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Summary

Digital PCR (polymerase chain reaction) analysis of environmental samples can be biased by common statistical assumptions. This study introduces a Bayesian model to accurately quantify gene targets using concentration estimates, even without partition counts, improving environmental monitoring.

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dPCReDNAlimit of detectionlimit of quantificationwastewater-based epidemiology

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Area of Science:

  • Environmental microbiology
  • Molecular ecology
  • Bioinformatics and statistical modeling

Background:

  • Digital PCR (dPCR) is crucial for quantifying genetic material in environmental samples, supporting species monitoring and wastewater epidemiology.
  • Existing dPCR analysis models often require precise assay parameters and partition counts, which are frequently unavailable in environmental studies.
  • Current practices often rely on concentration estimates with normal or log-normal distribution assumptions, ignoring inherent PCR assay characteristics like measurement noise and nondetects, leading to biased results.

Purpose of the Study:

  • To develop and present a robust Bayesian statistical model for analyzing dPCR concentration estimates without requiring partition counts or exact assay parameters.
  • To address and mitigate biases introduced by conventional statistical assumptions in dPCR data analysis for environmental applications.
  • To provide accurate inference from dPCR measurements, especially when complete data, including partition counts, is inaccessible.

Main Methods:

  • A Bayesian model incorporating a dPCR-specific likelihood function was developed to directly analyze reported concentrations.
  • The model integrates uncertainty in assay parameters using interpretable prior distributions.
  • The approach was validated using real-world case studies on free-eDNA decay in marine environments and pathogen surveillance in wastewater.

Main Results:

  • The proposed Bayesian model produced estimates comparable to fully informed models that utilize partition counts.
  • The method successfully avoided the biases associated with normal or log-normal distribution approximations for dPCR data.
  • Accurate inference was achieved even with incomplete dPCR data, demonstrating the model's practical utility.

Conclusions:

  • The developed Bayesian approach offers a statistically sound and unbiased method for analyzing dPCR concentration estimates in environmental science.
  • This methodology enhances the reliability of environmental monitoring and epidemiological studies using dPCR, particularly when partition data is limited.
  • The R packages "dPCRfit" and "EpiSewer" are provided for practical implementation in regression analyses and wastewater surveillance, respectively.