A STATISTICAL FRAMEWORK FOR DATA INTEGRATION THROUGH GRAPHICAL MODELS WITH APPLICATION TO CANCER GENOMICS.
A STATISTICAL FRAMEWORK FOR DATA INTEGRATION THROUGH GRAPHICAL MODELS WITH APPLICATION TO CANCER GENOMICS.
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DOI：
10.1214/16-aoas998
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发表时间：
2017-03
期刊：
影响因子：
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通讯作者：
Yuping Zhang;Z. Ouyang;Hongyu Zhao
中科院分区：
文献类型：
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作者：
Yuping Zhang;Z. Ouyang;Hongyu Zhao
高通量生物技术的最新进展已经产生了不同生物条件下的各种类型的遗传、基因组、表观遗传、转录组和蛋白质组数据。整合来自不同实验的数据可能会导致对生物系统和复杂疾病的更统一和全球化的看法。我们提出了一个连贯的统计框架，整合不同类型的数据，但相关的生物条件，通过图形模型。具体来说，我们的统计框架是专为从异构的高维数据集建模多个网络与共享的监管机制。我们的方法的性能说明通过模拟和其应用到癌症基因组学。
Recent advances in high-throughput biotechnologies have generated var-ious types of genetic, genomic, epigenetic, transcriptomic and proteomic data across different biological conditions. It is likely that integrating data from diverse experiments may lead to a more unified and global view of biolog-ical systems and complex diseases. We present a coherent statistical frame-work for integrating various types of data from distinct but related biological conditions through graphical models. Specifically, our statistical framework is designed for modeling multiple networks with shared regulatory mech-anisms from heterogeneous high-dimensional datasets. The performance of our approach is illustrated through simulations and its applications to cancer genomics.