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Related Experiment Video

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Gene Selection in a Single Cell Gene Space Based on D-S Evidence Theory.

Zhaowen Li1, Qinli Zhang2, Pei Wang1

  • 1Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, Yulin Normal University, Yulin, 537000, Guangxi, People's Republic of China.

Interdisciplinary Sciences, Computational Life Sciences
|April 28, 2022
PubMed
Summary

This study introduces a novel gene selection method using Dempster-Shafer (D-S) evidence theory to reduce noise and uncertainty in single-cell gene expression data. The proposed algorithm enhances classification and clustering performance while achieving high gene reduction rates.

Keywords:
Belief functionD–S evidence theoryGene selectionPlausibility functionSingle cell gene spaceTolerance relation

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Data Science

Background:

  • High-dimensional single-cell gene expression data presents challenges due to noise and redundancy, leading to uncertainty.
  • Traditional methods like Bayesian probability theory have limitations in handling such uncertain data.
  • Dempster-Shafer (D-S) evidence theory offers a robust framework for managing uncertainty with weaker conditions than Bayesian approaches.

Purpose of the Study:

  • To develop and evaluate a gene selection strategy for single-cell gene spaces to mitigate noise and redundancy.
  • To leverage D-S evidence theory for quantifying and managing uncertainty in gene expression data.
  • To improve classification and clustering performance in single-cell analysis through effective gene selection.

Main Methods:

  • Defining cell-to-cell distance based on individual gene expression values.
  • Establishing tolerance relations derived from the defined cell distances.
  • Applying belief and plausibility functions, based on tolerance classes, to model single-cell gene space uncertainty.
  • Developing and implementing gene selection algorithms utilizing these belief and plausibility functions.

Main Results:

  • The developed belief and plausibility functions effectively measure the uncertainty inherent in single-cell gene spaces.
  • The proposed gene selection algorithms demonstrate superior performance compared to existing state-of-the-art methods.
  • The algorithm achieves significant improvements in classification and clustering accuracy.
  • A high gene reduction rate was observed, indicating efficient noise and redundancy removal.

Conclusions:

  • D-S evidence theory provides a powerful tool for addressing uncertainty in high-dimensional single-cell gene expression data.
  • The proposed gene selection method effectively reduces noise and redundancy, enhancing downstream analysis.
  • This approach offers a promising advancement for improving the accuracy and efficiency of single-cell data analysis, particularly for classification and clustering tasks.