Abstract
Here we present a method based on multivariable linear models for qPCR data analysis as an alternative to the most commonly used method, 2(-ΔΔCT). It has long been understood that amplification efficiency during qPCR may be less than two, that is, the amount of DNA present may not double in each cycle, and it is also known that amplification efficiency may differ between target and reference genes. Therefore, it has long been recommended that qPCR experiments include direct assessment of amplification efficiency, and that efficiency values be included in the calculation of differential gene expression. Nonetheless, current reports that include qPCR data continue to use 2(-ΔΔCT), even though 2(-ΔΔCT) assumes an efficiency of two in both reference and target genes. Using multivariable linear models for qPCR data analysis does not require direct measurement of amplification efficiency but provides correct significance estimates for differential expression even when amplification is less than two or differs between target and reference genes. We introduce the logic behind using multivariable linear models in the context of qPCR data analysis, the mathematics behind using them, and provide simulations demonstrating that multivariable linear models outperform 2(-ΔΔCT) for qPCR data analysis.