Hi Nur,

This is one of those "don't rely on blindly following methods" issues, more than anything else.

A common problem I have noticed with ddCt methods is that in general, people handle logarithms badly, and often get very lost, especially once various values have been subtracted from each other.

What you are describing here are simply two ways of looking at the exact same number, not two ways of analysing data. It appears you are unsure as to what that number means, though (again, common problem with ddCt methods). And since people seem to do ddCt methods differently anyway, I can't tell you a priori what your answer should be. I can't even be sure you're applying the analysis correctly.

So, look at your raw data first, and get an idea as to what you SHOULD be seeing. If you have high Ct values for your GOI in your treated samples, and low Ct values for your GOI in your untreated samples, this suggests a downregulation following treatment (more cycles needed = less starting transcript). If your effect is strong enough (and your technique is good enough) you should be able to spot the likely result even before normalising.

My personal preference is to avoid dCt methods entirely and instead convert to relative quantities as quickly as possible (something I have ranted on about at length previously), as RQs convert all of your numbers into nice linear values you can easily compare, pretty much from the outset.

I've included an excel file, using made up numbers, showing RQ methodology and dCT methodology: note both give the same answer, because the answer IS the answer, it's simply a question of knowing what the numbers mean at each step.