[BioC] Missing values - metaArray, GeneMeta, RankProd, metahdep
john.r.stevens at usu.edu
Wed Aug 10 23:48:00 CEST 2011
My metahdep package doesn't require all datasets to come from the same array version -- so while the package's tutorial vignette doesn't explicitly say anything about missing values, the example there does allow for missing values in this sense (some genes not represented in all datasets).
As to your second question, if you're combining studies with fundamentally different platforms (Affy for some, two-color for others, for example), you'll need to be careful to define an effect size (or some measure of differential expression) that means the same thing on all platforms, and for which you can calculate a meaningful standard error. I think trans-platform cases like this lead to the appeal of alternative scale methods such as those in the metaArray package (which uses the 'probability of expression' or POE scale).
From: bioconductor-bounces at r-project.org [bioconductor-bounces at r-project.org] on behalf of mjonczyk at biol.uw.edu.pl [mjonczyk at biol.uw.edu.pl]
Sent: Tuesday, August 09, 2011 1:45 PM
To: bioconductor at r-project.org
Subject: [BioC] Missing values - metaArray, GeneMeta, RankProd, metahdep
Dear List Members,
I'd like to perform a metaanalysis for few (minimum three) datasets.
Some of them are one channel (Affymetrix), some two-colour data.
Obviously, when I join, say three studies I will have big dataset in
which some data will be missing. Just because some probes will be present
in two experiments and absent in third experiment. I'd prefer not to
use *only* probes which are present on all arrays, but also this which
are present on two arrays.
My *question* is: which package (metaArray, GeneMeta, RankProd, metahdep)
could handle metaanalysis with missing data - in tutorials/manuals it is
not stated if missing data is allowed. Although metaArray cites MergeMaid
package as a example software for merging data with non-overlapping genes.
Second *question* which of the above package will best handle dataset
from merged two-colour and affymetrix studies?
I'd also be grateful for directions to additional tutorials, materials, etc.
Maciej Jończyk, MSc
Department of Plant Molecular Ecophysiology
Institute of Plant Experimental Biology
Faculty of Biology, University of Warsaw
02-096 Warszawa, Miecznikowa
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