BOOTSTRAP INFERENCE FOR NETWORK CONSTRUCTION WITH AN APPLICATION TO A BREAST CANCER MICROARRAY STUDY.
BOOTSTRAP INFERENCE FOR NETWORK CONSTRUCTION WITH AN APPLICATION TO A BREAST CANCER MICROARRAY STUDY.
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DOI：
10.1214/12-aoas589
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发表时间：
2013-03-01
期刊：
影响因子：
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通讯作者：
Wang P
中科院分区：
文献类型：
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作者：
Li S;Hsu L;Peng J;Wang P
高斯图模型（Gaussian Graphical Models，GGM）被用于构建基因调控网络，其中正则化技术被广泛应用，因为网络推理通常福尔斯高维低样本的情况。然而，找到合适的正则化量可能具有挑战性，特别是在无监督的环境中，BIC或交叉验证等传统方法通常不能很好地工作。在本文中，我们提出了一种新的方法-引导推理网络构造（BINCO）-推断网络直接控制的错误发现率（FDR）的选择边缘。该方法拟合边选择频率分布的混合模型来估计FDR，其中选择频率通过模型聚合来计算。该方法适用于网络建设以外的广泛应用。当我们将我们提出的方法应用于构建具有微阵列表达乳腺癌数据的基因调控网络时，我们能够识别高置信度边缘和连接良好的枢纽基因，这些基因可能在理解乳腺癌的潜在生物学过程中发挥重要作用。
Gaussian Graphical Models (GGMs) have been used to construct genetic regulatory networks where regularization techniques are widely used since the network inference usually falls into a high–dimension–low–sample–size scenario. Yet, finding the right amount of regularization can be challenging, especially in an unsupervised setting where traditional methods such as BIC or cross-validation often do not work well. In this paper, we propose a new method — Bootstrap Inference for Network COnstruction (BINCO) — to infer networks by directly controlling the false discovery rates (FDRs) of the selected edges. This method fits a mixture model for the distribution of edge selection frequencies to estimate the FDRs, where the selection frequencies are calculated via model aggregation. This method is applicable to a wide range of applications beyond network construction. When we applied our proposed method to building a gene regulatory network with microarray expression breast cancer data, we were able to identify high-confidence edges and well-connected hub genes that could potentially play important roles in understanding the underlying biological processes of breast cancer.