permGPU: Using graphics processing units in RNA microarray association studies.
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BACKGROUND: Many analyses of microarray association studies involve permutation, bootstrap resampling and cross-validation, that are ideally formulated as embarrassingly parallel computing problems. Given that these analyses are computationally intensive, scalable approaches that can take advantage of multi-core processor systems need to be developed. RESULTS: We have developed a CUDA based implementation, permGPU, that employs graphics processing units in microarray association studies. We illustrate the performance and applicability of permGPU within the context of permutation resampling for a number of test statistics. An extensive simulation study demonstrates a dramatic increase in performance when using permGPU on an NVIDIA GTX 280 card compared to an optimized C/C++ solution running on a conventional Linux server. CONCLUSIONS: permGPU is available as an open-source stand-alone application and as an extension package for the R statistical environment. It provides a dramatic increase in performance for permutation resampling analysis in the context of microarray association studies. The current version offers six test statistics for carrying out permutation resampling analyses for binary, quantitative and censored time-to-event traits.
SubjectGene Expression Profiling
Genetic Association Studies
Published Version (Please cite this version)10.1186/1471-2105-11-329
Publication InfoShterev, Ivo D; Jung, Sin-Ho; George, Stephen L; & Owzar, Kouros (2010). permGPU: Using graphics processing units in RNA microarray association studies. BMC Bioinformatics, 11. pp. 329. 10.1186/1471-2105-11-329. Retrieved from https://hdl.handle.net/10161/4333.
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Professor Emeritus of Biostatistics & Bioinformatics
Statistical issues related to the design, conduct, and analysis of clinical trials and related biomedical studies including sample size and study length determinations, sequential procedures, and the analysis of prognostic or predictive factors in clinical trials.
Professor of Biostatistics and Bioinformatics
Design of Clinical Trials Survival Analysis Longitudinal Data Analysis Clustered Data Analysis ROC Curve Analysis Design and Analysis of Microarray Studies
Professor of Biostatistics & Bioinformatics
cancer pharmacogenomicsdrug induced neuropathy, neutropenia and hypertensionstatistical genetics statistical methods for high-dimensional data copulas survival analysis statistical computing
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