What is the difference between calculating fold-changes from normalized counts v. fitting to a model?

Question

[snamjoshi87]

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?

Answer 1

[Devon Ryan]

Fold-change alone isn't a reliable indicator of significance, which is why a statistical model is used. This is the same reason you do a T-test (or similar) instead of just reporting the ratio of two groups of measurements. BTW, it's exactly the FDR (well, adjusted p-value) that everyone wants.

[spvensko]

I believe it's implied in your response, but it's also worth explicitly pointing out significant p-values do not necessarily imply biologically significant or interesting differences.

[Devon Ryan]

Definitely, statistical significance isn't biological relevance. The strategy is to filter by both adjusted p-value and fold-change (i.e., the fit coefficient from the model). I should note that fitting a model has the benefit that you can do various types of shrinkage (aka, regress with a prior distribution) and profile out things like dispersion.