On Wed, 13 Feb 2008, Wang, Zhaoming (NIH/NCI) [C] wrote:
>
> Try EIGENSTRAT http://www.nature.com/ng/journal/v38/n8/abs/ng1847.html
The same approach as EIGENSTRAT is pretty straightforward in R.
You need to create the covariance matrix of people (rather than of SNPs)
for the 0/1/2 genotype at each SNP and take the principal components of
that matrix.
In this case the number of individuals is small enough that you should be
able to create the covariance matrix directly by matrix operations. In
larger data sets where the entire data matrix doesn't fit in memory, you
need some sort of double loop.
-thomas
> Zhaoming
> -----Original Message-----
> From: SNN [mailto:[EMAIL PROTECTED]
> Sent: Wednesday, February 13, 2008 9:14 PM
> To: r-help@r-project.org
> Subject: [R] Principal component analysis PCA
>
>
> Hi,
>
> I am trying to run PCA on a set of data with dimension 115*300,000. The
> columns represnt the snps and the row represent the individuals. so this
> is what i did.
>
> #load the data
>
> code<-read.table("code.txt", sep='\t', header=F, nrows=300000)
>
> # do PCA #
>
> pr<-prcomp(code, retx=T, center=T)
>
> I am getting the following error message
>
> "Error: cannot allocate vector of size 275.6 Mb"
>
> I tried to increase the memory size :
>
> "memory.size(4000)"
>
> but it did not work, is there a solution for this ? or is there another
> software that can handle large data sets.
>
> Thanks
>
>
> --
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> 09.html
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>
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> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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>
Thomas Lumley Assoc. Professor, Biostatistics
[EMAIL PROTECTED] University of Washington, Seattle
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