



Hi Heyi,

You may want to try the GAGE method. GAGE
does differential expression tests on gene sets based on one-on-one comparison between
samples. This special approach together with carefully designed NA handling utility
makes GAGE tolerant to missing values (NAs). You don’t really have to remove
genes with missing values. Actually it is better not removing genes with
missing values, as the existent expression values for these genes can be fully
used to make the analysis more sensitive.

The gage package is newly available
with bioconductor 2.7 at http://bioconductor.org/help/bioc-views/release/bioc/html/gage.html.
GAGE method has been published at http://www.biomedcentral.com/1471-2105/10/161.
Let me know if you have other questions or need help. Thanks!

Weijun



--- On Wed, 11/3/10, heyi xiao <xiaoheyiyh@yahoo.com> wrote:

From: heyi xiao <xiaoheyiyh@yahoo.com>
Subject: gene set enrichment analysis with missing values
To: bioconductor@stat.math.ethz.ch
Date: Wednesday, November 3, 2010, 10:19 PM





Dear all, 

I have an expression data matrix with
genes as rows and samples as columns. Many genes (~30%) have missing values in
one or more samples. I would like to do a gene set enrichment type of analysis.
Shall I remove the whole rows for all these genes? I am a little concerned that
this may affect the testing power when so many genes are missing from the analysis.
Is there any better way to go? Any suggestions would be appreciate. Thank you! 

Heyi 




      


      
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