See BioGazelle -- software by Jan Helleman's and Jo Vandesompele. Or Dr. Mikael Kubista's software. There are others too... but $ and licensing is always involved for all. Some machine's also provide this at this point as well. I still use multiple Excel sheets as you do...

@Mohamed Mahdy:

Assuming you have also run an other target or targets of interest, you can use a very simple version of the efficiency-corrected Pfaffl equation: Fold change = [targetEamp^(avgCqcontrol-avgCqtreated)]/[B-actinEamp^(avgCqcontrol-Cqtreated)].

Assuming you are using log base 10 math:

targetEamp = 10^(-1/slope of target standard curve)

and

B-actinEamp = 10^(-1/slope of B-actin standard curve)

That's the best short answer i can conjur at the moment.

Samples and calibrators should both be normalised to the reference gene, then the normalised sample values are again normalised to the already reference-gene-normalised calibrator value.

Your colleague in the lab has chosen the highest expressing sample as the calibrator - which sounds good. What you don't want, is to choose a non-expressing sample as the calibrator. Everything is relative to whatever you choose as the calibrator in the [exponential amp efficiency to the delta delta Cq method] for computing fold change -- which is the Pfaffl method stated above.

To make things more complex, 3 ref genes are advised. For which the attached equation (in my next post here ) can theoretically be used.

Hope this helps.

The 3 reference gene normalization equation is attached here.