I have got ct values for Experimental gene in cancer samples, GAPDH in cancer samples, Experimental gene in control and GAPDH in control.Now I know about the double Delta Ct method. We will get the fold change. But I need to know which tests can I use on this data. I am using Graphpad Prism. Also I need to know that if For example I use an unpaired T-test how will I put the data. Should I only put the Ct values of experimental gene of both cancer and control or should I put the Delta Ct values. How will I get the graphs for fold change?
Dear Muhammad,
in order to analyze your data I recommend to follow the algorithm:
Now you need to analyze this in Prism.
Have fun.
Yeah, I forgot to tell about graphs. So, GraphPad Prism is the best tool that I ever used for making graphs, it has a huge variety of instruments for customizing your pictures. And the 'Magic' button is just awesome.
It is better not to try to describe all the features of Prism Graph tool in words, but better to watch videos and 'play' with the program yourself. First, for making Bar graphs (what you need for your data), you could check this video
https://youtu.be/V6Hc3Mc0pE0
And here you can find some words about the 'Magic'
http://www.graphpad.com/guides/prism/7/user-guide/index.htm?use_the_wand_to_repeat_analyse.htm
Please notice, in order to make your graphs scientifically correct, if your data are normally distributed, you put Mean±SEM (or Mean±SD) on your graphs; if your data are non-normally ditributed you make your graphs as Median and inter-quartile range.
Best wishes,
Alexander
https://www.researchgate.net/post/How_do_you_calculate_SD_for_Delta_Delta_Ct_Would_you_agree_with_the_one_I_found_out?_sg=-PZdJ_7YuxeTxFliq91Ozlf3B7gF81dQa1tlwbFmD5nTx7UEgWmuGmmNTX2KN9Nw0_dqNEzsJZs-omPa6jVjCHsC0w72Elg.cF2yDLJt7gACpSjYqqFz6B2koSOm3NVOpVpatW5esHsdeZ49HdKLGkzDJKHuT-vStb0ApBtnwtCc6FJOJEn0UA
Dear Alexander,
I am still unclear about the calculation of the algorithms. Until step 4, there is only one set of ddCq right, cause we already subtracts the first dCq of each samples to the mean of first dCq from control group. So only one set ddCq to be convert to fold change. Then, only one set of fold changes to be put into the Graphpad prism, right? so how to calculates the t-test cause we only have the one set of fold change.
Really appreciate to hear a clear understanding soon. Thank you.
"Raise 2 to the power of delta-deltas (2^ΔΔCt) " should be Raise 2 to the power of minus delta-deltas (2^-ΔΔCt)
Dear Alexander
can you please answer this query?
I am still unclear about the calculation of the algorithms. Until step 4, there is only one set of ddCq right, cause we already subtracts the first dCq of each samples to the mean of first dCq from control group. So only one set ddCq to be convert to fold change. Then, only one set of fold changes to be put into the Graphpad prism, right? so how to calculates the t-test cause we only have the one set of fold change.
Really appreciate to hear a clear understanding soon. Thank you.
I am also having trouble with the same thin Faiza Aziz . but this time I have 7 experimental groups like different doses of a drug. non of those models are giving me statistical analysis of those expression fold change of a gene in 7 groups
Oh, I have not been getting any notifications about the replies, sorry.
So, dear Siti, Ahmed, Faiza and Fulya,
Probably, you already know the answer and are doing your job great, but I still have to reply.
Indeed, in step 4 we are getting already -ΔΔCt, so the whole algorithm is technically correct (except for the "minus" misunderstanding), and if you follow it you will get correct results.
Probably, to avoid this misunderstanding, I should have written those steps not like this:
4. Subtract 1st deltas of each sample separately from that Mean - now you get delta-deltas (ΔΔCt).
5. Raise 2 to the power of delta-deltas (2^ΔΔCt) - here are your fold changes. If everything is done correct, your Control measures will be close to 1.
... , but as follows:
4. Subtract that Mean from 1st deltas of each sample separately - now you get delta-deltas (ΔΔCt).
5. Raise 2 to the power of "minus" delta-deltas (2^-ΔΔCt) - here are your fold changes. If everything is done correctly, your Control measures will be close to 1.
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Anyway, the point here is that the bigger the first ΔCt (Ct(GOI) - Ct(ref)), the lower the GOI relative-fold expression, I think this is obvious.
I can also recommend you this wonderful video and explanation:
https://toptipbio.com/delta-delta-ct-pcr/
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And to answer Siti's question: you will get as many sets of (2^-ΔΔCt) values, as you have groups, like in this table as an example.
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