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)
Results will appear here.
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)
How to use this calculator effectively
This guide helps you use ΔCt / ΔΔCt calculator (relative expression) in a repeatable way: define a baseline, change one variable at a time, and interpret outputs with explicit assumptions before you share or act on results.
How it works
The page applies deterministic logic to your inputs and shows rounded output for readability. Treat it as a comparison workflow: run one baseline case, adjust a single parameter, and measure both absolute and percentage deltas. If a result seems off, verify units, time basis, and sign conventions before drawing conclusions. This approach keeps your analysis reproducible across teammates and sessions.
When to use
Use this page when you need a fast estimate, a classroom check, or a practical what-if comparison. It works best for planning and prioritization steps where you need direction and magnitude quickly before investing in deeper modeling, manual spreadsheets, or formal external review.
Common mistakes to avoid
- Changing multiple parameters at once, which hides the true cause of output movement.
- Mixing units (percent vs decimal, monthly vs yearly, gross vs net) across scenarios.
- Comparing with another tool without aligning defaults, constants, and rounding rules.
- Using rounded display values as exact downstream inputs without re-checking precision.
Interpretation and worked example
Run a baseline scenario and keep that result visible. Next, modify one assumption to reflect your realistic alternative and compare direction plus size of change. If the direction matches your domain expectation and the size is plausible, your setup is usually coherent. If not, check hidden defaults, boundary conditions, and interpretation notes before deciding which scenario to adopt.
See also
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.
How to use ΔCt / ΔΔCt calculator (relative expression) effectively
What this calculator does
This page is for estimating outcomes by changing inputs in one controlled workflow. The model keeps your focus on variables, not output shape. Start with stable assumptions, then test sensitivity by changing one key input at a time to observe directional impact.
Input meaning and unit policy
Each input has an expected unit and a typical range. For reliable interpretation, check whether you are using the same unit system, period, and base assumptions across all runs. Unit mismatch is the most common source of unexpected drift in numeric results.
Use-case sequence
A practical sequence is: first run with defaults, then create a baseline log, then run one alternative scenario, and finally compare only the changed output metric. This sequence reduces cognitive load and prevents false pattern recognition in early experiments.
Common mistakes to avoid
Avoid changing too many variables at once, mixing incompatible data sources, and interpreting a one-time output without checking robustness. A single contradictory input can flip conclusions, so keep each experiment minimal and document assumptions as part of your note.
Interpretation guidance
Review both magnitude and direction. Direction tells you whether a strategy moves outcomes in the desired direction, while magnitude helps you judge practicality. If both agree, you can proceed; if not, rebuild the baseline and verify constraints before deciding.
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References (notes)
Assumptions and notes in this calculator are general guidance. For research or education, also check primary sources.
Comments
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