Parallel classification and feature selection in microarray data using SPRINT

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.