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4 Easy Steps to Analyze Your qPCR Data Using Double Delta Ct Analysis

Fingers "walking" up wooden blocks arranged as steps, representing the steps in Ct values from a qPCR experiment, analysed by double delta Ct analysis

You are at the airport burning away time with a report due tomorrow morning for your professor. You have your data. Why not take advantage of the time and calculate the expression fold change for the genes you have tested in that first qPCR experiment you did last week?

It’s easy – I’ll show you how.

Check Your Method

There are two main ways to analyze qPCR data: double delta Ct analysis and the relative standard curve method (Pfaffl method). Both methods make assumptions and have their limitations, so the method you should use for your analysis will depend on your experimental design.

The double delta Ct analysis assumes that:

  • there is equal primer efficiency between primer sets (i.e. within 5%);
  • there is near 100% amplification efficacy of the reference and the target genes;
  • the internal control genes are constantly expressed and aren’t affected by the treatment.

The method generally caters to experiments with a large number of DNA samples and a low number of genes to be tested.

The relative standard curve method assumes that:

  • there are equal efficiencies between the control and the treated samples.

This method works better if you have fewer DNA samples but a larger number of genes to test.

What You Need for Double Delta Ct Analysis

  • qPCR Ct values (raw data) for:
    • the housekeeping gene: control and experimental conditions;
    • the gene of interest: control and experimental conditions;
  • An Excel spreadsheet.

And that’s it! No expensive software required.

Here is a quick summary of the key steps in the double delta Ct analysis (for a detailed explanation read this paper).

4 Steps for Double Delta Ct Analysis

1.  Take the average of the Ct values for the housekeeping gene and the gene being tested in the experimental and control conditions, returning 4 values. The 4 values are Gene being Tested Experimental (TE), Gene being Tested Control (TC), Housekeeping Gene Experimental (HE), and Housekeeping Gene Control (HC).

Average Experimental Ct ValueAverage Experimental Ct ValueAverage Control Ct ValueAverage Control Ct Value\DeltaCt Value (Experimental)\DeltaCt Value (Control)
TEHETCHC\DeltaCTE\DeltaCTC
21.2720.2319.6019.271.030.33

2.  Calculate the differences between experimental values (TE – HE) and the control values (TC – HC). These are your \DeltaCt values for the experimental (\DeltaCTE) and control (\DeltaCTC) conditions, respectively.

3.  Then, calculate the difference between the \DeltaCT values for the experimental and the control conditions (\DeltaCTE – \DeltaCTC) to arrive at the double delta Ct value (ddCt).

4.  Since all calculations are in logarithm base 2, every time there is twice as much DNA, your Ct values decrease by 1 and will not halve. You need to calculate the value of 2^{-2\Delta\Delta C_{t}} to get the expression fold change.

dCt Value (Experimental)dCt Value (Control)ddCt ValueExpression Fold Change
dCTEdCTCddCt2^-ddCt
1.030.330.700.615572207

What Does the Value Mean?

Now that you have your value for fold change, what does it actually mean? This value is the fold change of your gene of interest in the test condition, relative to the control condition, which has all been normalized to your housekeeping gene.

To make it a little clearer – you can think about it as a percentage. A fold change of 1 means that there is 100% as much gene expression in your test condition as in your control condition – so there is no change between the experimental group and the control group. A fold-change value above 1 is showing upregulation of the gene of interest relative to the control (1.2-fold change = 120% gene expression relative to control, 5 = 500%, 10 = 1,000%, etc.). Values below 1 are indicative of gene downregulation relative to the control (fold change of 0.5 is 50% gene expression relative to control, so half as much expression as in the control, etc.).

You can present these data as fold-change bar charts, graphing the control conditions equal to 1. You can also use statistical analyses to check the significance of the changes, e.g. using an analysis of variance (ANOVA) or t-tests, whatever is appropriate for your experimental set-up!

Using these steps you can conduct your qPCR analysis wherever you are, even if you’re on a road trip. To make things even easier, you can create an Excel template to use each time. Then you will only have to input your data and you will astonish others with your alacrity in conducting analyses!

Originally published July 9, 2016. Reviewed and updated on February 8, 2021.

Further Reading

Livak KJ, Schmittgen TD. Analysis of Relative Gene Expression Data Using RealTime Quantitative PCR and the 2^{-2\Delta\Delta C_{t}} Method. Methods. 2001;25:402–8.

51 Comments

  1. Dhafer Al-koofee on January 8, 2020 at 7:49 pm

    its a good explanation and easy to applied, but there is no a fixed role for done, for example some one say if fold change less than ONE meaning down-regulation and vise versa with respect there is no difference in expression when the fold change equals one. Some time one divided on the double delta Ct values and I think this is confused for many researchers. However the Livak method applied only when the efficiency of both GOI and HKG are similar or need with each other with 5%. While delta Ct can be applied for individual samples and is benefit for cell line application as well as Livak because the delta Ct method is variation to Livake in addition to Pfaffi method which used to non equals or near efficiency of GOI and HKG.
    best regards, I built a sepratesheet of Excel deal with not average of experiment and control.



  2. Mohammad on March 12, 2019 at 4:05 pm

    i want ask you why you add ( – ) in 2^-ΔΔCt when you calculate =2^(-O4) already when you calculate in excl should you put negative ? thats why u fold change 17.5 i think thats wrong because i said u If the ddCt has a positive value, the gene of interest is upregulated, because the fold change will be larger than 1. On the other hand, if the ddCt has a negative value, the gene is downregulated and the fold change is <1. So delet the – you will get on 0.1 fold change , but i really dont know how i can say this gene was downregulated in 0.1 fold change ?



    • Ramón on June 27, 2019 at 1:20 pm

      You need to keep the negative in 2^-ΔΔCt. If you have a negative expression, that equation will retrieve a value below 1.

      If the value of the “Expression Fold Change” or “RQ” is below 1, that means you have a negative fold change. To calculate the negative value, you will need to transform the RQ data with this equation in Excel:

      =IF(X>=1,X,(1/X)*(-1))

      Change “X” to the cell of your RQ data. In the Excel of the example it will be the cell “P4”, therefore:

      =IF(P4>=1,P4,(1/P4)*(-1))

      That way if the number is 1 or >1 nothing changes, but if the number is <1 you will have the negative fold change. In your example if the value is 0.1, you will retrieve -10 fold change, and you will be able to say: "My RQ is 0.1, that means we have 10 times lower expression than our control population".

      For me is easier to transform the RQ data this way to have a better graphical representation of the data (more intuitive representation). hope this clarifies your concerns!



  3. Mohammad on March 12, 2019 at 3:53 pm

    hi, please can u tell me why a lot of people did graph for fold change and they put negative value for example in the graph they put gene down regulated -10 but when they discussed they said this gene was upregulatd 10 fold can you explain for me. my opinion according in your excel sheet i just can say the gene was downregulated -4.13 in fold change 17.5 is it correct way to explain?



    • Sadia Nazir on July 1, 2019 at 1:43 pm

      So that is not the case with fold change. fold change goes down like 0.1, 0.001, 0.002, 0.000000007 etc. what goes in negative is delta delta ct. and delta delta ct grpahs make more sense scientifically than fold change. However, as we starting the calculation doing subtraction as ct gene of interest-ct house keeping gene, the delta ct value here is inversely proportional to amount of dna or rna. so lesser the ct more the amount. so a negative value means up regulation. but if you do ct subtraction like reference gene- gene of interest than the delta ct will be directly proportional to amount of starting material which makes more sense.



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