Estimating equation-based causality analysis with application to microarray time series data.
Estimating equation-based causality analysis with application to microarray time series data.
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DOI：
10.1093/biostatistics/kxp005
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发表时间：
2009-07
期刊：
影响因子：
2.1
通讯作者：
Jianhua Hu;F. Hu
中科院分区：
文献类型：
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作者：
Jianhua Hu;F. Hu
基因芯片的时程数据可以用来研究基因间的相互作用和推断基因网络。构建基因网络的关键步骤是开发合适的因果关系检验。在这方面，每个基因的表达谱可以被视为时间序列。一个典型的现有方法建立格兰杰因果关系的基础上Wald类型的测试，这依赖于数据分布的同方差正态假设。然而，在真实的微阵列实验中，这一假设可能被严重违反，从而可能导致不一致的测试结果和错误的科学结论。为了克服这个缺点，我们提出了一种基于估计方程的方法，该方法对基因表达数据的异方差性和非正态性都具有鲁棒性。实际上，它只要求残差不相关。我们将使用模拟研究和真实数据的例子来证明所提出的方法的适用性。
Microarray time-course data can be used to explore interactions among genes and infer gene network. The crucial step in constructing gene network is to develop an appropriate causality test. In this regard, the expression profile of each gene can be treated as a time series. A typical existing method establishes the Granger causality based on Wald type of test, which relies on the homoscedastic normality assumption of the data distribution. However, this assumption can be seriously violated in real microarray experiments and thus may lead to inconsistent test results and false scientific conclusions. To overcome the drawback, we propose an estimating equation-based method which is robust to both heteroscedasticity and nonnormality of the gene expression data. In fact, it only requires the residuals to be uncorrelated. We will use simulation studies and a real-data example to demonstrate the applicability of the proposed method.