Hi Daniel,

I am the maintainer of the pcaMethods package. The package provides several
PCA implementations that can handle missing data, so this should solve your
problem. Maybe you want to have a look at the ppca (probabilistic pca), from
my experience it produces nice results.
You should take care about the amount of missing data, or or rows / columns
that
contain large numbers of missing values. For these the estimation may not be
very accurate (as there is little information to base the estimate on).
Better remove
them first...
If there are further questions I will be happy to answer them.
There will be a new release of the package coming with the new Bioconductor
release. This version contains many improvements, maybe you want to have a
look.

Regards,
Wolfram


On 4/10/07, Daniel Brewer <daniel.brewer@icr.ac.uk> wrote:
>
> I have an expression matrix that contains NA values.  What is the best
> way to get:
> 1) prcomp()  (Exits with "Error in svd(x, nu = 0) : infinite or missing
> values in 'x'")
> 2) cor() (Exits with "Error in cor(exprs(Sperger)) : missing
> observations in cov/cor")
> to work with this type of matrix.
>
> Many Thanks
>
> Dan
>
> --
> **************************************************************
> Daniel Brewer, Ph.D.
> Institute of Cancer Research
> Email: daniel.brewer@icr.ac.uk
> **************************************************************
>
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