A new method for quantitative real-time polymerase chain reaction data analysis

Abstract

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantification method that has been extensively used in biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle method and linear and nonlinear model-fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence can hardly be accurate and therefore can distort results. We propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtract the fluorescence in the former cycle from that in the latter cycle, transforming the n cycle raw data into n-1 cycle data. Then, linear regression is applied to the natural logarithm of the transformed data. Finally, PCR amplification efficiencies and the initial DNA molecular numbers are calculated for each reaction. This taking-difference method avoids the error in subtracting an unknown background, and thus it is more accurate and reliable. This method is easy to perform, and this strategy can be extended to all current methods for PCR data analysis.

FIG. 1.

Comparison of PCR amplification efficiencies estimated using different methods. (A) The original linear regression method with the subtraction of the mean of cycles 1–3. (B) The original linear regression method subtracting the mean of cycles 3–7. (C) The original linear regression method with the minimum subtraction. (D) The taking-difference linear regression method. PCR, polymerase chain reaction.