Nonparametric pathway-based regression models for analysis of genomic data
Nonparametric pathway-based regression models for analysis of genomic data
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DOI：
10.1093/biostatistics/kxl007
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发表时间：
2007-04-01
期刊：
影响因子：
2.1
通讯作者：
Li, Hongzhe
中科院分区：
文献类型：
--
作者：
Wei, Zhi;Li, Hongzhe
高通量基因组数据为识别与各种临床表型相关的途径和基因提供了机会。除了这些基因组数据外，另一个有价值的数据来源是关于基因和途径的生物学知识，这些知识可能与许多复杂疾病的表型有关。这些知识的数据库通常被称为元数据。在微阵列数据分析中，这些元数据目前通过基因集富集分析以事后方式进行探索，但很少用于建模步骤。我们建议开发和评估一种基于路径的梯度下降增强程序，用于非参数路径回归（NPR）分析，以有效地整合基因组数据和元数据。这种NPR模型同时考虑多种途径，并允许途径内基因之间的复杂相互作用，可用于识别与表型变异相关的途径和基因。这些方法还提供了一种替代方案，通过将分析限制在相关途径中基因之间生物学上合理的相互作用来调解大量潜在相互作用的问题。我们的仿真研究表明，所提出的增强程序确实可以识别相关途径。对乳腺癌远端转移基因表达数据集的应用发现，淋巴结阴性乳腺癌患者中Wnt、细胞凋亡和细胞周期调节通路更可能与远端转移风险相关。另外两组乳腺癌基因表达数据的分析结果表明，金属内肽酶（metalloendoeptidases, MMPs）和MMP抑制剂的通路以及细胞增殖、细胞生长和维持对乳腺癌的复发和生存至关重要。我们还观察到，通过结合通路信息，我们可以更好地预测癌症复发。
High-throughout genomic data provide an opportunity for identifying pathways and genes that are related to various clinical phenotypes. Besides these genomic data, another valuable source of data is the biological knowledge about genes and pathways that might be related to the phenotypes of many complex diseases. Databases of such knowledge are often called the metadata. In microarray data analysis, such metadata are currently explored in post hoc ways by gene set enrichment analysis but have hardly been utilized in the modeling step. We propose to develop and evaluate a pathway-based gradient descent boosting procedure for nonparametric pathways-based regression (NPR) analysis to efficiently integrate genomic data and metadata. Such NPR models consider multiple pathways simultaneously and allow complex interactions among genes within the pathways and can be applied to identify pathways and genes that are related to variations of the phenotypes. These methods also provide an alternative to mediating the problem of a large number of potential interactions by limiting analysis to biologically plausible interactions between genes in related pathways. Our simulation studies indicate that the proposed boosting procedure can indeed identify relevant pathways. Application to a gene expression data set on breast cancer distant metastasis identified that Wnt, apoptosis, and cell cycle-regulated pathways are more likely related to the risk of distant metastasis among lymph-node-negative breast cancer patients. Results from analysis of other two breast cancer gene expression data sets indicate that the pathways of Metalloendopeptidases (MMPs) and MMP inhibitors, as well as cell proliferation, cell growth, and maintenance are important to breast cancer relapse and survival. We also observed that by incorporating the pathway information, we achieved better prediction for cancer recurrence.