That's a good idea as you can address the sample selection bias. You might also be interested in reading the following papers if you haven't done so already (there are other on a similar topic): Michiels S, Koscielny S, Hill C. Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet. 2005 Feb 5-11;365(9458):488-92. PMID: 15705458 Ein-Dor L, Kela I, Getz G, Givol D, Domany E. Outcome signature genes in breast cancer: is there a unique set? Bioinformatics. 2005 Jan 15;21(2):171-8. Epub 2004 Aug 12. PMID: 15308542 Regards, Adai Eleni Christodoulou wrote: > I was actually thinking of creating bootstrap samples and applying > univariate cox models in each of them. Then > I would sdelect the significant genes for each bootstrapping. I would > declare the common genes among the bootstrap samples as actually > significant... > > On Thu, Feb 14, 2008 at 5:46 AM, Adaikalavan Ramasamy < > ramasamy at cancer.org.uk> wrote: > >> Eleni, >> >> Note that some of the genes that declared as significant in a univariate >> analysis could be highly correlated. Thus, some of the selected genes >> would not be informative in building the multivariate model. >> >> You might want to consider reducing the dimensionality by first grouping >> the genes into clusters with similar patterns. There are many techniques >> but the one I can recall now is one of the earliest called gene shaving. >> >> Or you can pre-select some genes based on variability measures etc. >> >> Regards, Adai >> >> >> >> Eleni Christodoulou wrote: >>> Hi, >>> >>> Thanks for the replies. I will probably try to perform survival analysis >> on >>> each of the genes to get gene-wise p-values and then select the most >>> significant (the ones that are below a certain p-value) and proceed to a >>> full cox regression using the significant genes. Do you think that this >>> makes sense? >>> >>> Thanks a lot, >>> Eleni >>> >>> On Feb 13, 2008 2:11 PM, <phguardiol at="" aol.com=""> wrote: >>> >>>> Hi, >>>> wouldnt it make sense to first have data reduction dimensionality >> before >>>> undergoing such survival analysis ? Certainly, some of your genes have >>>> similar expression profiles across samples...? >>>> Best, >>>> Philippe Guardiola >>>> >>>> >>>> -----E-mail d'origine----- >>>> De : Ramon Diaz-Uriarte <rdiaz at="" cnio.es=""> >>>> A : bioconductor at stat.math.ethz.ch >>>> Cc : Eleni Christodoulou <elenichri at="" gmail.com=""> >>>> Envoy? le : Me, 13 F?vrier 2008 11:23 >>>> Sujet : Re: [BioC] Cox Model >>>> >>>> Dear Eleni, >>>> >>>> >>>> You are trying to fit a model with 18000 covariates but only 80 samples >> (of >>>> which, at most, only 80 are not censored). Just doing it the way you >> are >>>> trying to do it is unlikely to work or make much sense... >>>> >>>> >>>> You might want to take a look at the work of Torsten Hothorn and >> colleagues on >>>> survival ensembles, with implementations in the R package mboost, and >> their >>>> work on random forests for survival data (see R package party). Some of >> this >>>> funcionality is also accessible through our web-based tool SignS >>>> >>>> (http://signs.bioinfo.cnio.es), which uses the above packages. >>>> >>>> >>>> Depending on your exact question, you might also want to look at the >> approach >>>> of Jelle Goeman, for testing whether sets of genes (e.g., you complete >> 18000 >>>> set of genes) are related to the outcome of interest (survival in your >> case). >>>> Goeman's approach is available in the globaltest package from BioC. >>>> >>>> >>>> Hope this helps, >>>> >>>> >>>> R. >>>> >>>> >>>> >>>> On Wednesday 13 February 2008 08:10, Eleni Christodoulou wrote: >>>> >>>>> Hello BioC-community, >>>>> It's been a week now that I am struggling with the implementation of a >> cox >>>>> model in R. I have 80 cancer patients, so 80 time measurements and 80 >>>>> relapse or no measurements (respective to censor, 1 if relapsed over >> the >>>>> examined period, 0 if not). My microarray data contain around 18000 >> genes. >>>>> So I have the expressions of 18000 genes in each of the 80 tumors >> (matrix >>>>> 80*18000). I would like to build a cox model in order to retrieve the >> most >>>>> significant genes (according to the p-value). The command that I am >> using >>>>> is: >>>>> test1 <- list(time,relapse,genes) >>>>> coxph( Surv(time, relapse) ~ genes, test1) >>>>> where time is a vector of size 80 containing the times, relapse is a >> vector >>>>> of size 80 containing the relapse values and genes is a matrix >> 80*18000. >>>>> When I give the coxph command I retrieve an error saying that cannot >>>>> allocate vector of size 2.7Mb (in Windows). I also tried linux and >> then I >>>>> receive error that maximum memory is reached. I increase the memory by >>>>> initializing R with the command: >>>>> R --min-vsize=10M --max-vsize=250M --min-nsize=1M --max- nsize=200M >>>>> I think it cannot get better than that because if I try for example >>>>> max-vsize=300 the memomry capacity is stored as NA. >>>>> Does anyone have any idea why this happens and how I can overcome it? >>>>> I would be really grateful if you could help! >>>>> It has been bothering me a lot! >>>>> Thank you all, >>>>> Eleni >>>>> [[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 >>>> -- >>>> >>>> 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}} >>>> >>>> _______________________________________________ >>>> >>>> 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 >>>> >>> [[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 >> >> >

On Fri, Feb 15, 2008 at 10:14 AM, Eleni Christodoulou <elenichri at="" gmail.com=""> wrote: > Hi again, > > I am trying to create bootstrap samples of my training set and conduct a > univariate cox analysis on each of them. I tried both in Linux and windows. > After 17 samples in Linux and 8 in Windows the process is killed. Does > anyone know why this happens? Hi, Eleni. You will probably need to post the code that you are using as well as any error messages. However, I might venture a guess that this is a memory-related issue. Sean