here is a good overview: http://www3.bio-rad.com/B2B/vanity/gexp/content.do?BV_SessionID=@@@@1649667781.1352052928@@@@&BV_EngineID=ccccadfiemfjgeecfngcfkmdhkkdfll.0&root=/Product%20Family/GX/Home&pcatoid=-35468&ccatoid=-36539&country=US&language=English&BV_SessionID=@@@@1649667781.1352052928@@@@&BV_EngineID=ccccadfiemfjgeecfngcfkmdhkkdfll.0
here is a good overview: http://www3.bio-rad.com/B2B/vanity/gexp/content.do?BV_SessionID=@@@@1649667781.1352052928@@@@&BV_EngineID=ccccadfiemfjgeecfngcfkmdhkkdfll.0&root=/Product%20Family/GX/Home&pcatoid=-35468&ccatoid=-36539&country=US&language=English&BV_SessionID=@@@@1649667781.1352052928@@@@&BV_EngineID=ccccadfiemfjgeecfngcfkmdhkkdfll.0
All samples were collected from patient in their first phase before treated. However, if the patient were completely treated when we take the samples we will not find any RNA to be amplified by RT-PCR
I found this reference to be pretty useful when I was initally setting up my qPCR calculations:
http://pathmicro.med.sc.edu/pcr/realtime-home.htm
In terms of stats, if you just have one treatment group vs. control, I believe all you need is a simple t-test, which you can do in Excel. If your setup is more complecated than that though, say multiple different treatments, you should perform an ANOVA test, in which case you would need more powerful statistical software. I have been using GraphPad Prism: