qPCR ΔΔCt calculator
Relative gene expression by the Livak method is fold change = 2^−ΔΔCt, where ΔCt = Ct(target) − Ct(reference gene) and ΔΔCt = ΔCt(sample) − ΔCt(control). Paste your Ct values (one row per well), choose the control sample, and the calculator gives ΔCt, ΔΔCt, fold change with its SD range, the Pfaffl ratio if efficiencies differ from 100 %, a chart and a CSV.
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What it does
How to use the qPCR ΔΔCt calculator
- Enter Ct valuesOne row per well: sample, gene, target Ct and reference-gene Ct. Paste from a spreadsheet or type them in.
- Choose the controlSelect the calibrator sample (for example untreated or wild type) and, if known, the primer efficiencies.
- Read and exportSee ΔCt, ΔΔCt and fold change per sample and gene, then download the table and the chart.
How it works
ΔCt = Cttarget − Ctreference
ΔΔCt = mean ΔCtsample − mean ΔCtcontrol
Fold change = 2−ΔΔCt (range 2−(ΔΔCt ± SD))
Pfaffl ratio = EtargetΔCttarget(control − sample) / ErefΔCtref(control − sample)
ΔCt is calculated per well and averaged per sample, and its standard deviation gives the asymmetric fold-change range. The Livak method assumes both assays have about 100 % efficiency (E = 2); if a standard curve gives other efficiencies, enter them to add the Pfaffl ratio, with E = 1 + efficiency/100.
Worked example
Control: target Ct 28.1, GAPDH 18.2 → ΔCt 9.9. Treated: target 25.0, GAPDH 18.2 → ΔCt 6.8. ΔΔCt = 6.8 − 9.9 = −3.1, so fold change = 23.1 = 8.6: about 8.6 times more expression in treated cells.
Frequently asked questions
How do you calculate fold change from Ct values?
Subtract the reference-gene Ct from the target Ct for each sample (ΔCt), subtract the control’s ΔCt (ΔΔCt), and raise 2 to the power of −ΔΔCt.
What is a good reference (housekeeping) gene?
One whose expression does not change between your conditions, such as GAPDH, ACTB, 18S rRNA or B2M, validated for your samples. Using the geometric mean of two or three reference genes is more robust.
When should I use the Pfaffl method?
When the target and reference assays have different amplification efficiencies (outside about 90–110 %), measured with a standard curve. With equal 100 % efficiencies it gives the same result as 2^−ΔΔCt.
Should statistics be done on fold change or ΔCt?
On ΔCt (or ΔΔCt) values, which are approximately normally distributed; fold changes are log-normal. Report fold change for readability.
What does a fold change below 1 mean?
Lower expression than the control: 0.5 is half the control level (a 2-fold decrease).
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