For example, in one experiment I have collected data (Electric current levels, a continuous variable) from 7-8 cells (replicates) which express a particular ion channel gene (Gene1). This gives me a mean current level for Gene1. I have then measured current levels from a different set of 7-8 replicate cells which also express Gene1 PLUS an activator gene (the "treatment"). Now I have a mean current value for Gene1+activator.

I repeat the two measurements for Gene2 (a different ion channel gene), again recording mean current values from 7-8 cell replicates for Gene2 alone and then for Gene2+activator.

The quantity I am interested in comparing is the %activation or fold activation caused by the activator for Gene1 versus Gene2. So, I would obtain a ratio by dividing the mean current for Gene1+activator by the mean current for Gene1 alone. This would give me the fold activation for Gene1. I would compare this to a similar ratio obtained for Gene2.

I have done some research on this and using Fieller's intervals to compute the error bars or confidence intervals seems promising. However, I don't know how to convert that to hypothesis/comparison test and get an appropriate p-value for the comparison of means being same/different. Furthermore, the best solution would also allow multiple comparisons and allow me to compare "fold-activations" for Gene1 and Gene2 and Gene3 and Gene4 at the same time.

Fieller's intervals seem like the perfect tool to compute 95% confidence intervals etc around each fold-activation but this does not lend itself to calculation of a p-value. As of now, I am reduced to insisting the lack of CI overlap clearly signals a significant difference but I know that lack of 95%CI overlap is an overly stringent comparison test which represents an alpha<0.05. I would truly appreciate any suggestions to the appropriate comparison test (single or multiple comparison, either at this point will be helpful) which would test at alpha=0.05 and yield a p-value. Thanks in advance.