Per-Sample Normalization: DCT = CT(target) - mean(CT of reference genes)
Control Group Baseline
Mean -DCT of the control group per gene (reference baseline for fold change calculation).Fold Change Calculation
Neg_DDCT = Neg_DCT - control baseline. FC = E^(Neg_DDCT).Summary by Gene and Group
ANOVA Results
qPCR Relative Quantification - User Guide
1. Overview
This app performs qPCR relative quantification using the DDCT method. It calculates fold changes in gene expression between treatment and control groups, with optional amplification efficiency correction (Pfaffl method). Statistical tests and visualizations are included.
2. Data Format
Upload an Excel/CSV file with columns: Sample, Group, Gene, CT. Each row represents one technical replicate. Multiple replicates per sample/gene combination are automatically averaged.
Ctrl1 | Control | GAPDH | 18.20
Ctrl1 | Control | GAPDH | 18.25
Ctrl1 | Control | TP53 | 24.50
Treat1 | Treatment | GAPDH | 18.25
Treat1 | Treatment | TP53 | 22.10
... | ... | ... | ...
3. Calculation Methods
A. Livak Method (2^-DDCT)
Assumes 100% amplification efficiency (E = 2).
Step 1: DCT = CT(target) - mean(CT of all reference genes)
Step 2: DDCT = DCT(treatment) - mean(DCT(control))
Step 3: Fold Change = 2^(-DDCT)
B. Pfaffl Method (efficiency-corrected)
Uses primer-specific amplification efficiency (E).
For multi-reference genes: E_ref = geometric mean of all ref efficiencies.
Fold Change = (E_target)^(-DDCT) (DDCT already includes ref normalization).
E is determined from standard curve: E = 10^(-1/slope) - 1.
Enter efficiency values (1.0 - 2.0) for each gene. Default = 2.0 (100%).
To use Pfaffl method, click 'Advanced Settings' in the left sidebar.
4. Result Tabs
Step 2: Fold Change - Control baseline + per-sample DDCT + group-level Fold Change with 95% CI.
Visualizations - Bar plot (with pooled 95% CI), heatmap, volcano plot.
Statistics - Welch t-test p-values based on pre-computed summary statistics.
5. Statistical Tests
t = Mean_Neg_DDCT / Pooled_SE, where Pooled_SE = sqrt(SE_ctrl^2 + SE_treat^2)
Welch-Satterthwaite df for unequal variances.
Significance: * p<0.05, ** p<0.01, *** p<0.001
6. Visualizations
Heatmap: Log2 fold change across genes and samples.
Volcano Plot: Log2 fold change vs -log10(p-value), highlighting significant genes.
7. Tips
- Use multiple reference genes (2-3) for more stable normalization.
- Check amplification efficiency (90-110%) before using Pfaffl method. To access Pfaffl, expand 'Advanced Settings' in the left sidebar.
- Biological replicates (n >= 3) are needed for reliable statistics and CI.
- Download the Excel template to see the expected data format.