# 4 Easy Steps to Analyze Your qPCR Data Using 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 gene you have tested in the lab?

It’s easy – I’ll show you how.

## What you need for analysis

You need qPCR Ct values (raw data) for

1. the housekeeping gene: control and experimental conditions
2. the gene being tested: control and experimental conditions

There are two ways to analyze qPCR data: double delta Ct analysis and the relative standard curve method. With the assumption of equal primer efficiency, double delta Ct analysis caters to large amounts of DNA samples and a low number of genes to be tested. The standard curve method is more optimal if you have very few DNA samples but many genes to test.

Here, I explain the double delta Ct analysis (for a detailed explanation of double delta Ct analysis, read this paper).

## Steps to conduct 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). 2.  Calculate the differences between TE and HE (TE-HE) and TC and HC (TC-HC). These are your ΔCt values for the experimental (ΔCTE) and control (ΔCTC) conditions, respectively.

3.  Then, calculate the difference between ΔCTE and ΔCTC (ΔCTE-ΔCTC) to arrive at the Double Delta Ct Value (ΔΔCt).

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^-ΔΔCt to get the expression fold change. Using these steps you can conduct this analysis anywhere you are, even if you are on a road trip. To make things even easier, you can create an excel template, like the one attached. Then you will only have to input your data and you will astonish others by your alacrity in conducting analyses!

DDCT excel template 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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