Hi Hollis

At the moment the only way you can perform these analyses is on a complete
matrix of genomic measurements. Depending on the structure of the missing
data, one approach is to use the impute package:

http://www.bioconductor.org/packages/release/bioc/html/impute.html

to impute the missing values before performing Combat/SVA.

Hope this helps!

Jeff




On Mon, Sep 23, 2013 at 4:08 PM, Hollis Wright <wrighth@ohsu.edu> wrote:

> Hi, all; I have a 224-gene OpenArray RT-PCR data set that I'm analyzing,
> and I strongly suspect one of the runs is having a batch effect on the
> entire experiment from EDA and analysis of the quality values.
> Unfortunately, this means that there are a number of missing values in the
> matrix (though not all from the suspect run). If I pull the genes
> containing these values, SVA/ComBat works nicely to clean up the data that
> remains; if I don't, it fails with a "system is exactly singular" Lapack
> routine error. Is there any way to force these functions to use something
> like the "pairwise.complete.obs" from cor to work around the missing
> values, or is my only option to drop genes?
>
> Hollis Wright, PhD
> Ojeda Lab, Division of Neuroscience
> Oregon Health and Science University
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