R help - Feb 2008 - Cox model

Hello R-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]]

Hi Eleni,

I am not an expert in R or statistics but in my opinion you have too 
many regressors compared to the number of observations and that might 
be the reason why you get the error. Others might say better but as 
far as I know, having only 80 observations, it is a good idea to first 
filter your list of variables down to a few tenths.


HTH

David
> Hello R-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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> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-
guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

What you appear to want are all of the univariate models.  You can get this 
with a loop (and patience - it won't be fast).
 
ngene <- ncol(genes)
coefmat <- matrix(0., nrow=ngene, ncol=2)
for (i in 1:ngene) {
	tempfit <- coxph(Surv(time, relapse) ~ genes[,i])
	coefmat[i,] <- c(tempfit$coef, sqrt(tempfit$var))
	}


  However, the fact that R can do this for you does not mean it is a good idea.
In fact, doing all of the univariate tests for a microarray has been shown by 
many people to be a very bad idea.  There are several approaches to deal with 
the key issues, which you should research before going forward.
  
  Terry Therneau