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 08-05-2014, 09:12 AM #3 vkartha Member   Location: Boston Join Date: Feb 2012 Posts: 28 On a similar note - I am trying to perform differential expression contrasts for 2 different populations - one resistant to a given drug and one wild type (i.e. sensitive). Within each population, I have samples that were treated at just one given concentration of the drug, and samples that were treated with a drug vehicle (control). So the basic study design for this experiment is : Population A (resistant):Treatment (Y) vs Control (X) Population B (wild type): Treatment (Y) vs Control (X) In addition to population and treatment variables, I also have to adjust for a very strong batch effect (3 batches). So the formula for my design matrix at the moment is: ~ Batch + Population + Treatment + Population:Treatment and the resulting design matrix looks like so: (Intercept) Batch2 Batch3 PopulationB TreatmentY PopulationB:TreatmentY Sample_1 1 0 0 0 0 0 Sample_2 1 0 0 0 0 0 Sample_3 1 0 0 0 0 0 Sample_4 1 1 0 0 0 0 Sample_5 1 1 0 0 0 0 Sample_6 1 0 0 0 1 0 Sample_7 1 0 0 0 1 0 Sample_8 1 0 0 0 1 0 Sample_9 1 1 0 0 1 0 Sample_10 1 1 0 0 1 0 Sample_11 1 1 0 1 0 0 Sample_12 1 1 0 1 0 0 Sample_13 1 0 1 1 0 0 Sample_14 1 0 1 1 0 0 Sample_15 1 0 1 1 0 0 Sample_16 1 1 0 1 1 1 Sample_17 1 1 0 1 1 1 Sample_18 1 0 1 1 1 1 Sample_19 1 0 1 1 1 1 Sample_20 1 0 1 1 1 1 What I don't understand is given this complete linear model formula, how do I specify contrasts (both edgeR and DESEq) to compare the two treatment methods WITHIN each population? i.e. I want to test the effect of treatment separately for population A and for population B, adjusting for Batch effect, using this complete model. Specifying coef=4 would give me the overall difference in expression between the two populations and specifying coef=5 will give me the overall difference in expression between the two treatment methods, but that is less interesting a question given my study design Any help would be greatly appreciated Regards