Lubomirski Mariusz, D'Andrea Michael R, Belkowski Stanley M, Cabrera Javier, Dixon James M, Amaratunga Dhammika
Johnson & Johnson Pharmaceutical Research & Development LLC, Spring House, Pennsylvania 19477, USA.
J Comput Biol. 2007 Apr;14(3):350-9. doi: 10.1089/cmb.2006.0116.
DNA microarrays are a well-known and established technology in biological and pharmaceutical research providing a wealth of information essential for understanding biological processes and aiding drug development. Protein microarrays are quickly emerging as a follow-up technology, which will also begin to experience rapid growth as the challenges in protein to spot methodologies are overcome. Like DNA microarrays, their protein counterparts produce large amounts of data that must be suitably analyzed in order to yield meaningful information that should eventually lead to novel drug targets and biomarkers. Although the statistical management of DNA microarray data has been well described, there is no available report that offers a successful consolidated approach to the analysis of high-throughput protein microarray data. We describe the novel application of a statistical methodology to analyze the data from an immune response profiling assay using human protein microarray with over 5000 proteins on each chip.
DNA微阵列是生物和制药研究中一项广为人知且成熟的技术,它提供了大量对于理解生物过程和辅助药物开发至关重要的信息。蛋白质微阵列正迅速成为一项后续技术,随着蛋白质点样方法方面的挑战被克服,它也将开始经历快速增长。与DNA微阵列一样,它们的蛋白质对应物会产生大量数据,必须对这些数据进行适当分析,以便得出有意义的信息,最终应能产生新的药物靶点和生物标志物。尽管DNA微阵列数据的统计管理已有详尽描述,但尚无一份报告提供一种成功的综合方法来分析高通量蛋白质微阵列数据。我们描述了一种统计方法的新应用,该方法用于分析来自免疫反应谱分析试验的数据,该试验使用的人类蛋白质微阵列每个芯片上有超过5000种蛋白质。