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Unsupervised gene set testing based on random matrix theory.

H Robert Frost1, Christopher I Amos2

  • 1Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, 03755, NH, USA. rob.frost@dartmouth.edu.

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|November 5, 2016
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Summary
This summary is machine-generated.

Two new unsupervised gene set testing methods, the Marchenko-Pastur Distribution Test (MPDT) and Tracy-Widom Test (TWT), offer superior statistical performance for analyzing genomic data. These novel bioinformatics tools effectively support biologically relevant use cases in pathway analysis.

Keywords:
Gene set testingMarc̆enko-PasturPathway analysisRandom matrix theoryTracy-Widom

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Area of Science:

  • Bioinformatics
  • Genomics
  • Statistical analysis

Background:

  • Gene set testing (pathway analysis) statistically evaluates biologically meaningful genomic variable sets.
  • Existing methods are limited for unsupervised applications, particularly for specific biological use cases.
  • There is a need for advanced unsupervised gene set testing techniques.

Purpose of the Study:

  • Introduce two novel unsupervised gene set testing methods: Marchenko-Pastur Distribution Test (MPDT) and Tracy-Widom Test (TWT).
  • Address limitations in current unsupervised pathway analysis tools.
  • Provide effective methods for analyzing genomic data in unsupervised settings.

Main Methods:

  • Developed MPDT and TWT based on random matrix theory.
  • Supported both self-contained and competitive null hypotheses.
  • Compared MPDT and TWT against classic multivariate tests and the Spectral Gene Set Enrichment (SGSE) method.

Main Results:

  • MPDT and TWT demonstrated superior statistical performance in simulation studies.
  • Evaluated methods using weighted p-value analysis on real gene expression datasets.
  • Confirmed effectiveness on MSigDB gene sets.

Conclusions:

  • MPDT and TWT are effective novel tools for unsupervised gene set analysis.
  • These methods offer improved statistical performance over existing techniques.
  • MPDT and TWT generate biologically significant results from real genomic data.