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Updated: May 20, 2026
07:35
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
iBBiG: iterative binary bi-clustering of gene sets.
Daniel Gusenleitner1, Eleanor A Howe, Stefan Bentink
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
Bioinformatics (Oxford, England)
|July 14, 2012
Summary
This study introduces iBBiG, a novel bi-clustering algorithm for genomics meta-analysis. It effectively integrates diverse datasets to uncover gene set-phenotype associations, aiding in disease subtype prediction.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Integrating diverse genomics data for meta-analysis is challenging due to platform differences.
- Gene set-based meta-analysis offers a promising, yet underdeveloped, approach for data integration.
Purpose of the Study:
- To develop and validate a novel statistical method for gene set-based genomics meta-analysis.
- To identify coordinately associated gene sets and phenotypes across multiple studies, even with unmatched samples.
Main Methods:
- Developed an iterative bi-clustering algorithm (iBBiG) that transforms gene expression profiles into binary gene set profiles.
- Applied gene set enrichment analysis and iBBiG to identify clusters of gene sets associated with phenotype clusters.
- Optimized iBBiG for large-scale, diverse genomics data and overlapping clusters without prior size knowledge.
Main Results:
- iBBiG outperforms common clustering methods on simulated data, demonstrating robustness to noise and discovery of diverse, overlapping clusters.
- Applied to breast cancer meta-analysis, iBBiG identified novel gene set-phenotype associations.
- These associations successfully predicted tumor metastases within specific tumor subtypes.
Conclusions:
- iBBiG provides a powerful new tool for gene set-based genomics meta-analysis.
- The method facilitates the discovery of biologically relevant associations and improves predictive capabilities for disease subtypes.
- iBBiG is available as an R/Bioconductor package for broader scientific application.
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