How to use (3 steps)
- Paste data (Group, Sample, Ct_target, Ct_ref) or load a CSV. For multiple references, use Ct_ref1, Ct_ref2, ...
- Select a calibrator group (or sample) and summary method (mean/median). Enable Pfaffl correction if needed.
- Click Calculate to show ΔCt/ΔΔCt, log2FC, and fold in tables and plots. Share URLs keep the same settings.
Recommended order
- First, check efficiency with a standard curve → qPCR standard curve
- Next, calculate ΔCt / ΔΔCt (this calculator)
- If needed, assess variability or significance → Confidence interval & hypothesis tests
Go deeper
- Descriptive stats
Check distributions and outliers
- Linear regression & correlation
Understand standard curves
Data input and options
Results (summary)
Default example data is already loaded. With JavaScript enabled, this summary updates to ΔCt, ΔΔCt, log2FC, and fold change.
Per-row results
| Row | Group | Sample | Ct_target | Ct_ref | ΔCt | ΔΔCt | log2FC | fold | Outlier |
|---|
Outliers are not removed automatically; they are shown as candidates (IQR-based).
Group summary
| Group | n | log2FC mean | log2FC SD | log2FC SEM | fold geometric mean | fold arithmetic mean |
|---|
Plots (log2FC / fold)
Calculation steps (How it’s calculated)
Use this page after you already have Ct data
This workflow starts once target and reference Ct values are available and you want relative expression against a calibrator. Open qPCR Standard Curve when you still need efficiency or unknown concentration checks, use A260 Calculator for nucleic-acid concentration and purity, and switch to Growth Curve Fitter when the downstream task is time-series modeling instead of Ct normalization.
- Confirm the calibrator choice and reference-gene handling before comparing groups.
- Use Livak by default, then switch to Pfaffl only when efficiency differences matter.
- Review ΔCt and ΔΔCt tables before you quote fold change in a report.
FAQ
What is the difference between ΔCt and ΔΔCt?
ΔCt is the within-sample difference between target and reference. ΔΔCt is the difference relative to a calibrator.
What is the assumption for 2^-ΔΔCt?
It generally assumes the target and reference amplification efficiencies are equal or similar. Use Pfaffl correction if they differ.
Should I use fold or log2FC?
log2FC is symmetric and easier for statistics and plots. Fold is more intuitive.
How are multiple reference genes handled?
When Ct_ref1, Ct_ref2, etc. are provided, reference Ct values are aggregated before calculating ΔCt.
When should I use Pfaffl efficiency correction?
Consider Pfaffl correction when target and reference efficiencies differ substantially.
Does the share URL include data?
Only settings are saved; data are not included.
Related tools
- Growth curve fitter (exponential & logistic) | CalcBEFit time-course biology data when your qPCR interpretation needs matching growth or saturation context.
- qPCR standard curve calculator | CalcBEEstimate efficiency, slope, and unknown quantities before deciding whether Livak or Pfaffl assumptions are appropriate.
- A260 Calculator (DNA/RNA Concentration & Purity) | CalcBECheck nucleic-acid concentration and purity before troubleshooting Ct shifts caused by template quality.
- Beta diversity calculator (Jaccard / Bray–Curtis) | CalcBEOpen diversity metrics later when the analysis moves from one target gene to community-level comparison.
- Cell seeding calculator | cells/well → required volume | CalcBEPlan the plate setup upstream when relative expression data depend on consistent cell numbers across samples.
References (notes)
Assumptions and notes in this calculator are general guidance. For research or education, also check primary sources.
Comments
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