[BioC] Design question: How to account for dependent samples?
Gordon K Smyth
smyth at wehi.EDU.AU
Sun Feb 24 01:38:36 CET 2013
Dear Moritz,
See Section 8.7 "Multi-level Experiments" in the limma User's Guide:
http://www.bioconductor.org/packages/2.11/bioc/vignettes/limma/inst/doc/usersguide.pdf
Your experiment is a bit simpler than the example because you have only
one factor other than patient.
Best wishes
Gordon
--------------- original message -----------------
[BioC] Design question: How to account for dependent samples?
Moritz Kebschull endothel at gmail.com
Thu Feb 21 11:43:44 CET 2013
Dear list,
I am looking at a microarray dataset that consists of 'healthy' and
'diseased' samples from patients with two different diagnoses.
We have several 'diseased' samples per patient. For many, but not all
patients, a single healthy sample exists (therefore, I cannot do paired
analyses within individual patients).
Thus far, since the multiple samples per patient are dependent on each
other, we had aggregated them into a single 'diseased' sample mean for
each patient.
edata_diseased_aggregated <- sapply(unique(patnumbers),
function(i)rowMeans(edata_diseased[, patnumbers==i]))
The design was basically
design = cbind(Cond1 healthy, Cond1 diseased, Cond2 healthy, Cond2
diseased)
with the following contrasts
contrastsMatrix=makeContrasts("C1d-C1h", "C2d-C2h", "C1h-C2h", "C1d-C2d",
levels=design)
This approach does, however, strongly reduce the power of the comparison.
I was wondering whether aggregation was in fact the correct thing to do
here.
What about a design that factors in the multiple samples per patient,
similar to technical (=within patient) and biological (=several patients
with the same diagnosis) replicates? How would you suggest to implement
this here?
Many thanks,
Moritz (Univ. of Bonn, Germany)
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