Drybench Assay.
qPCR fold change with a 95 percent interval
Fold change from Cq values by ΔΔCq or entered efficiencies, with a Welch interval per condition against the calibrators; biological samples are n.
Relative expression from a Cq table, by equal-efficiency ΔΔCq or with your measured amplification efficiencies, with each condition's 95 percent interval on the fold change. The interval is a two-sample Welch t-interval on the difference of mean normalised log₂ quantities against the run's calibrator samples, so the calibrators' own scatter is inside it. Biological samples are n; technical wells are averaged first and never counted. Below three biological samples on either side the interval is withheld and the page says why.
Worked example: equal-efficiency ΔΔCq (E = 2)
| Condition | Biological n | Geometric mean fold change | 95% interval (fold change) | Welch df |
|---|---|---|---|---|
| Vehicle | 3 | 1 | Withheld: this group contains a declared calibrator sample. | |
| Drug | 3 | 3.605 | 2.886 to 4.502 | 3.72 |
Worked example: entered efficiencies (IL6 E = 1.92, ACTB E = 1.97)
| Condition | Biological n | Geometric mean fold change | 95% interval (fold change) | Welch df |
|---|---|---|---|---|
| Vehicle | 3 | 1 | Withheld: this group contains a declared calibrator sample. | |
| Drug | 3 | 3.346 | 2.736 to 4.092 | 3.65 |
Open this example in the appCalculate there, or paste your own Cq export and confirm its sample IDs in the import review.
Worked example
- Three vehicle samples are the calibrators and three drug-treated samples are compared with them, each measured in two technical wells for IL6 and the reference ACTB. The values are synthetic.
- The fold change is 2 to the mean difference of normalised log₂ quantities. Its interval comes from the six biological values, not the twelve wells. The vehicle row has no interval, because it is the reference.
- Computed when this page is built, by the same engine the app uses. That engine is checked against SciPy, and against R's t.test, on the published fixture.
Method
- qPCR relative expression. Pfaffl MW (2001), Nucleic Acids Research 29:e45; Hellemans et al. (2007), Genome Biology 8:R19. Technical Cq means, explicit efficiencies, geometric normalization by selected references and run-local biological calibrators.Exact ΔΔCq and independently calculated unequal-efficiency fixtures; no reference-gene stability validation or inter-run normalization. Group intervals are the Welch method's, verified separately.
- Welch t-interval on the log₂ fold change. Welch BL (1947), Biometrika 34:28–35: the two-sample t with unequal variances and Welch–Satterthwaite degrees of freedomChecked against a hand Welch computation with SciPy t quantiles on 10 synthetic Cq tables: bounds within 1e-9, df within 1e-9 relative, withheld reasons exact; R t.test agrees on all 8 intervals (12 October 2026).
Limits
- Group intervals are 95 percent Welch t-intervals against the run's calibrators, withheld below three biological samples a side; technical wells are never n. No hypothesis tests; efficiencies are treated as fixed.
- Reference-gene stability and specificity are experimental assumptions; Cq tables cannot establish them.
- Efficiencies are constant per assay. Equal-efficiency ΔΔCq explicitly assumes E = 2 for every assay.
- Technical wells are averaged only within run, biological sample and assay. Runs are never pooled or normalised to each other.
- All declared reference assays and calibrator samples are required for each run/target comparison. Missing measurements are not substituted.
- Group fold change is the geometric mean over quantified biological samples; unresolved sample counts are reported separately.
- Relative quantities compare the same target across samples, not absolute abundance between different genes.
- NTC/no-RT observations support review only; this calculation does not establish assay validation or MIQE conformance.