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A comparison of rule-based and centroid single-sample multiclass predictors for transcriptomic classification.

Pontus Eriksson1, Nour-Al-Dain Marzouka1, Gottfrid Sjödahl2

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Single sample predictors (SSPs) using gene-pair rules show strong performance for tumor subtyping. These methods offer accurate, robust classification across platforms, with a new Random Forest approach providing informative scores.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression-based tumor subtyping is challenging.
  • Traditional classifiers require sample comparisons, limiting isolated use.
  • Single sample predictors (SSPs) analyze expression patterns within a single sample.

Purpose of the Study:

  • Evaluate multiclass SSPs based on gene-pair rules.
  • Compare performance against centroid-based methods.
  • Assess accuracy, purity robustness, cross-platform compatibility, and score informativeness.

Main Methods:

  • Assessed k-Top Scoring Pairs (k-TSP), Absolute Intrinsic Molecular Subtyping (AIMS), and a Random Forest (RF) SSP.
  • Compared SSPs to centroid methods using centered and raw expression values.
  • Evaluated performance based on accuracy, tumor purity, platform differences, and prediction scores.

Main Results:

  • Gene-pair-based SSPs demonstrated excellent performance in expression-based classification.
  • k-TSP and RF achieved high accuracy with informative prediction scores.
  • Cross-platform compatibility requires verification on new datasets, despite training on mixed data.

Conclusions:

  • Gene-pair rule-based SSPs are effective for multiclass prediction tasks like tumor subtyping.
  • The Random Forest SSP offers a robust and informative approach.
  • The 'multiclassPairs' R package facilitates the use of these methods.