Optimization of a parallel permutation testing function for the SPRINT R package

Petrou, Savvas; Sloan, Terence; Mewissen, Muriel; Forster, Thorsten; Piotrowski, Michał; Dobrzelecki, Bartosz; Ghazal, Peter; Trew, Arthur

doi:10.1002/cpe.1787

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…However, it is unfortunate to see that most of the software programs perform PTs under univariate designs and the facility to deal with MPTs is still lacking. It is also noted that we are happy to see that parallel computing technique combined with high performance computing has begun to be employed in genetic research (Petrou et al, 2011). Although it only allows for the applications of PTs for multiple comparisons, we regard it as a revolution for PTs.…”

Section: Discussionmentioning

confidence: 99%

Permutation Tests in the Educational and Behavioral Sciences

et al. 2014

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Over the past two decades, permutation tests (PTs) have received much attention in the educational and behavioral sciences. The aim of this article is to review the theoretical developments of PTs, the active areas in the educational and behavioral research using PTs, and the types of analysis under which PTs have been applied. We obtained 224 published articles, which included 141 theoretical articles and 83 application articles. After scrutinizing each article, we are happy to see that (1) some researchers began to advocate introducing PTs into basic statistics training; (2) computing load for PTs may be reduced dramatically by some intelligent algorithms; (3) PTs began to be applied in new areas such as studies on the relationship between brain and behavior and the relationship between gene and behavior; (4) besides simple types of analysis such as independent two-group comparison, PTs can also be carried out under more complex situations such as multivariate analysis. However, we should also notice that PTs are still mostly used for simple analyses (e.g., randomness analysis).

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Section: Discussionmentioning

confidence: 99%

Permutation Tests in the Educational and Behavioral Sciences

et al. 2014

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“…A number of parallel functions (namely correlation, clustering, rank product, permutation test, bootstrap, random forest classifier and the apply method) are available in SPRINT. The SPRINT package has been tested on several HPC architectures including the UK’s national supercomputer service HECToR and has exhibited very good performance and speedup up to 512 processors ([10], [11], [12]) The SPRINT package has therefore proven to be an efficient parallel solution for R users on dedicated HPC infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Parallel R in the Cloud with SPRINT

Piotrowski¹,

McGilvary²,

Sloan³

et al. 2013

Methods Inf Med

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Background-Advances in DNA Microarray devices and next-generation massively parallel DNA sequencing platforms have led to an exponential growth in data availability but the arising opportunities require adequate computing resources. High Performance Computing (HPC) in the Cloud offers an affordable way of meeting this need.Objectives-Bioconductor, a popular tool for high-throughput genomic data analysis, is distributed as add-on modules for the R statistical programming language but R has no native capabilities for exploiting multi-processor architectures. SPRINT is an R package that enables easy access to HPC for genomics researchers. This paper investigates: setting up and running SPRINT-enabled genomic analyses on Amazon's Elastic Compute Cloud (EC2), the advantages of submitting applications to EC2 from different parts of the world and, if resource underutilization can improve application performance. Methods-The

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“…These drop‐in replacements are parallelised using the Message Passing Interface (MPI) , with data distribution carried out transparently from the end‐user's point of view. For a more detailed description of the SPRINT architecture, see . Users of SPRINT write R analysis scripts as they would have performed previously for serial analysis.…”

Section: Introductionmentioning

confidence: 99%

Parallel classification and feature selection in microarray data using SPRINT

Mitchell

Sloan

Mewissen

et al. 2012

Concurrency and Computation

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SUMMARYThe statistical language R is favoured by many biostatisticians for processing microarray data. In recent times, the quantity of data that can be obtained in experiments has risen significantly, making previously fast analyses time consuming or even not possible at all with the existing software infrastructure. High performance computing (HPC) systems offer a solution to these problems but at the expense of increased complexity for the end user. The Simple Parallel R Interface is a library for R that aims to reduce the complexity of using HPC systems by providing biostatisticians with drop-in parallelised replacements of existing R functions. In this paper we describe parallel implementations of two popular techniques: exploratory clustering analyses using the random forest classifier and feature selection through identification of differentially expressed genes using the rank product method.

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Optimization of a parallel permutation testing function for the SPRINT R package

Cited by 4 publications

References 8 publications

Permutation Tests in the Educational and Behavioral Sciences

Permutation Tests in the Educational and Behavioral Sciences

Exploiting Parallel R in the Cloud with SPRINT

Parallel classification and feature selection in microarray data using SPRINT

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