[R] Very large matrices for very large genome
Duncan Murdoch
dmurdoch at pair.com
Mon Apr 12 04:47:33 CEST 2004
On Sun, 11 Apr 2004 19:15:08 -0700 (PDT), you wrote:
>Hello,
>
>I am using R to look at whole-genome gene expression data. This means
>about 27,000 genes, each with a vector of numbers reflecting expression at
>different tissues and times.
How long is that vector? Presumably shorter than 27000.
>I need to do an all against all co-expression
>calculation (basically, just calculate Pearson's r for every gene-gene
>pair). I try to store the result of such a thing in a 27000x27000 matrix,
>but r seems not to like allocating such a large beast. Any
>recommendations?
If you have fewer than 27000 cases, then the correlation matrix is not
full rank, and could be summarized in much less space. For example,
if you have 100 cases, then a 100x100 matrix will give the correlation
structure, and a 26900x100 matrix would give the weights for the rest
of the genes.
(It's late, so I might wrong about this, but I don't think so.)
To calculate those matrices, just pick the first 100 genes to use for
the correlation matrix (assuming you get a full rank matrix that way),
then regress each of the others onto those.
Duncan Murdoch
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