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AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number
Aaron M Newman1, James B Cooper
1Biomolecular Science and Engineering Program, University of California, Santa Barbara, CA 93106, USA.
BMC Bioinformatics
|March 6, 2010
Summary
AutoSOME, a new informatics method, automatically clusters high-dimensional gene expression data without prior knowledge of cluster number or structure. This approach reveals novel biological insights from "omics" data, including cancer cell line variations and pluripotency gene networks.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Clustering high-dimensional gene expression data is crucial for "omics" biology.
- Natural datasets often have fuzzy structures, requiring prior knowledge of cluster number and geometry for computational analysis.
Purpose of the Study:
- To develop a novel informatics method for automatic clustering of high-dimensional gene expression data.
- To identify discrete and fuzzy data clusters without prior knowledge of their number or structure.
Main Methods:
- Integration of machine learning, cartography, and graph theory.
- Application to self-organizing map ensembles of high-dimensional data.
- Development of the AutoSOME (Automatic Self-Organizing Map Ensemble) method.
Main Results:
- AutoSOME successfully identified data clusters in diverse datasets, including whole genome microarray data.
- Visualization revealed unexpected variations among cancer cell lines.
- Analysis of pluripotent stem cells identified over 3400 up-regulated genes and revised the scale of a pluripotency protein-protein interaction network.
Conclusions:
- AutoSOME extracts systems-level insights from high-dimensional microarray data without prior knowledge or filtration.
- The method's generality makes it applicable to various data-intensive applications, including deep sequencing.
- AutoSOME is publicly available for download.
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