CtQuant

# CtQuant - qPCR Analysis Tool CtQuant is a powerful and intuitive tool for analyzing qPCR data using the ddCt (2^-ddCt) quantification method. It simplifies technical replicate averaging, dCt/ddCt calculations, and statistical comparisons. ## Version 1.0.0 ## Citation Users are requested to cite **Hauffe et al. 2026 (in submission)** when using CtQuant. ## LicenseThis software is free for academic and non-commercial research use.Commercial use requires a separate license agreement.See the LICENSE file for details. ## Credits **Author:** Dr. Robert Hauffe ## Features- **Flexible Mapping:** Visual plate mapper for 96-well and 384-well formats.- **Automated Calculations:** Technical replicate averaging, dCt, ddCt, and Fold Change.- **Statistical Analysis:** Built-in Student's t-test and Welch's t-test.- **Interactive Refinement:** Exclude biological replicates directly from results view.- **Comprehensive Export:** Export summary and detailed biological/technical replicate data to multi-sheet Excel files. ## Methodology CtQuant processes raw Ct values through the following pipeline: 1. **Technical Replicate Averaging:** - Ct values for technical replicates (same Sample, Gene, and Group) are averaged. - Standard Deviation (SD) is calculated; samples with SD > 0.5 are flagged in the detailed view. 2. **Delta Ct ($\Delta Ct$) Calculation:** - For each biological replicate: $$ \Delta Ct = Ct_{GOI} - Ct_{Housekeeping} $$ - Housekeeping (HK) gene values are matched to Genes of Interest (GOI) by Sample Name. 3. **Delta Delta Ct ($\Delta\Delta Ct$) Calculation:** - The average $\Delta Ct$ of the **Control Group** is calculated for each gene. - For each biological replicate: $$ \Delta\Delta Ct = \Delta Ct_{Sample} - \text{Average }\Delta Ct_{ControlGroup} $$ 4. **Fold Change:** - Relative quantification is calculated as: $$ \text{Fold Change} = 2^{-\Delta\Delta Ct} $$ 5. **Statistical Analysis:** - Statistical tests are performed comparing the **Fold Change** values of the Treatment group vs. the Control group. - **Student's t-test:** Assumes equal variance between groups. - **Welch's t-test:** Does not assume equal variance (recommended for biological data). - **Note:** Two-way ANOVA is currently a placeholder for future implementation; selecting it defaults to Welch's t-test.