Estimating Real-Time qPCR Amplification Efficiency from Single-Reaction Data
Methods for estimating the qPCR amplification efficiency <i>E</i> from data for single reactions are tested on six multireplicate datasets, with emphasis on their performance as a function of the range of cycles <i>n</i><sub>1</sub>–<i>n</i><sub>...
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MDPI AG
2021-07-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/11/7/693 |
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| author | Joel Tellinghuisen |
| author_facet | Joel Tellinghuisen |
| author_sort | Joel Tellinghuisen |
| collection | DOAJ |
| description | Methods for estimating the qPCR amplification efficiency <i>E</i> from data for single reactions are tested on six multireplicate datasets, with emphasis on their performance as a function of the range of cycles <i>n</i><sub>1</sub>–<i>n</i><sub>2</sub> included in the analysis. The two-parameter exponential growth (EG) model that has been relied upon almost exclusively does not allow for the decline of <i>E</i>(<i>n</i>) with increasing cycle number <i>n</i> through the growth region and accordingly gives low-biased estimates. Further, the standard procedure of “baselining”—separately estimating and subtracting a baseline before analysis—leads to reduced precision. The three-parameter logistic model (LRE) does allow for such decline and includes a parameter <i>E</i><sub>0</sub> that represents <i>E</i> through the baseline region. Several four-parameter extensions of this model that accommodate some asymmetry in the growth profiles but still retain the significance of <i>E</i><sub>0</sub> are tested against the LRE and EG models. The recursion method of Carr and Moore also describes a declining <i>E</i>(<i>n</i>) but tacitly assumes <i>E</i><sub>0</sub> = 2 in the baseline region. Two modifications that permit varying <i>E</i><sub>0</sub> are tested, as well as a recursion method that directly fits <i>E</i>(<i>n</i>) to a sigmoidal function. All but the last of these can give <i>E</i><sub>0</sub> estimates that agree fairly well with calibration-based estimates but perform best when the calculations are extended to only about one cycle below the first-derivative maximum (FDM). The LRE model performs as well as any of the four-parameter forms and is easier to use. Its proper implementation requires fitting to it plus a suitable baseline function, which typically requires four–six adjustable parameters in a nonlinear least-squares fit. |
| first_indexed | 2024-03-10T09:34:20Z |
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| language | English |
| last_indexed | 2024-03-10T09:34:20Z |
| publishDate | 2021-07-01 |
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| series | Life |
| spelling | doaj.art-61326a04bc334c4e870c22a8931976c22023-11-22T04:13:17ZengMDPI AGLife2075-17292021-07-0111769310.3390/life11070693Estimating Real-Time qPCR Amplification Efficiency from Single-Reaction DataJoel Tellinghuisen0Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USAMethods for estimating the qPCR amplification efficiency <i>E</i> from data for single reactions are tested on six multireplicate datasets, with emphasis on their performance as a function of the range of cycles <i>n</i><sub>1</sub>–<i>n</i><sub>2</sub> included in the analysis. The two-parameter exponential growth (EG) model that has been relied upon almost exclusively does not allow for the decline of <i>E</i>(<i>n</i>) with increasing cycle number <i>n</i> through the growth region and accordingly gives low-biased estimates. Further, the standard procedure of “baselining”—separately estimating and subtracting a baseline before analysis—leads to reduced precision. The three-parameter logistic model (LRE) does allow for such decline and includes a parameter <i>E</i><sub>0</sub> that represents <i>E</i> through the baseline region. Several four-parameter extensions of this model that accommodate some asymmetry in the growth profiles but still retain the significance of <i>E</i><sub>0</sub> are tested against the LRE and EG models. The recursion method of Carr and Moore also describes a declining <i>E</i>(<i>n</i>) but tacitly assumes <i>E</i><sub>0</sub> = 2 in the baseline region. Two modifications that permit varying <i>E</i><sub>0</sub> are tested, as well as a recursion method that directly fits <i>E</i>(<i>n</i>) to a sigmoidal function. All but the last of these can give <i>E</i><sub>0</sub> estimates that agree fairly well with calibration-based estimates but perform best when the calculations are extended to only about one cycle below the first-derivative maximum (FDM). The LRE model performs as well as any of the four-parameter forms and is easier to use. Its proper implementation requires fitting to it plus a suitable baseline function, which typically requires four–six adjustable parameters in a nonlinear least-squares fit.https://www.mdpi.com/2075-1729/11/7/693qPCRdata analysisnonlinear least squaresstatistical errorscalibration |
| spellingShingle | Joel Tellinghuisen Estimating Real-Time qPCR Amplification Efficiency from Single-Reaction Data Life qPCR data analysis nonlinear least squares statistical errors calibration |
| title | Estimating Real-Time qPCR Amplification Efficiency from Single-Reaction Data |
| title_full | Estimating Real-Time qPCR Amplification Efficiency from Single-Reaction Data |
| title_fullStr | Estimating Real-Time qPCR Amplification Efficiency from Single-Reaction Data |
| title_full_unstemmed | Estimating Real-Time qPCR Amplification Efficiency from Single-Reaction Data |
| title_short | Estimating Real-Time qPCR Amplification Efficiency from Single-Reaction Data |
| title_sort | estimating real time qpcr amplification efficiency from single reaction data |
| topic | qPCR data analysis nonlinear least squares statistical errors calibration |
| url | https://www.mdpi.com/2075-1729/11/7/693 |
| work_keys_str_mv | AT joeltellinghuisen estimatingrealtimeqpcramplificationefficiencyfromsinglereactiondata |