Schmidberger Markus, Vicedo Esmeralda, Mansmann Ulrich
Division of Biometrics and Bioinformatics, IBE, University of Munich, 81377 Munich, Germany. Email:
Bioinform Biol Insights. 2009 Jul 22;3:83-7. doi: 10.4137/bbi.s3060.
Microarray data repositories as well as large clinical applications of gene expression allow to analyse several hundreds of microarrays at one time. The preprocessing of large amounts of microarrays is still a challenge. The algorithms are limited by the available computer hardware. For example, building classification or prognostic rules from large microarray sets will be very time consuming. Here, preprocessing has to be a part of the cross-validation and resampling strategy which is necessary to estimate the rule's prediction quality honestly.This paper proposes the new Bioconductor package affyPara for parallelized preprocessing of Affymetrix microarray data. Partition of data can be applied on arrays and parallelization of algorithms is a straightforward consequence. The partition of data and distribution to several nodes solves the main memory problems and accelerates preprocessing by up to the factor 20 for 200 or more arrays.affyPara is a free and open source package, under GPL license, available form the Bioconductor project at www.bioconductor.org. A user guide and examples are provided with the package.
微阵列数据存储库以及基因表达的大型临床应用使得能够一次性分析数百个微阵列。对大量微阵列进行预处理仍然是一项挑战。算法受到可用计算机硬件的限制。例如，从大型微阵列集构建分类或预后规则将非常耗时。在此，预处理必须成为交叉验证和重采样策略的一部分，这对于诚实地评估规则的预测质量是必要的。本文提出了新的Bioconductor软件包affyPara，用于对Affymetrix微阵列数据进行并行预处理。数据分区可应用于阵列，算法并行化是直接的结果。数据分区和分布到多个节点解决了主内存问题，并将200个或更多阵列的预处理加速了20倍。affyPara是一个免费的开源软件包，遵循GPL许可，可从www.bioconductor.org的Bioconductor项目获得。该软件包提供了用户指南和示例。
