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Impact of missing data imputation methods on gene expression clustering and classification.

Marcilio C P de Souto1, Pablo A Jaskowiak2, Ivan G Costa3,4

  • 1Univ. Orleans, INSA Centre Val de Loire, LIFO EA 4022, Orleans, France. marcilio.desouto@univ-orleans.fr.

BMC Bioinformatics
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Summary

Simple imputation methods like mean or median replacement are as effective as complex strategies for gene expression data. Evaluating imputation methods should consider practical impacts on downstream analyses like classification and clustering.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Missing value imputation is crucial for gene expression data analysis.
  • Previous evaluations focused on imputation accuracy (e.g., RMSE).
  • Practical impacts, like preserving significant genes and classification power, are increasingly important.

Purpose of the Study:

  • To systematically evaluate the impact of five imputation methods on clustering and classification.
  • To assess if imputation methods improve downstream analysis performance using a statistical framework.
  • To compare simple imputation techniques against more complex strategies.

Main Methods:

  • Analysis of 12 cancer gene expression datasets.
  • Application of five well-known missing value imputation methods.
  • Evaluation using three clustering and four classification methods.
  • Utilized a novel statistical framework for performance assessment.

Main Results:

  • Imputation methods had a minor impact on classification and clustering performance.
  • Simple imputation methods (mean, median) performed comparably to complex methods.
  • The choice of imputation method did not significantly alter downstream analysis outcomes across datasets.

Conclusions:

  • The evaluated imputation methods show minimal influence on gene expression data classification and clustering.
  • Basic imputation strategies are often sufficient, performing as well as advanced techniques.
  • Dataset availability: http://costalab.org/Imputation/