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multiclassPairs: an R package to train multiclass pair-based classifier.

Nour-Al-Dain Marzouka1, Pontus Eriksson1

  • 1Department of Clinical Sciences, Division of Oncology, Lund University, 22381 Lund, Sweden.

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Summary

The multiclassPairs R package enables gene expression-based classification for multiple classes, extending k-Top Scoring Pairs (kTSP) algorithms. It offers novel methods for robust multiclass prediction in complex biological data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • k-Top Scoring Pairs (kTSP) algorithms are effective for binary gene expression-based classification.
  • Existing tools primarily support binary prediction, limiting applications in complex multiclass biological problems like tumor subtyping.

Purpose of the Study:

  • To introduce multiclassPairs, an R package for developing single-sample classifiers for multiclass gene expression data.
  • To extend the utility of pair-based classification methods to handle more than two biological classes.

Main Methods:

  • Implementation of a one-versus-rest kTSP strategy for multiclass prediction.
  • Development of a novel pair-based Random Forest approach for multiclass classification.
  • Inclusion of features for handling class imbalance, multiplatform data, missing values, and result visualization.

Main Results:

  • The multiclassPairs package provides robust methods for multiclass classification using gene expression data.
  • The package supports diverse data scenarios, including imbalanced classes and multiplatform datasets.
  • Novel visualization tools aid in interpreting training and testing outcomes.

Conclusions:

  • multiclassPairs effectively addresses the need for multiclass classification in gene expression analysis.
  • The package enhances the applicability of pair-based methods in complex biological research.
  • It offers a flexible and powerful tool for computational biologists and bioinformaticians.