I have a dataset that looks something like this
The numbers describe a fold change with respect to the control (where the control is always set to 1).
Does it make sense to compute values such as averages, sums, and differences (e.g. the average fold change for treatment1)? And yes, wary that doing things on log fold-change is not a thing, but how about just fold change?
By extension, does it also make sense to compute something like Euclidean distances (e.g. fold change vector of Sample1 vs. Sample2) for clustering fold change data? I think clustering on counts/TPM makes more sense, but wasn't sure about doing it on fold changes.
Something tells me that it's not possible because the fold changes are relative values as opposed to measured numbers. I've opted for looking at correlations (e.g. Spearman) for now though; are there alternative metrics/strategies to consider?
If there are some good papers talking about clustering and manipulating fold change data I'd be equally grateful, thanks!