filtering on variance
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Last seen 6.8 years ago
I was planning to filter on summarized/normalized Affy expression data and keep the genes in the high 50% of variance, but saw the recent paper on an algorithm called FLUSH in NAR that filters on probe level data. What is the recommendation of the collective BioC consciousness about the stage in data analysis that one should filter data using variance? Dennis Burian, Ph.D. Functional Genomics Group Civil Aerospace Medical Institute, AAM-610 6500 S. MacArthur Blvd. Oklahoma City OK 73169 405-954-6087 dennis.burian at
probe affy probe affy • 1.3k views
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Last seen 20 minutes ago
WEHI, Melbourne, Australia

How to filter depends on your purpose. Filtering on variance might be a good way to select genes for a PCA plot for example. If however your intention is to do a DE analysis using limma then you should absolutely not be filtering on variance. For limma (or for any program that borrows information between genes) you should instead filter on expression level or not at all.


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