7.8 years ago by
Washington University, St Louis, USA
Here are a few possibilities that you might consider:
- Some kind of outlier analysis on the difference (or fold-change) between treatment and reference. You might hope that important biological differences would stand out compared to differences arising from just sample-to-sample variation.
- You could pretend that these are RNA-seq samples and concert to count-based data. People do statistics (e.g., Fisher Exact) on a single sample vs single sample in the RNAseq field all the time.
- You could calculate the change gene rank in treatment vs reference. Then you could set up a random permutation test where you randomly assign ranks to genes by drawing from the reference and treatment and see if there were any unusually "lucky" jumps in rank in the actual data compared to random simulations.
Just to be clear. These are all really bad options. Trying to come up with statistics with no replicates will likely just get you smacked down by a reviewer if you ever attempt to publish. Your original idea to just use fold-changes is probably best. Although I would also consider the relative expression of genes. You might put more weight on a gene with FC=2 if it went from 10,000 to 20,000 than if it went from 1 to 2. Any candidates you identify will have to be validated before they are worth anything at all. The suggestion #1 above could also help you identify fold-change values that really stand out. Good luck!