Morris Jeffrey S, Baggerly Keith A, Gutstein Howard B, Coombes Kevin R
Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA.
Methods Mol Biol. 2010;641:143-66. doi: 10.1007/978-1-60761-711-2_9.
Proteomic profiling has the potential to impact the diagnosis, prognosis, and treatment of various diseases. A number of different proteomic technologies are available that allow us to look at many proteins at once, and all of them yield complex data that raise significant quantitative challenges. Inadequate attention to these quantitative issues can prevent these studies from achieving their desired goals, and can even lead to invalid results. In this chapter, we describe various ways the involvement of statisticians or other quantitative scientists in the study team can contribute to the success of proteomic research, and we outline some of the key statistical principles that should guide the experimental design and analysis of such studies.
蛋白质组分析有潜力影响各种疾病的诊断、预后和治疗。有多种不同的蛋白质组技术可供使用，这些技术能让我们同时检测多种蛋白质，并且所有这些技术都会产生复杂的数据，带来重大的定量挑战。对这些定量问题关注不足可能会妨碍这些研究实现预期目标，甚至可能导致无效结果。在本章中，我们描述了统计学家或其他定量科学家参与研究团队的各种方式如何有助于蛋白质组研究的成功，并且概述了一些应指导此类研究的实验设计和分析的关键统计原则。
