How to calculate qPCR fold change with the ΔΔCt method

Updated October 30, 2026

The ΔΔCt method (Livak and Schmittgen, 2001) compares how much a target gene changes between two conditions, normalised to a reference gene. It needs only Ct values, which makes it the most common way to report relative expression from qPCR.

Before you start

• Both primer pairs should amplify with efficiency close to 100% (90–110%) and within about 10% of each other. Check this with a standard curve.
• The reference gene (for example GAPDH, ACTB or 18S) should be stable across your conditions.
• Average technical replicates before you calculate.

The calculation, with an example

1. ΔCt for each sample = target Ct − reference Ct.
2. ΔΔCt = ΔCt treated − ΔCt control = 5.8 − 8.2 = −2.4.
3. Fold change = 2^−ΔΔCt = 2^2.4 = 5.28. The target is about 5.3 times higher in the treated sample.

A fold change below 1 means lower expression. A value of 0.25 is often reported as a 4-fold decrease, or as log₂ fold change −2.

Biological replicates and statistics

Calculate ΔCt for each biological replicate and run statistics on the ΔCt (or ΔΔCt) values, which are roughly normally distributed. Fold changes are not, so don't average them or test them directly. Report the mean fold change with a confidence interval derived from the ΔCt statistics.

When to use the Pfaffl method instead

If the two assays have clearly different efficiencies, 2^−ΔΔCt becomes biased. The Pfaffl method uses each assay's own amplification factor E (for example 1.95 for 95% efficiency):

ratio = E_target^{(Ct control − Ct treated)} ÷ E_reference^{(Ct control − Ct treated)}