I am new to RNAseq and I am trying to understand some of the theory behind the various analysis methods.
Once you have raw read counts, you can pass these values to a package like DESeq or EdgeR for differential expression analysis. These packages will filter low counts, normalize, perform modeling based on a distribution, and report a fold-change along with some kind of value like an FDR cut-off.
However, you could also normalize the raw counts, filter very low counts/0 counts, average across replicates, perform additional filtering for replicates that vary greatly in counts, and then divide your filtered counts for the experimental sample over a control sample. This would give you a "fold-change over baseline". This very simple approach does not use a model or have any kind of associated FDR.
My question(s): What is the difference between these two different approaches and what do they tell you (or what is the limit of what they can tell you)? Is one "more valid" than the other?