Briefly explained for those who do not wish to read the resources: once the data is downloaded, firstly the function
TCGAanalyze_Preprocessing is applied in order to eliminate any potential outliers. Afterward, normalization is performed using the
TCGAanalyze_Normalization function, which uses an EDAseq method to normalize by gene length. Then, the
TCGAanalyze_Filtering function is applied (the method is 'quantile' and the cut value is set at 0.25, not too sure what this part is about, to be honest). Finally,
TCGAanalyze_DEA offers two methods for differential expression analysis: limma and edgeR. The default method is edgeR and it's the one used in both of the links I cited.
My question is: in order to use limma should I follow the exact same workflow and just change the method parameter of the function? or do I have to change anything in the process (normalization, filtering, or anything else) in order to use limma? Sorry if the question is too dumb but I'm a beginner and while I am aware that the limma and edgeR methods are different, I am not sure if the input data should differ or not. Thanks in advance!