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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)

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}}

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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  1. Dr.Mohammad on March 12, 2019 at 3:21 pm

    Hi, i want ask you if i got on fold change 0.7 ok. now this down regulated because 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. i got it from researchgate but this against with your excel sheet. and please tell me can i say this gene was downregulated in 10-fold for example. is there any realation between delta delta ct and fold because if i get negative value in delta delat and fold change 17 fold what does mean? and also how to create graph and which value u put in it?
    please can you send me your email i want to send you my results to check it

  2. Ellie on February 25, 2019 at 12:09 pm

    If I have 3 experimental cell lines, plus 3 wild type cell lines, how would I input this into the spreadsheet? I don’t want to take the average of the experimental or controls, because these are meant to be independent replicates. I want to see how each of the 6 lines compare to all others. I could input 1 experimental vs 1 wild type, 3 times, but this would be biasing results as I would have to pick how to pair them. Any advice?

    • Sadia Nazir on July 1, 2019 at 1:46 pm

      take average of control and than subtract from experimental in order to avoid bias and then you will get three indepedent values in the end. however it is advisable to take average of technical replicates, at the least.

  3. parom_d on December 3, 2018 at 7:37 pm

    If i use exogenous control cel-39-3p for normalization instead of housekeeping gene , will it be same process for analysis?

  4. Dami on November 1, 2018 at 1:08 pm

    Can I still use this method if primer efficiency of my target gene is 104% and of my housekeeping gene 93%?

  5. Akhter on April 10, 2018 at 10:49 am

    can relative quantification be used without using housekeeping genes, when we test the effect of such substance on the expression of some genes, considering the untreated sample as a control, therefore how does the fold change calculated?

    • Rongchang Yang on May 8, 2019 at 3:26 am

      Theoretically, you can conduct the relative quantitative PCR without house keep genes if you can be sure all the samples have the same concentration of DNA or cDNA,. In fact, it is impossible to do so. Therefore, house keep gene (s) are required.

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