R2GUESS is an R package that runs the GUESS code, a computationally optimised C++ implementation of a fully Bayesian variable selection approach (Evolutionary Stochastic Search; ESS algorithm) that can analyse single and multiple responses in an integrated way and can scale to genome-wide datasets. The multi-SNP model underlying GUESS seeks for the best combinations of SNPs to predict the (possibly multivariate) outcome(s). The program uses packages from the GNU Scientific Library (GSL) and offers the possibility to re-route computationally intensive linear algebra operations towards the Graphical Processing Unit (GPU) through the use of proprietary CULA-dense library. The use of GPU-based numerical libraries implies extensive data transfer between the memory/CPU and the GPU, which, in turn, can be computationally expensive. Consequently, for smaller data sets (as the example provided in the package) in which matrix operations are not rate-limiting, the CPU version of GUESS may be more computationally efficient. To ensure an optimal use of the algorithm, and to enable running GUESS on non-CULA compatible systems, the call to GPU-based calculations within GUESS can be switched off through a single argument, and will automatically be disabled in non CULA-compatible systems. Extensive documentation of the source C++ code is available at http://www.bgx.org.uk/software/GUESS_Doc_short.pdf.
The current manual details most features of the GUESS algorithm and focuses on the built-in scripts enabling easy runs and automatic post-processing of outputs from GUESS.
Bottolo L, Chadeau-Hyam M ; et al. GUESS-ing polygenic associations with multiple phenotypes using a GPU-based Evolutionary Stochastic Search algorithm. PLoS Genetics. 2013;9(8):e1003657.
Bottolo L, Chadeau-Hyam M ; et al. ESS++ : a C++ Object-Oriented Algorithm for Bayesian Stochastic Search Model Exploration. Bioinformatics. 2011 ; 27 :587-588.
Bottolo L and Richardson S (2010). Evolutionary Stochastic Search for Bayesian model exploration. Bayesian Analysis 5(3), 583-618.
Petretto E, Bottolo L, Langley SR, Heinig M, McDermott-Roe C, Sarwar R, Pravenec M, Hubner N, Aitman TJ, Cook SA and Richardson S (2010). New insights into the genetic control of gene expression using a Bayesian multi-tissue approach. PLoS Comput. Biol., 6(4), e1000737.
R2GUESS, as.ESS.object,
plotMPPI, plot.ESS,
print.ESS, summary.ESS