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BioLattice: a framework for the biological interpretation of microarray gene expression data using concept lattice

Jihun Kim¹, Hee-Joon Chung, Yong Jung

¹Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, 28 Yongon-dong Chongno-gu, Seoul 110-799, Republic of Korea.

Journal of Biomedical Informatics

|December 21, 2007

Summary

BioLattice provides a novel mathematical framework for interpreting gene expression data by analyzing clusters within their experimental context. This approach offers a structured overview and improved biological insights from microarray experiments.

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Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Interpreting gene expression data from microarrays is challenging.
Current methods analyze clusters individually, lacking experimental context.
Associations with ontologies and pathways are often presented as long, unordered lists.

Purpose of the Study:

To develop a mathematical framework for comprehensive biological interpretation of gene expression data.
To provide a structured overview of experimental contexts by analyzing gene expression clusters.
To enable systematic comparison of experimental concepts and contexts.

Main Methods:

BioLattice framework based on concept lattice analysis.
Gene expression clusters as objects and annotations as attributes.
Utilizes set inclusion relationships to create a lattice of concepts.
Incremental addition of external knowledge resources (Gene Ontology, pathway graphs).
Quantitative structural analysis: 'prominent sub-lattice' and 'core-periphery' analyses.

Main Results:

BioLattice generates an 'executive' summary of the experimental context.
Enables systematic comparison of experimental concepts and contexts.
Web-based utility with interactive visualization using Scalable Vector Graphics.
Applied to real microarray datasets, yielding improved biological interpretations.

Conclusions:

BioLattice offers a powerful, context-aware approach to gene expression data interpretation.
Facilitates deeper understanding of biological properties and relationships from microarray experiments.
Enhances the biological interpretation of complex experimental datasets.