Survival analysis of longitudinal microarrays
Survival analysis of longitudinal microarrays
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DOI：
10.1093/bioinformatics/btl450
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发表时间：
2006-11-01
期刊：
影响因子：
5.8
通讯作者：
Schoenfeld, David A.
中科院分区：
文献类型：
--
作者：
Rajicic, Natasa;Finkelstein, Dianne M.;Schoenfeld, David A.
动机：将基因表达与各种临床和表型特征联系起来的方法的发展是基因组研究的一个活跃领域。例如，科学家们希望这种分析可以描述基因功能与临床事件（如死亡或康复）之间的关系。现有方法可将基因表达与分类或连续的测量相关联，但将表达与观察到的事件时间（如死亡时间、反应时间或复发时间）相关联的工作较少。当基因表达随时间变化而变化时，有一些方法可以区分时间模式。然而，还没有提出方法来分析纵向收集的微阵列的生存。结果：我们描述了一种纵向基因表达数据的生存分析方法。我们构建了一种测量事件发生时间和随时间收集的基因表达之间的关联的方法。使用排列和错误发现率的控制来处理统计显著性。我们提出的方法是在一个来自炎症和损伤反应的多中心研究的数据集上进行说明的，该研究旨在揭示患者在遭受创伤性损伤后可能有显著不同结果的生物学原因（www.gluegrant.org）。
Motivation: The development of methods for linking gene expressions to various clinical and phenotypic characteristics is an active area of genomic research. Scientists hope that such analysis may, for example, describe relationships between gene function and clinical events such as death or recovery. Methods are available for relating gene expression to measurements that are categorized or continuous, but there is less work in relating expressions to an observed event time such as time to death, response or relapse. When gene expressions are measured over time, there are methods for differentiating temporal patterns. However, methods have not yet been proposed for the survival analysis of longitudinally collected microarrays.Results: We describe an approach for the survival analysis of longitudinal gene expression data. We construct a measure of association between the time to an event and gene expressions collected over time. Statistical significance is addressed using permutations and control of the false discovery rate. Our proposed method is illustrated on a dataset from a multi-center research study of inflammation and response to injury that aims to uncover the biological reasons why patients can have dramatically different outcomes after suffering a traumatic injury (www.gluegrant.org).