A Bayesian Analysis Strategy for Cross-Study Translation of Gene Expression Biomarkers
A Bayesian Analysis Strategy for Cross-Study Translation of Gene Expression Biomarkers
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DOI：
10.2202/1544-6115.1436
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发表时间：
2009-01-01
影响因子：
0.9
通讯作者：
West, Mike
中科院分区：
文献类型：
--
作者：
Lucas, Joseph;Carvalho, Carlos;West, Mike
我们描述了一种策略，用于分析实验获得的基因表达特征及其翻译为人类观察数据。稀疏多变量回归模型用于鉴定在设计的实验中干预后代表下游生物途径事件的表达标记基因集。当转化为体内人类观察数据时，使用稀疏潜在因子模型的分析可以产生表征表达模式的多个定量因子，这些表达模式通常比在受控的体外环境中更复杂。在表达的共同模式，反映在体内明显的共变的各个方面的估计提供了一个增强的，模块化的观点的复杂性的生物协会的签名基因。这可以在实验研究下识别生物过程中的亚结构，并改善临床结果的生物标志物。我们从一个癌基因干预实验的详细研究中说明了这种方法，其中体外签名的体内因子分析产生了与潜在途径活动和染色体结构相关的生物学见解，并导致了几项癌症研究中癌症复发风险分层的改进。
We describe a strategy for the analysis of experimentally derived gene expression signatures and their translation to human observational data. Sparse multivariate regression models are used to identify expression signature gene sets representing downstream biological pathway events following interventions in designed experiments. When translated into in vivo human observational data, analysis using sparse latent factor models can yield multiple quantitative factors characterizing expression patterns that are often more complex than in the controlled, in vitro setting. The estimation of common patterns in expression that reflect all aspects of covariation evident in vivo offers an enhanced, modular view of the complexity of biological associations of signature genes. This can identify substructure in the biological process under experimental investigation and improved biomarkers of clinical outcomes. We illustrate the approach in a detailed study from an oncogene intervention experiment where in vivo factor profiling of an in vitro signature generates biological insights related to underlying pathway activities and chromosomal structure, and leads to refinements of cancer recurrence risk stratification across several cancer studies.