Dear Bioconductor Community,
I would like to ask a specific question regarding the interpretation of an "ANOVA" approach in limma and topTable function. In detail, in a previous post i have created ( C: Questions about complex design in limma regarding an agilent microarray dataset ) , Aaron helpfully mentioned the difference about dropping separately each coefficient in topTable about a statistical comparison (i.e. coef=1) and by dropping for instance coef=1:4, which essentially performs an ANOVA test checking for DE in any of my comparisons. Thus, my crucial (and might naive question) is the following: is it sensible to get a significantly greater number of DE genes in my ANOVA implementation, than in the sum of dropping each coefficient separately ? And this could be probably due to the "nature" of the ANOVA testing ? In other words, what is the crucial difference in the computation of statistics and DE genes when moving from i.e. coef=2 (a specific comparison) to coef=1:4 ? For instance, the ANOVA approach also tests for difference in means in coef=1 versus coef=2 ? Or this is irrelevant as all the mentioned comparisons have been specified in the makeContrasts function? (above link for code).
Please excuse me for this beginner question, but I'm a newbie in R/statistics and this specific part is very crucial !!