qPCR ΔΔCt Calculator — Biological vs Technical Replicates
Free qPCR fold change calculator that handles replicates correctly: technical replicates are averaged within each biological replicate, and n comes from biological replicates only. Imports QuantStudio and Bio-Rad CFX exports. Livak 2^-ddCt and Pfaffl efficiency correction, MIQE-aware.
How it works
Fold change is calculated by the comparative Ct method. ΔCt is the difference between target and reference gene Ct within a sample; ΔΔCt is the difference between the treated ΔCt and the calibrator ΔCt; fold change is 2^(-ΔΔCt), with efficiency correction available where primer efficiency departs from 100%. The part that differs from most calculators is the replicate handling: technical replicates are averaged within each biological replicate, and n comes from biological replicates only. Statistics run on ΔCt, which is a log-ratio and approximately normal, rather than on fold change, which is log-normal and skewed. Where a design has just one biological replicate per condition, no p-value is produced at all and the reason is stated — technical replicates measure pipetting, not biology.
Frequently asked questions
How do you calculate fold change from qPCR Ct values?
Subtract the reference gene Ct from the target gene Ct within each sample to get ΔCt. Subtract the control sample’s ΔCt from the treated sample’s ΔCt to get ΔΔCt. Fold change is 2 raised to the power of negative ΔΔCt. For example, a ΔΔCt of −2 gives a fold change of 4, meaning the target is four times more abundant in the treated sample.
What is a good reference gene for qPCR normalisation?
A reference gene must be stably expressed across your specific conditions — there is no universally valid choice. GAPDH, ACTB and 18S are common starting points, but each varies under hypoxia, confluence changes, and many drug treatments. Best practice is to validate two or three candidates in your own experimental conditions and normalise to their geometric mean.
Are three technical replicates enough for qPCR statistics?
No. Technical replicates are the same cDNA pipetted into several wells, so they measure pipetting precision rather than biological variability. Using them as n is pseudoreplication: it shrinks the error bars and can manufacture significance from an experiment done once. The MIQE guidelines (Bustin et al. 2009) require the distinction to be stated. Average the technical replicates within each sample, then set n from the number of independent biological replicates — separate animals, passages or transfections. This calculator does that automatically, and refuses to report a p-value when every condition has only one biological replicate.
What is the difference between biological and technical replicates?
A biological replicate is an independent sample — a different animal, a separate passage of cells, an independent transfection. A technical replicate is the same sample measured more than once, such as three wells loaded from one cDNA prep. Only biological replicates carry information about whether an effect generalises beyond the one sample you happened to prepare, so only they should set n. Technical replicates are still worth running: averaging them reduces measurement noise on that sample, which is exactly what they are for.
Method reference
Livak KJ & Schmittgen TD (2001) Methods 25(4):402-408.