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 sensibleĀ 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 irrelevantĀ 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 !!

Best Regards,

Konstantinos Yeles

Dear Gordon, thank you very much for your answer !! Just two quick points to mention in order to be on the "safe side":

1) About the comparing coefficients with coef=1:4--essentially, ANOVA will perform only the comparisons that have already been defined in the coefficients with makeContrasts, right ? For example, if coef=1 represents bystander samples vs control samples in 0.5h, ANOVA will NOT also perform a between coefficients comparison, correct ? I.E. coef2 vs coef4.

2) Or my above notion is incorrect, and actually ANOVA compares in any of the means in each contrast (defined above-shown in the previous post) in each gene is significantly higher("different") from the other three ?

Please excuse for my new question, but this is the point that confuses me the most for the specific interpretation !!

It depends on how the coefficients are defined, i.e., what they mean in the context of the fitted model. Sorry, but I only want to answer the question you asked here. I don't have time to read your earlier post and the long question and answer series with Aaron.