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

Posted in: PCR, qPCR and qRT-PCR
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)∆Ct 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 ΔCt values for the experimental (∆CTE) and control (∆CTC) 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 (\Delta\DeltaCt).

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^{-\Delta\Delta C_{t}} to get the expression fold change.

\DeltaCt Value (Experimental)\DeltaCt Value (Control)\Delta\DeltaCt ValueExpression Fold Change
\DeltaCTE\DeltaCTC\Delta\DeltaCt2^{-\Delta\Delta C_{t}}
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!

Need more qPCR help? Discover our top 11 qPCR papers and improve your qPCR data and analysis.

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^{-\Delta\Delta C_{t}} Method. Methods. 2001;25:402–8.

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51 Comments

  1. Milena Georgieva on October 5, 2016 at 1:55 pm

    Awesome help, Kannan!
    Thanks a lot!!!

    Other useful links for RTqPCR data quantification I have also found at: https://www.youtube.com/watch?v=GQOnX1-SUrI
    Might be useful for all of you too!



  2. Vijay Venkatraj on September 14, 2016 at 6:47 pm

    Amazing help!



  3. KFS on August 5, 2016 at 4:48 pm

    I need some help with my qPCR analysis.. I have cells grown at 22% o2 and 2% oxygen– extracted RNA and made cDNA.. now I want to see if a gene is increased in hypoxic conditions (2% o2).

    when working out delta delta CT can I normalise the experimental results to the cells grown at 22% o2 (which would be the control) or not because the conditions are different?



    • Marissa on February 28, 2017 at 2:18 pm

      Hi. I am also doing work with hypoxia induced gene expression. From my reading and what my PI told me, using a delta delta CT calculation should be good. If this can be combined with the 2^delta CT method to do a 2^delta delta CT I am unsure. But for the delta delta CT an equation like this should be good: (delta CT of gene of interest (hypoxia treated) – delta CT of housekeeping gene (hypoxia treated)) – (delta CT gene of interest (normoxia) – delta CT housekeeping gene (normoxia)) = delta delta CT for hypoxia normalized to normoxia. To my knowledge, a different method to look at relative expression is the 2^delta CT, but you might be able to do 2^delta delta CT, but you would need to check on that. I’m just postulating on that part. The delta delta CT part should be sufficient though.



      • Marissa on February 28, 2017 at 2:20 pm

        Just remembered that the 2^delta delta CT was covered in this article so it can be done lol.



  4. SPUD’s Your Bud When it Comes to Sensitive qPCR on July 23, 2016 at 8:36 am

    […] will also keep you from achieving the efficiency assumed by the widely used quantification method, delta-delta Ct. Using this method, you assume each template is amplified with 100% efficiency – every DNA […]



  5. Sneha on July 10, 2016 at 4:33 am

    Dear Suganth

    Many thanks for the explanation. I am currently analysing a four group experiment. Once control and three experimental. Would you know of any softwares to display the fold changes pictorially forming gene networks?



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