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Auto-regressive modeling and diagnostics for qPCR amplification

Benjamin Hsu, Valeriia Sherina, View ORCID ProfileMatthew N. McCall
doi: https://doi.org/10.1101/665596
Benjamin Hsu
University of Rochester
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Valeriia Sherina
University of Rochester
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Matthew N. McCall
University of Rochester Medical Center
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  • ORCID record for Matthew N. McCall
  • For correspondence: mccallm@gmail.com
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Abstract

Current methods used to analyze real-time quantitative polymerase chain reaction (qPCR) data exhibit systematic deviations from the assumed model over the progression of the reaction. Slight variations in the amount of the initial target molecule or in early amplifications are likely responsible for these deviations. Commonly-used 4- and 5-parameter sigmoidal models appear to be particularly susceptible to this issue, often displaying patterns of autocorrelation in the residuals. The presence of this phenomenon, even for technical replicates, suggests that these parametric models may be misspecified. Specifically, they do not account for the sequential dependent nature of qPCR fluorescence measurements. We demonstrate that a Smooth Transition Autoregressive (STAR) model addresses this limitation by explicitly modeling the dependence between cycles and the gradual transition between amplification regimes. In summary, application of a STAR model to qPCR amplification data improves model fit and reduces autocorrelation in the residuals.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted June 10, 2019.
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Auto-regressive modeling and diagnostics for qPCR amplification
Benjamin Hsu, Valeriia Sherina, Matthew N. McCall
bioRxiv 665596; doi: https://doi.org/10.1101/665596
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Auto-regressive modeling and diagnostics for qPCR amplification
Benjamin Hsu, Valeriia Sherina, Matthew N. McCall
bioRxiv 665596; doi: https://doi.org/10.1101/665596

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