Exploring the functional landscape of gene expression: directed search of large microarray compendia

• Published in: Bioinformatics Vol. 23; no. 20; pp. 2692 - 2699
• Main Authors: Hibbs, Matthew A., Hess, David C., Myers, Chad L., Huttenhower, Curtis, Li, Kai, Troyanskaya, Olga G.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 15.10.2007; Oxford Publishing Limited (England)
• Subjects: Algorithms; Biological and medical sciences; Biological research; Database Management Systems; Databases, Protein; Fundamental and applied biological sciences. Psychology; Gene Expression - physiology; Gene Expression Profiling - methods; General aspects; Information Storage and Retrieval - methods; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Oligonucleotide Array Sequence Analysis - methods; Reproducibility of Results; Saccharomyces cerevisiae; Saccharomyces cerevisiae - metabolism; Saccharomyces cerevisiae Proteins - metabolism; Sensitivity and Specificity; Gene; Gene expression; Microarray; Bioinformatics
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btm403

Motivation: The increasing availability of gene expression microarray technology has resulted in the publication of thousands of microarray gene expression datasets investigating various biological conditions. This vast repository is still underutilized due to the lack of methods for fast, accurate exploration of the entire compendium. Results: We have collected Saccharomyces cerevisiae gene expression microarray data containing roughly 2400 experimental conditions. We analyzed the functional coverage of this collection and we designed a context-sensitive search algorithm for rapid exploration of the compendium. A researcher using our system provides a small set of query genes to establish a biological search context; based on this query, we weight each dataset's relevance to the context, and within these weighted datasets we identify additional genes that are co-expressed with the query set. Our method exhibits an average increase in accuracy of 273% compared to previous mega-clustering approaches when recapitulating known biology. Further, we find that our search paradigm identifies novel biological predictions that can be verified through further experimentation. Our methodology provides the ability for biological researchers to explore the totality of existing microarray data in a manner useful for drawing conclusions and formulating hypotheses, which we believe is invaluable for the research community. Availability: Our query-driven search engine, called SPELL, is available at http://function.princeton.edu/SPELL Contact: ogt@genomics.princeton.edu Supplementary information: Several additional data files, figures and discussions are available at http://function.princeton.edu/SPELL/supplement