Abstract
Background: Many analyses of microarray association studies involve permutation and 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 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.
The homepage for permGPU: http://code.google.com/p/permgpu/
Disciplines
Bioinformatics | Computational Biology | Microarrays | Numerical Analysis and Computation
Suggested Citation
Shterev, Ivo D.; Jung, Sin-Ho; George, Stephen L.; and Owzar, Kouros, "permGPU: Using Graphics Processing Units in RNA Microarray Association Studies" (March 2010). Duke Biostatistics and Bioinformatics (B&B) Working Paper Series. Working Paper 8.
http://biostats.bepress.com/dukebiostat/art8
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Bioinformatics Commons, Computational Biology Commons, Microarrays Commons, Numerical Analysis and Computation Commons