapping? >>> >> See ?ReadAffy, specifically the cdfname argument (you _did_ already read >> the help, no?). >> > > (Yes, of course. And the vignettes.) > > I'll try to clarify. Is there a way to tell from the CDF requested in the > CEL which *custom* CDF it should load instead? For example, how do I know > which (of the many CDFs for the Affy 1.0R, 2.0R, 21/22 arrays) custom CDF is > the replacement for the default CDF Wgc_Universal_fe1? No. You just have to know enough about the array you are using to know which custom one is applicable. > > >> 2. How do I deal with multiple CDFs for a single experiment? Do I load >>> each of my 3452 files separately, specifying the CDF each time? >>> >> Again, see ReadAffy(). And good luck reading in 3452 celfiles unless you >> have more RAM than the NSA (or Google - not that there is much difference >> ;-D) >> > > Yes, Google = NSA, and NASA, too. Soon they'll put NAAAAAAAAAASA at the > bottom of search results instead of Goooooooooogle. > > So you're saying that instead of calling ReadAffy() with no or few args on > the current working directory, I should call ReadAffy for each CEL > separately, specifying the custom CDF manually? (I guess I was wondering if > there was an option like usecustom=TRUE.) Calling ReadAffy() on individual celfiles isn't likely to be helpful if you are intending to process them together. You will want to background correct and normalize things in batches. > > Trying to increase my understanding: > If I've got three replicates of an array that all come from the same > biological replicate, should those be read in a single call to ReadAffy? Yes. > > > >> 3. What about the probe packages? Is there a unified package that >>> contains both pieces (CDF and probes) of information? >>> >> See the oligo and pdInfoBuilder packages, which is what you should be using >> for tiling arrays anyway (or the affyTiling package). >> > > I'm getting: > package 'affyTiling' is not available. Well, that shows my ignorance of the package name - it _is_ tilingArray. > > I do have package tilingArray, but the vignettes there don't really answer > my question. Is there somewhere else I should look? Specifically, I'd like > help on how best to load this data into R using existing (Yoda) methods in > Bioconductor--if it's possible. > > The pdInfoBuilder vignette is not terribly helpful either. Is there some > document that explains how all of this stuff gets tied together, and who > should be using which package for what application? I'm really having a > little trouble keeping track. Well, that's the trouble with being on the bleeding edge of things. I have worked in an Affy core for over 6 years now, and have *never* seen a tiling array, unless you count CHiP-chip stuff with the promoter arrays. I think they might be nice for some things, but they are pretty hard to sell to the Standard Issue Biologist (SAB). In an Open Source project like Bioconductor, the things that get the attention are the things that people use most/ask questions about. Since tiling arrays seem to be a bit rare, I don't think they have got as much attention as the standard expression arrays. Best, Jim > > >> 4. Why aren't the CDFs for the human tiling arrays made available >>> through Bioconductor? >>> >> Mainly because the demand is so low, and there isn't a really easy way to >> analyze them at this time. The barrier to entry is so high that most people >> who analyze these things are on the bleeding edge anyway, so they typically >> don't need our help. Plus, it somehow never occurred to me to build them ;-D >> > > Yay bleeding edge. > > Cheers, > John > > [[alternative HTML version deleted]] > > _______________________________________________ > 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 -- James W. MacDonald, M.S. Biostatistician Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623

Hi Steve, Steve Lianoglou wrote: > Hi Naira, > > On Jul 28, 2008, at 9:57 AM, Naira Naouar wrote: > >> Personally, I have been working on Arabidopsis Thaliana 1.0R tiling >> array and I have produced my own CDF for this array. The way I did it >> is explained here: >> http://wiki.fhcrc.org/bioc/DetailedScheduleTentative?action=AttachF ile&do=get&target=Lightning-Naouar.pdf >> > > Thanks for the slides! I've actually done something very similar for > the Drosophila 1.0R tiling array in terms of realigning probes and > annotating as exon/ambiguous-exon (ie, an exon in only 1 isoform of a > transcript)/intron/intergenic/etc. The only question I have is what > did you then use to get it back into the expected CDF format for > seamless use in Bioconductor? > > Sorry if I'm missing something, but I haven't come across a way to > create a custom cdf w/o affy's bpmap and cif files ... did you create > your own bpmap from your annotations (or something)? > Actually, I have created a CDF package for Arabidopsis 1.0R Tiling array that is available here: ftp://ftp.psb.ugent.be/pub/nanao/athtiling1.0rcdf.tar.gz doing the following: 1. For each probe that I selected for a specific gene (AGI code), I kept track of the PM and MM 'xy' position on the array (via the bpmap file provided by Affymetrix for the tiling array). 2. I created 2 functions to convert the 'xy' positions on the array to the 'i' positions that will be on your CDF. The basic functions are the following: xy2i=function(x,y) {y*DIM+x+1} i2xy = function(i) {r=cbind((i-1)%%DIM,(i-1)%/%DIM); colnames(r)=c('x','y'); return(r)} where DIM corresponds to the dimension of the tiling array. 3. Then, I created an environment containing the PM and MM positions for each gene (like in a normal CDF). You should have an environment (ex: athtiling1.0rcdf) where the labels are the names of the genes and the content is a matrix with 'i' positions of the PM and MM indexes for each. (the i positions are simply calculated with the previous xy2i function. Something like: ## Create environment myverynicecdf = new.env() ## For each gene create a matrix of 2 columns (pm, mm) and x rows (corresponding to the i position on the array) and matrix_PM_MM = blabla assign(GENE_NAME, matrix_PM_MM,envir=myverynicecdf) In the end, you should end up with the following: ## Exemple > get("AT1G01020",athtiling1.0rcdf) pm mm [1,] 3984824 3987384 [2,] 1022692 1025252 ... [10,] 511312 513872 [11,] 5051196 5053756 4. I saved this environment as a package. The code should resembles the following: package.skeleton(name = "myverynicecdf", list = c("myverynicecdf", "xy2i", "i2xy"), path = "my/path/") There are 2/3 changes to do in the package (folder and files) created that are basically the normal things for R. (If you have any questions about that I can also help). Then, you build your package and basically you have your drosophila CDF library that can be used in R :) I hope I am clear but you can always ask me questions about this. Naira -- ================================================================== Naira Naouar Tel:+32 (0)9 331 38 63 VIB Department of Plant Systems Biology, Ghent University Technologiepark 927, 9052 Gent, BELGIUM nanao at psb.ugent.be http://www.psb.ugent.be