qPCR

Gradiance supports efficiency-aware relative expression from tidy Cq measurements, absolute quantification from a Cq versus log₁₀(copy) standard curve, and primer-efficiency estimation from a dilution series. Each workflow requires its scientific controls and units explicitly.

The workflows

• Relative expression (ΔΔCt). Compare a target gene across conditions, normalized to a reference gene. Output is fold-change (2^−ΔΔCt) per condition.
• Absolute quantification. Copy number from a standard curve of known templates. Output is copies (or copies/µL, copies/cell) for each unknown.
• Primer efficiency. Fit Cq against log₁₀(template amount) and report efficiency from the fitted negative slope.

Relative ΔΔCt

The math

For each well: ΔCt = Ct(target) − Ct(reference). Then: ΔΔCt = ΔCt(sample) − ΔCt(control). Fold-change is 2^−ΔΔCt.

With no supplied efficiency map, the method uses an amplification factor of 2 for each gene. You may instead provide a validated amplification factor greater than 1 for each target and reference. The method does not infer efficiency from standards in the same table.

What you need on the plate

• Target - the gene of interest, in technical triplicate per biological replicate.
• Reference - a stable housekeeping gene (GAPDH, β-actin, 18S rRNA, or whatever your lab has validated as stable in your system). Same triplicate structure.
• Calibrator - one condition designated as “control” or “untreated.” Every other condition is reported as fold-change relative to this.

Tell the agent the exact reference_gene andcontrol_sample. The input table itself usessample, target, and ct columns; names alone are not treated as control metadata.

Uncertainty

When all four target/reference and sample/control groups have at least two technical measurements, the method propagates their mean Cq variances to a 95% interval on fold-change. This interval represents technical measurement scatter with fixed efficiencies; it is not a biological-replicate significance test.

QC specific to ΔΔCt

• Complete comparison groups. Every reported target must have sample target/reference and control target/reference measurements. Missing groups stop the analysis rather than producing a partial result.
• Uncertainty availability. If any required group has fewer than two technical replicates, fold-change is still reported but its confidence interval is marked not estimable.
• Cq cutoff. A positive protocol-specificmax_ct may be supplied. The method never invents a universal cutoff from a sample or target name.

Absolute quantification

The math

Fit a linear regression of Ct versus log₁₀(copies) using known standards. Interpolate copy number for each unknown from its measured Ct.

PCR efficiency is computed from the slope: E = 10^(-1/slope) - 1. A slope of −3.32 corresponds to 100% efficiency. Values between −3.1 and −3.6 (90-110% efficiency) are acceptable.

What you need on the plate

• Standards - at least three distinct positive copy levels. More levels and replicates usually make the range and slope easier to assess.
• Unknowns - your samples. Triplicate per sample.
• Reviewed controls - inspect NTCs separately before fitting. The absolute-quantification table accepts onlystandard and unknown rows.

QC specific to absolute quant

• Standard curve R² ≥ 0.99. Anything lower means the standards weren't prepared with consistent dilution factors, or contamination is leaking in at the low end.
• Efficiency 90-110%. Outside this range, the slope-derived copy numbers are off by enough that you should fix the assay before reporting.
• Unknowns within standard range. Extrapolated unknowns are flagged. Dilute or concentrate to land within the curve.

Designing a qPCR run that fits well

• Three biological replicates per condition, three technical replicates per biological. The biological reps drive your statistics; the technical reps suppress pipetting noise.
• Validate your reference gene in your specific system. The “always-stable” references in textbooks are not always stable in practice.
• Keep all wells for one comparison on the same plate. Cross-plate comparisons require an inter-plate calibrator and a known adjustment factor.
• Always run NTCs. Late or unexpected amplification in NTCs invalidates everything else on the plate.

What the result looks like

Relative runs return fold-change and a technical-scatter interval when estimable. Absolute runs return copy numbers for unknowns inside the calibrated Cq range, plus slope, intercept, efficiency, R², and RMSE. Primer-efficiency runs return the slope-derived efficiency with curve diagnostics. Free accounts include20 free runs per calendar month (UTC) and can export a PNG figure and basic CSV results. Entitled accounts may also export other formats and a Python saved-fit replay; that replay does not recompute parameters from raw Ct data. See Understanding your results for how to read the result panel.