Provided that you repeat one experiment 3 times independently. For each experiment, you have 3 technical replicates, i.e 3 wells of the same treatment and calculated fold change of gene A from those replicates. I have several questions:
1. Is the control group always normalized to 1.0? Or you can keep it as the raw power 2 of the -ddCt (which is close to 1, e.g. 1.02, 1.24).
2. If you agree that the control group is 1.0 for every experiment, how do you pool fold change data from 3 independent experiments together and get the SD/SEM for the control group if they're all 1.0?
Or if you have any other ways to calculate it (e.g., from the dCt), please let me know. I attached here an example of my calculation for expression of 1 gene from 3 experiments. Thank you so much
1. Yes, control should be used for normalization
2. Please refer to this tutorials
http://bitesizebio.com/24894/4-easy-steps-to-analyze-your-qpcr-data-using-double-delta-ct-analysis/
Basically, for each set of experiment, you should normalize your gene of interest with housekeeping gene first (hACTB or hGAPDH), then you should fine the fold change of this gene with reference to housekeeping gene (dCT)
Then you compared the treatment fold change with control fold change to find the ddCT.
So, for each set of experiment, you will get a fold change value normalized with your housekeeping gene compared with control groups.
You simply use these three fold change value to calculate SD.
Therefore, most realtime graph will not show the housekeeping gene expression because they have already be included during calculation.
Thank you. I understand what you said, but how do you get the SD for the control group if they're always 1.0?
For example: Experiment 1: Control 1, Treated 2.5
Exp 2: Control 1, treated 2.6
Exp 3: control 1, treated 2.7
Since this method calculate the relative expression of treatment compared to control. So, you are right.
However, if you really want to calculate the fold change of control group, just simply use the above method while input CT from control group to replace the value of treatment group with the same formula.
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