Dear Dr  Arief Gusnanto,

Thank you very much for the information. I will certainly have a look at
this paper. The univariate analysis that you suggest (compute the cox model
coefficients for each gene separately) is a very good idea indeed.

Thank you once again,
Eleni

On Feb 13, 2008 11:40 AM, A Gusnanto <arief@maths.leeds.ac.uk> wrote:

> Hi Eleni,
>
> The analysis you have in mind has been described in Pawitan et al.
> Statistics in Medicine 2004; 23:1767–1780 (DOI: 10.1002/sim.1769).
>
> Prof. Pawitan gave me the R codes for the analysis long time ago, but I
> can't find it in my computer at present. I will try to look for it in my
> old archives, and if I find it, I'll let you know.
>
> If you are interested to identify significant genes using this type of
> analysis, no gene will turn up significant (in the sense of having
> gene-wise 95% CI away from zero). This is due to the limited number of
> samples you use in estimating 18,000 parameters (sparse
> variance-covariance matrix involved). Further details are described in
> the paper.
>
> Although I have never tried this, my suggestion would be to perform
> survival analysis on each of the genes to get gene-wise p-values, and
> control for false discoveries using FDR.
>
> Regards,
>
> Arief
>
> --
> Dr. Arief Gusnanto
> Dept. of Statistics
> University of Leeds
> Leeds LS2 9JT
> United Kingdom
> Phone +44 113 3435135
> Fax   +44 113 3435090
> Email arief@maths.leeds.ac.uk
>
>
> On Wed, 2008-02-13 at 09:10 +0200, 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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>

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