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Integrative gene set analysis of multi-platform data with sample heterogeneity.

Jun Hu1, Jung-Ying Tzeng2

  • 1Bioinformatics Research Center, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA, Division of Bioinformatics, Omicsoft Inc., 200 Cascade Pointe Lane, Suite 101, Cary, NC 27513, USA, Department of Statistics, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA and Department of Statistics, National Cheng-Kung University, No.1, University Road, Tainan 701, TaiwanBioinformatics Research Center, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA, Division of Bioinformatics, Omicsoft Inc., 200 Cascade Pointe Lane, Suite 101, Cary, NC 27513, USA, Department of Statistics, North Carolina State University, Ricks Hall, 1 Lampe Dr., Raleigh, NC 27607, USA and Department of Statistics, National Cheng-Kung University, No.1, University Road, Tainan 701, Taiwan.

Bioinformatics (Oxford, England)
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Summary

New gene set analysis methods improve performance for multi-platform genomic data, especially when sample heterogeneity is present. The multi-platform Mann-Whitney statistics method shows higher power in these complex scenarios.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Gene set analysis is crucial for interpreting large-scale genomic studies by analyzing genes with shared biological functions.
  • Multi-platform genomic data integration is increasingly common due to technological advancements.
  • Existing integrative gene set analysis methods require evaluation, particularly concerning sample heterogeneity.

Purpose of the Study:

  • To evaluate the performance of existing integrative gene set analysis methods under various scenarios, including sample heterogeneity.
  • To develop novel methods for gene set analysis that effectively handle multi-platform genomic data with heterogeneity.

Main Methods:

  • Comparative simulation analysis using The Cancer Genome Atlas (TCGA) breast cancer dataset.
  • Development of three new methods: multi-platform Mann-Whitney statistics (non-parametric), multi-platform outlier robust T-statistics (non-parametric), and multi-platform likelihood ratio statistics (parametric).

Main Results:

  • Existing gene set analysis methods are less effective with heterogeneous samples.
  • The proposed multi-platform Mann-Whitney statistics method demonstrates superior power for heterogeneous samples.
  • The new methods successfully identified novel pathways in TCGA datasets that were missed by other strategies.

Conclusions:

  • The developed methods offer improved performance for multi-platform gene set analysis, particularly in the presence of sample heterogeneity.
  • The multi-platform Mann-Whitney statistics method is a powerful tool for analyzing heterogeneous genomic data.
  • These advancements enable more robust and biologically informative discoveries from complex genomic datasets.