Question: How to report and plot qPCR data - delta delta Ct
gravatar for sabela
4.6 years ago by
sabela0 wrote:


I am trying to write a script in python to analyse my qPCR data, however I get confused with terminology how to report and plot results. I'd appreciate some advice on how to do this properly

  • What is the best way to report the results? In my lab they usually report mRNA expression of the Gene of Interest (GOI) relative to the reference gene, but the numbers you get seem quite irrelevant from my point of view; if you plot them, however, you clearly see the differences. I am also aware of the fold change, which seems more relevant if you just report the value, but when you plot them I think there's the problem that it's skewed to upregulated genes (which can go from one to infinity, whereas the downregulated ones can just be from 0 to 1).

  • To solve the latter, I have seen recommendations to plot log2 fold change, but that seems weird, because isn't that just the same value as ddCt?

  • If you do recommend reporting the fold change, what kind of error bars should I use when making the bar plot? Are the distributions still normal?

Thank you very much!

fold change qpcr • 16k views
ADD COMMENTlink modified 14 months ago by Kevin Blighe69k • written 4.6 years ago by sabela0


What exactly is the purpose of the plot you are trying to make? Show the raw Ct values? To show the normalized (compared to ref gene) values? The fold change values between two conditions?

All options are possible, it depends on what your purpose is of the plot.

ADD REPLYlink written 4.6 years ago by Benn8.1k

So my final purpose is to study differences in expression of a gene of interest in treated vs control (or disease vs healthy). For that, yes, I want to have the values normalised to the reference gene. So I wonder what is the best way to show that, I guess it's partly a matter of opinions, but I'd like to make an informed decision :)

ADD REPLYlink written 4.6 years ago by sabela0

In that case I would choose for log2 transform fold change values, especially when both up- and down regulated genes are put in one plot. Log2 transformation makes up- and down regulation (ratios) symmetrical.

ADD REPLYlink written 4.6 years ago by Benn8.1k

Aha, thanks. However, isn't the log 2 of the fold change equal to ddCt? Also, in the case of plotting that, what kind of error bars do you use?

Could you please put an example of such analysis and display of data?

Thanks a lot!

ADD REPLYlink written 4.6 years ago by sabela0

I think ddCt is similar to log2 FC.

You can check this package in R for further explanation:

ADD REPLYlink written 4.6 years ago by Benn8.1k
gravatar for Kevin Blighe
2.5 years ago by
Kevin Blighe69k
Republic of Ireland
Kevin Blighe69k wrote:

The standard is the delta delta Ct (ddCt) method, whereby the Ct values in each sample are normalised to:

  1. a housekeeper gene (or genes) within the same sample
  2. the respective values in a control DNA sample

The final, normalised Ct, the delta delta Ct, is then typically put as a negative power to 2:

2 ^ (- ddCt)

For example, we have the following raw Ct values:

                  Sample1_Rep1 Sample1_Rep2 Sample1_Rep3 | Control_DNA1 Control_DNA2 Control_DNA3
Gene1             19           20           19           | 21           21           20
HousekeeperGene   22           22           22           | 21.5         21           22

[1] Gene1, Delta Ct in Sample1:

((19+20+19) / 3) - ((22+22+22) / 3) = -2.667

[2] Gene1, Delta Ct in Control DNA:

((21+21+20) / 3) - ((21.5+21+22) / 3) = -0.8333

[3] Gene1, Delta Delta Ct:

-2.667 - (-0.8333) = -1.8337

[4] fold increase / decrease:

2 ^ (- ddCt) = 2 ^ (- (-1.8337)) = 3.57

So, Gene1 in sample1 has a 3.57 fold increase over control DNA


ADD COMMENTlink modified 7 months ago • written 2.5 years ago by Kevin Blighe69k
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