BAYESIAN SPARSE GRAPHICAL MODELS FOR CLASSIFICATION WITH APPLICATION TO PROTEIN EXPRESSION DATA.
BAYESIAN SPARSE GRAPHICAL MODELS FOR CLASSIFICATION WITH APPLICATION TO PROTEIN EXPRESSION DATA.
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DOI：
10.1214/14-aoas722
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Mallick BK
中科院分区：
文献类型：
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作者：
Baladandayuthapani V;Talluri R;Ji Y;Coombes KR;Lu Y;Hennessy BT;Davies MA;Mallick BK
反相蛋白质阵列（RPPA）分析是一个强大的，相对较新的平台，允许高通量，定量分析蛋白质网络。目前限制该技术潜力的挑战之一是缺乏允许准确数据建模和识别相关网络和样本的方法。这些模型可以提高基于蛋白质网络激活模式的生物样本分类的准确性，并提供对不同类型癌症背后的独特生物学关系的见解。受RPPA数据的启发，我们提出了一种贝叶斯稀疏图形建模方法，该方法在存在类信息的条件关系上使用选择先验。我们的贝叶斯模型的新颖之处在于能够从网络数据中提取信息，以及在统一的分层模型中从相关的分类结果中提取信息进行分类。此外，我们的方法允许直接在模型中直观地集成先验网络信息，并允许在类内和类之间对网络拓扑进行后验推理。将我们的方法应用于从人类乳腺癌和卵巢癌细胞系面板生成的RPPA数据集，我们证明该模型能够比几种现有模型更准确地区分不同的癌细胞类型，并识别这两种类型癌症之间关键信号网络（PI3K-AKT通路）组分的差异调节。这种方法代表了一种强大的新工具，可以用来提高我们对癌症蛋白质网络的理解。
Reverse-phase protein array (RPPA) analysis is a powerful, relatively new platform that allows for high-throughput, quantitative analysis of protein networks. One of the challenges that currently limit the potential of this technology is the lack of methods that allow for accurate data modeling and identification of related networks and samples. Such models may improve the accuracy of biological sample classification based on patterns of protein network activation and provide insight into the distinct biological relationships underlying different types of cancer. Motivated by RPPA data, we propose a Bayesian sparse graphical modeling approach that uses selection priors on the conditional relationships in the presence of class information. The novelty of our Bayesian model lies in the ability to draw information from the network data as well as from the associated categorical outcome in a unified hierarchical model for classification. In addition, our method allows for intuitive integration of a priori network information directly in the model and allows for posterior inference on the network topologies both within and between classes. Applying our methodology to an RPPA data set generated from panels of human breast cancer and ovarian cancer cell lines, we demonstrate that the model is able to distinguish the different cancer cell types more accurately than several existing models and to identify differential regulation of components of a critical signaling network (the PI3K-AKT pathway) between these two types of cancer. This approach represents a powerful new tool that can be used to improve our understanding of protein networks in cancer.