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Identifying gene-specific subgroups: an alternative to biclustering.

Vincent Branders1, Pierre Schaus2, Pierre Dupont2

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|December 5, 2019
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Summary

The K-CPGC algorithm identifies gene expression patterns by finding max-sum submatrices, outperforming traditional biclustering for biological relevance and interpretability.

Keywords:
BiclusteringGene enrichment analysisGene expression analysisIdentification of significant GO terms

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Transcriptome analysis reveals cellular processes via gene expression patterns.
  • Biclustering identifies gene subsets with similar expression across sample subgroups.
  • Outliers can cause standard biclustering to miss relevant gene-sample submatrices.

Purpose of the Study:

  • To introduce the K-CPGC algorithm for identifying K relevant submatrices.
  • To demonstrate K-CPGC's superiority over biclustering algorithms in finding biologically relevant gene subsets.
  • To provide a statistical validation protocol for assessing algorithm performance.

Main Methods:

  • The K-CPGC algorithm identifies K max-sum submatrices in gene expression data.
  • Comparative experiments were conducted on 35 human and yeast gene expression datasets.
  • Statistical validation involved Friedman tests and Hochberg's procedure.

Main Results:

  • K-CPGC outperformed several biclustering algorithms (CCA, xMOTIFs, ISA, QUBIC, Plaid, Spectral).
  • Gene enrichment analysis showed K-CPGC identifies more statistically significant and biologically relevant gene subsets.
  • Identified Gene Ontology (GO) terms were consistent with experimental conditions, confirming biological relevance.

Conclusions:

  • K-CPGC is an efficient algorithm for identifying interpretable max-sum submatrices in large gene expression matrices.
  • The method yields more significantly enriched gene subsets and reliable GO terms.
  • An R package implementation is available for broader use.