Parallel computing methods for analyzing gene expression relationships

Edward B. Suh; Edward R. Dougherty; Seungchan Kim; Daniel E. Russ; Robert L. Martino

doi:10.1117/12.427990

4 June 2001 Parallel computing methods for analyzing gene expression relationships

Edward B. Suh, Edward R. Dougherty, Seungchan Kim, Daniel E. Russ, Robert L. Martino

Proceedings Volume 4266, Microarrays: Optical Technologies and Informatics; (2001) https://doi.org/10.1117/12.427990
Event: BiOS 2001 The International Symposium on Biomedical Optics, 2001, San Jose, CA, United States

Abstract

This paper presents a parallel program for assessing the codetermination of gene transcriptional states from large- scale simultaneous gene expression measurements with cDNA microarrays. The parallel program is based on a nonlinear statistical framework recently proposed for the analysis of gene interaction via multivariate expression arrays. Parallel computing is key in the application of the statistical framework to a large set of genes because a prohibitive amount of computer time is required on a classical single-CPU machine. Our parallel program, named the Parallel Analysis of Gene Expression (PAGE) program, exploits inherent parallelism exhibited in the proposed codetermination prediction models. By running PAGE on 64 processors in Beowulf, a clustered parallel system, an analysis of melanoma cDNA microarray expression data has been completed within 12 days of computer time, an analysis that would have required about one and half years on a single-CPU computing system. A data visualization program, named the Visualization of Gene Expression (VOGE) program, has been developed to help interpret the massive amount of quantitative information produced by PAGE. VOGE provides graphical data visualization and analysis tools with filters, histograms, and accesses to other genetic databanks for further analyses of the quantitative information.

Citation Download Citation

Edward B. Suh, Edward R. Dougherty, Seungchan Kim, Daniel E. Russ, and Robert L. Martino "Parallel computing methods for analyzing gene expression relationships", Proc. SPIE 4266, Microarrays: Optical Technologies and Informatics, (4 June 2001); https://doi.org/10.1117/12.427990

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