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Spectral gene set enrichment (SGSE).

H Robert Frost1,2,3, Zhigang Li4,5, Jason H Moore6,7,8

  • 1Institute of Quantitative Biomedical Sciences, Geisel School of Medicine, Lebanon, NH, 03756, USA. rob.frost@dartmouth.edu.

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We introduce spectral gene set enrichment (SGSE), a new unsupervised method for analyzing genomic data without clinical phenotypes. SGSE accurately identifies biological signals in noisy data by leveraging principal components and spectral structure.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene set testing traditionally requires supervised analysis with clinical phenotypes.
  • Unsupervised methods often rely on clustering, which can be algorithm-dependent.
  • A need exists for robust unsupervised gene set analysis in the absence of phenotype data.

Purpose of the Study:

  • To develop a novel unsupervised method for gene set enrichment analysis.
  • To assess the association between gene sets and the spectral structure of genomic data.
  • To provide a robust alternative to cluster-based unsupervised methods.

Main Methods:

  • Propose spectral gene set enrichment (SGSE) utilizing principal component gene set enrichment (PCGSE).
  • Combine PC-level p-values using a weighted Z-method, incorporating PC variance and Tracy-Widom test p-values.
  • Evaluate performance on simulated and real microarray gene expression data.

Main Results:

  • SGSE accurately recovers spectral features from noisy simulated data.
  • Demonstrate superior performance of SGSE compared to standard cluster-based techniques.
  • Identify associations between MSigDB gene sets and the variance structure of gene expression data.

Conclusions:

  • Unsupervised gene set testing reveals biological signals in high-dimensional genomic data.
  • SGSE is independent of clustering or network algorithms, relying on sample principal components.
  • SGSE effectively utilizes PC eigenvalue significance to mitigate noise in data analysis.