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) [[alternative HTML version deleted]]

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/do c/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) ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}