Parallel classification and feature selection in microarray data using SPRINT

Lawrence Mitchell, Terence M. Sloan, Muriel Mewissen, Peter Ghazal, Thorsten Forster, Michal Piotrowski, Arthur Trew

Research output: Contribution to journalArticlepeer-review

Abstract

The 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.
Original languageEnglish
Pages (from-to)854-865
Number of pages12
JournalConcurrency and Computation: Practice and Experience
Volume26
Issue number4
Early online date13 Sep 2012
DOIs
Publication statusPublished - Mar 2014

Fingerprint

Dive into the research topics of 'Parallel classification and feature selection in microarray data using SPRINT'. Together they form a unique fingerprint.

Cite this