In statistics and in science in general, it is always harder to convincingly show lack of effect rather than significant differences. In low throughput experiments, one can always report pvalues and show that the difference is not statistically significant but the situation is more complex (at least to me) in genome-wide studies. For instance, in the case of RNA-seq expression data, even if there is no biological differences between two conditions, there will always be some genes significantly differentially expressed because thousands of genes are tested. In such a case, one can not just say "we didn't see any significant differences between condition X and Y".
How would you illustrate and discuss such a case ? Do you have any example of publications that address this issue ?
Here are some ideas :
- Discuss that there is less DEG between the conditions X and Y than between X and Z (where there is an effect that has been biologically confirmed). However I find this a bit weak.
- Discuss that there is obviously no global differences between X and Y beside some differences that might be anecdotical.
- Show MA-plot/volcano plot and let the reader decide for himself.