Clustering Formal Concepts to Discover Biologically Relevant Knowledge from Gene Expression Data

The production of high-throughput gene expression data has generated a crucial need for bioinformatics tools to generate biologically interesting hypotheses. Whereas many tools are available for extracting global patterns, less attention has been focused on local pattern discovery. We propose here a...

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Bibliographic Details
Published in:In silico biology 2007, Vol.7 (4-5), p.467-483
Main Authors: Blachon, Sylvain, Pensa, Ruggero G., Besson, Jérémy, Robardet, Céline, Boulicaut, Jean-François, Gandrillon, Olivier
Format: Article
Language:English
Subjects:
Biological and medical sciences
Cellular Biology
Cluster Analysis
Computational Biology
Computational Biology - instrumentation
Fundamental and applied biological sciences. Psychology
Gene Expression
General aspects
Genome, Human
Humans
Life Sciences
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Pattern Recognition, Automated
Pattern Recognition, Automated - methods
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Summary:The production of high-throughput gene expression data has generated a crucial need for bioinformatics tools to generate biologically interesting hypotheses. Whereas many tools are available for extracting global patterns, less attention has been focused on local pattern discovery. We propose here an original way to discover knowledge from gene expression data by means of the so-called formal concepts which hold in derived Boolean gene expression datasets. We first encoded the over-expression properties of genes in human cells using human SAGE data. It has given rise to a Boolean matrix from which we extracted the complete collection of formal concepts, i.e., all the largest sets of over-expressed genes associated to a largest set of biological situations in which their over-expression is observed. Complete collections of such patterns tend to be huge. Since their interpretation is a time-consuming task, we propose a new method to rapidly visualize clusters of formal concepts. This designates a reasonable number of Quasi-Synexpression-Groups (QSGs) for further analysis. The interest of our approach is illustrated using human SAGE data and interpreting one of the extracted QSGs. The assessment of its biological relevancy leads to the formulation of both previously proposed and new biological hypotheses.
ISSN:1386-6338
1434-3207
DOI:10.3233/ISI-2007-00321