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qPCR analyser: ΔΔCq fold change, efficiency-corrected (Pfaffl) expression with several reference genes, and standard curves

Paste sample, gene and Cq for every well. Get fold changes against a control sample with 2^−ΔΔCq or efficiency correction, normalised to one or more reference genes, with replicate checks and a bar chart. Fit standard curves for slope, efficiency and R², and read unknown quantities off them.

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  1. Wells
  2. References
  3. Efficiency
  4. Fold change
Nothing is uploadedWorks offline once open

Wells

Technical replicates are wells with the same sample and gene; they are averaged. Wells marked "Undetermined", "N/A" or left blank are skipped and counted. Up to 10,000 wells. The example is made up.

Every other gene is a target. With several reference genes the tool uses their geometric mean, which is steadier than one gene. "From the standard curves" corrects each gene for its own efficiency (genes without a curve stay at 100 %).

Standard curves (optional)

Quantity in any unit (copies, ng, or relative dilution such as 1, 0.1, 0.01). The line is Cq = slope × log₁₀(quantity) + intercept, and efficiency = 10^(−1/slope) − 1: a slope of −3.32 is 100 %. Unknowns are read off the curve of their gene.

Relative expression

Replicates

Standard curves

Unknowns

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What it does

Real-time PCR gives a Cq (or Ct) for each well: the cycle where the signal crosses a threshold. Lower Cq means more starting template. This page turns a plate’s worth of Cq values into the numbers you report:

  • Relative expression. For each target gene and sample, the fold change against a control sample, normalised to one or more reference (housekeeping) genes. With 100 % efficiency this is the classic 2^−ΔΔCq method; with efficiencies from standard curves it is the efficiency-corrected (Pfaffl) ratio. Several reference genes are combined by their geometric mean.
  • Replicate checks. Technical replicates are averaged; groups that spread more than 0.5 cycles, Cq values above 35, and reference genes that drift by more than a cycle across samples are flagged.
  • Standard curves. For each gene’s dilution series: slope, intercept, R² and efficiency with a ± 1 SE range, a plot, and the quantity of any unknowns.

How to use it

  1. Wells. One row per well: sample, gene, Cq. Paste from your instrument’s export or open a CSV or spreadsheet. “Undetermined” wells are skipped and counted.
  2. References and control. Type the reference gene names, separated by commas, and pick the control sample (its fold change is 1).
  3. Efficiencies. Keep 100 % for ΔΔCq, or choose From the standard curves and paste each gene’s dilution series below (gene, quantity, Cq). Leave the quantity blank to quantify an unknown.
  4. Read the results. The bar chart is on a log scale: bars above the 1× line are up-regulated, below are down. Whiskers show ± 1 SD from the replicates.

Worked example

The made-up example has three samples (Control, Drug A, Drug B), two targets (GeneX, GeneY) and two references (Ref1, Ref2), in triplicate. In Drug A, GeneX averages 22.13 against 24.20 in the control, while the references barely move, so ΔΔCq is about −2.17 and GeneX is up 4.49×. GeneY goes the other way, down about 2.1×. One Drug B GeneX well (24.3) is 0.9 cycles from its partners, so that group is flagged.

The GeneX standard curve has a slope of −3.37 (R² 0.9997), which is 98.0 % efficiency; an unknown at Cq 20.0 reads as about 37,100 copies.

Good to know

  • The fold-change range uses the sample’s replicate SDs, as in the ΔΔCq method; for significance, run a test on the ΔCq values of biological replicates.
  • Efficiency correction matters most when target and reference efficiencies differ, and when the change is large.
  • Reference genes should not respond to your treatment. Check the “Reference genes” line.

FAQ

Ct or Cq? Same thing. Cq is the name used in the MIQE guidelines.

Can I use biological replicates? Give each one its own sample name, or average them first. Wells with the same sample and gene are treated as technical replicates.

Is my data uploaded? No. Everything runs in your browser.

Where the numbers come from

  • Livak and Schmittgen (2001), 2^−ΔΔCT method, and Pfaffl (2001), efficiency-corrected ratio, cited for the methods only; several reference genes combined by their geometric mean as in Vandesompele et al. (2002). Written by fibub.
  • Efficiency = 10^(−1/slope) − 1 from a least-squares standard curve. The 90–110 %, R² 0.98, 0.5-cycle and 35-cycle checks are common rules of thumb, not a standard.
  • Checked in the tests against scipy.stats.linregress and an independent NumPy calculation. The example wells and curves are made up.

Standard scientific values, such as constants, atomic weights and melting points, are facts found in every textbook; we name where ours were checked. If you are an author or publisher and want a value, a credit or a table changed or removed, tell us and we will act quickly.

Last updated 2026-11-09.

Change log for qPCR analyser

  1. Add #165 qPCR analyser: ΔΔCq and efficiency-corrected fold change, standard curves and unknowns Details on GitHub

Every change to fibub

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