Thank you for answering our previous questions. We've made progress addressing our shared controls and "model matrix not full rank" issues. As a reminder, we had 8 chemicals (plus control) tested at 4 concentrations both with and without estrogen (E2). We had three biological replicates and three technical replicates per plate. We assigned samples labeled "control" to each chemical and assigned a concentration of 0 uM.
We are mostly interested in the three way interaction between chemical, concentration and E2. We've explored two ways of doing this.
1) Michael Love suggested the following:
design= ~ bio_rep + E2 + E2:Conc_uM + E2:new_chem:Conc_uM
2) we have also used the paste0 command to create a mega-variable that combines chemical, concentration and E2.
design ~ bio_rep + mega_variable
We are getting very different results with these two approaches. Can you articulate what the differences might be?
As a second question, we've also seen two different syntax styles for contrast statements:
1) Tam_results_1 <- results(ddsColl, contrast= list(c("mega_varTam_0.1uM_E2_0","mega_varcontrol_0uM_E2_0")),alpha=0.05)
2) Tam_results_2 <- results(ddsColl, contrast= (c("mega_var","Tam_0.1uM_E2_0", "control_0uM_E2_0")),alpha=0.05)
These two yield different results. When would each be appropriate to use?
Thank you in advance,