[BioC] Cox Model

[Ramon Diaz-Uriarte]

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]]
>
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-- 
Ramón Díaz-Uriarte
Statistical Computing Team
Centro Nacional de Investigaciones Oncológicas (CNIO)
(Spanish National Cancer Center)
Melchor Fernández 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)



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