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Updated: Dec 22, 2025

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
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Bayesian approach to discriminant problems for count data with application to multilocus short tandem repeat dataset.

Koji Tsukuda1,2, Shuhei Mano3, Toshimichi Yamamoto4

  • 1Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, 153-8902, Tokyo, Japan.

Statistical Applications in Genetics and Molecular Biology
|May 5, 2020
PubMed
Summary

This study introduces an improved discriminant analysis method for determining individual DNA populations using Short Tandem Repeats (STRs). The new approach enhances classification accuracy and is robust to sample size imbalances, outperforming previous methods.

Keywords:
Bayes factorDNA polymorphismDirichlet–multinomial modellocus selectionsample size imbalance

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Area of Science:

  • Forensic Genetics
  • Population Genetics
  • Statistical Genetics

Background:

  • Short Tandem Repeats (STRs) are crucial DNA polymorphisms used in forensic science.
  • Discriminant analysis is a statistical method for classifying individuals into populations based on genetic markers.
  • Previous methods for population determination using STR data faced challenges with sample size imbalance and locus selection.

Purpose of the Study:

  • To develop an improved discriminant analysis method for population assignment using STR data.
  • To enhance robustness against sample size imbalance in population genetics studies.
  • To refine locus selection and prior distribution parameter treatment in STR analysis.

Main Methods:

  • Utilized discriminant analysis based on the Bayes factor.
  • Proposed an improved statistical method for STR data analysis.
  • Conducted computer simulations to evaluate method performance and robustness.
  • Investigated threshold adjustment as a countermeasure for sample size imbalance.

Main Results:

  • Achieved improved classification accuracy, with maximum g-mean increasing from 0.748 to 0.830 and AUC from 0.867 to 0.935.
  • The proposed method demonstrated greater robustness to sample size imbalance compared to previous approaches.
  • Threshold adjustment was confirmed as an effective strategy for mitigating sample size imbalance effects.

Conclusions:

  • The developed discriminant analysis method offers superior performance and robustness for population assignment using STR data.
  • The findings have implications for improving the accuracy and reliability of forensic DNA analysis.
  • The study highlights the importance of addressing sample size imbalance in genetic data analysis.