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Machine Learning-Based Determination of Sampling Depth for Complex Environmental Systems: Case Study with Single-Cell

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  • 1Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115-5026, United States.

Environmental Science & Technology
|September 1, 2022
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Summary

Determining sample size for microbial ecology studies is challenging. This research introduces a computational protocol using kernel divergence to optimize sample size, enhancing reproducibility in single-cell technology applications.

Keywords:
EBPRmachine learningsample size assessmentsingle-cell Raman microspectroscopysingle-cell technology

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Area of Science:

  • Microbial Ecology
  • Computational Biology
  • Analytical Chemistry

Background:

  • Advanced analytical methods like single-cell technologies offer deeper microbial ecology insights.
  • Determining adequate sample size is a major challenge due to unknown community complexity and resource limitations, hindering standardization.

Purpose of the Study:

  • To propose, test, and validate a computational sampling size assessment protocol.
  • To enable accurate determination of sample size for capturing desired information capacity and resolution levels.

Main Methods:

  • Developed a computational protocol utilizing kernel divergence, a metric comparing dataset distributional differences.
  • The method requires no human intervention or prior knowledge-based preclassification and makes minimal assumptions about data distribution.
  • Validated the protocol using Single-cell Raman Spectroscopy (SCRS) data from enhanced biological phosphorus removal (EBPR) activated sludge communities.

Main Results:

  • The kernel divergence metric effectively assesses distributional differences between datasets.
  • The validated protocol allows for the determination of sufficient sample size for various information capture goals.
  • Demonstrated flexibility in handling datasets with both linear and nonlinear relationships.

Conclusions:

  • The proposed computational protocol offers a standardized approach for sampling size optimization in microbial ecology.
  • Enhances comparability and reproducibility of experiments and analyses involving complex environmental samples.
  • The method's flexibility allows extension to other single-cell technologies and environmental applications with continuous data features.