I did the RTqPCR of a gene in different cell lines samples and I used GAPDH as a housekeeping gene.
I calculated the deltaCt and I want to calculate the fold change.
What's the difference between log2 (delatCt) and 2^(-deltaCt) ? and which one is the best to evaluate the gene expression level?
I can partly answer your question. Once you calculate delta Ct values , you can calculate 2^(-delta Ct) for all samples. When you plot a graph using these values, it will actually represent the expression of target gene relative to endogenous control (GAPDH) in a given sample. This may help you compare the expression of a particular target gene in different samples. I am not sure if you need to calculate fold change in this case. In cases, where you have a control group, you average the 2^(-delta Ct) values of all the samples belonging to control group. Next you divide each of the 2^(-delta Ct) values of all samples (including control) from this averaged 2^(-delta Ct) of Control. Now the values that you obtain can be used to plot a graph which will show the fold expression of gene in different groups. In this case, the mean expression of target gene in control group will be 1.
I can partly answer your question. Once you calculate delta Ct values , you can calculate 2^(-delta Ct) for all samples. When you plot a graph using these values, it will actually represent the expression of target gene relative to endogenous control (GAPDH) in a given sample. This may help you compare the expression of a particular target gene in different samples. I am not sure if you need to calculate fold change in this case. In cases, where you have a control group, you average the 2^(-delta Ct) values of all the samples belonging to control group. Next you divide each of the 2^(-delta Ct) values of all samples (including control) from this averaged 2^(-delta Ct) of Control. Now the values that you obtain can be used to plot a graph which will show the fold expression of gene in different groups. In this case, the mean expression of target gene in control group will be 1.
I tend to avoid deltaCt methods wherever possible, the reason being that manipulating log values is a very, very good way to lose track of what the numbers actually mean. After you've subtracted and averaged masses of log values, you're often left looking at a list of numbers, some maybe negative, some maybe positive, wondering what the hell you're doing.
I therefore like to convert my data into relative quantities (RQs) as soon as humanly possible, and then do all my manipulations with these. Mathematically the outcome is identical, but this way you render your confusing log values into nice linear values much quicker.
Essentially (for any given gene) it is:
Efficiency (lowest measured Cq - sample Cq)
I.e. if you had three samples with Cq values of 22, 23 and 21, with an efficiency of 95% (i.e. 1.95), you would obtain
1.95(21 - 22)
1.95(21 - 23)
1.95(21 - 21)
i.e. 0.51, 0.26 and 1, respectively. Sample 1 contains half as much transcript as sample 3, and sample 2 contains a quarter as much. No logs: these are actual relative quantities.
Do the same for your reference genes (you should use more than one), then calculate the geometric average of those, and simply divide your GOI relative quantities by your ref gene relative quantities.
So say the geometric average of GAPDH and ActB gave you 0.45, 0.89 and 1, your final (normalised) relative quantities are 1.1, 0.3 and 1, respectively.
You could plot this on a graph directly, or you could plot it on a graph with a log scale if you're dealing with larger changes. If you want to express these values as fold changes, then simply pick whichever value you want to be "1" and then divide the entire dataset by that value.
Remember: the numbers themselves are essentially arbitrary (2.2, 0.6, 2 is exactly the same as 1.1, 0.3, 1, here). What matters is the relationship between them. Making this relationship linear (rather than logarithmic) early on makes all the numbers easier to manipulate without confusion.
See attached excel sheet for the same data handled via RQ and via deltaCt methods.
Hello Safa Larafa,
The gene expression will be analyzed according to house keeping gene by Real-time PCR machine. Then you need to calculate according to your control sample. But as you have no control sample then you get result from housekeeping gene. As house keeping gene relative quantity value is 1 by real-time PCR. So I think you can find the gene expression level by real-time PCR, ΔΔCt Method relative quantity chart.
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