Comparison of different methods for quantitative real-time polymerase chain reaction data analysis
Polymerase Chain Reaction (PCR) is a laboratory technique for molecular biology to amplify and simultaneously quantify targeted DNA molecules, where the product of reaction is then detected at the end of all cycles. Differently, Real Time Polymerase Chain Reaction, also known as quantitative Polymerase Chain Reaction (qPCR), detected its product after every cycle as the reaction progresses using a specific fluorescence technique. Among the various quantitative methods currently used to analyze qPCR data, the quality of estimation varies. This study compares eight different models applied to the same qPCR dataset by evaluating the accuracy and precision of estimation. Also, the study evaluates a newly introduced preprocessing method, taking difference method, compared to currently used method of substracting background fluorescence. Taking difference method is to substract the fluorescence in the former cycle from that in the latter cycle to avoid background fluorescence estimation. Result from the eight models show that overall taking difference is a better way to preprocess qPCR data than original method due to a reduction in background estimation error. Also, weighted models are better than non-weighted models. Meanwhile the accuracy and precision of mixed models are close to that of linear regression models even though the data is clustered.
Chen, Ping, "Comparison of different methods for quantitative real-time polymerase chain reaction data analysis" (2014). Texas Medical Center Dissertations (via ProQuest). AAI1569998.