In the analysis of gene expression data, when there are two or more disease conditions/groups (e.g., diseased and normal, responder and nonresponder, and multiple stages/subtypes), differential analysis has been extensively conducted to identify key differences and has important implications. Network analysis takes a system perspective and can be more informative than that limited to simple statistics such as mean and variance. In differential network analysis, a common practice is to first estimate a gene expression network for each condition/group, and then spectral clustering can be applied to the network difference(s) to identify key genes and biological mechanisms that lead to the differences. Compared to “simple” analysis such as regression, differential network analysis can be more challenging with the significantly larger number of parameters. In this study, taking advantage of the increasing popularity of multidimensional profiling data, we develop an assisted analysis strategy and propose incorporating regulator information to improve the identification of key genes (that lead to the differences in gene expression networks). An effective computational algorithm is developed. Comprehensive simulation is conducted, showing that the proposed approach can outperform the benchmark alternatives in identification accuracy. With the The Cancer Genome Atlas lung adenocarcinoma data, we analyze the expressions of genes in the KEGG cell cycle pathway, assisted by copy number variation data. The proposed assisted analysis leads to identification results similar to the alternatives but different estimations. Overall, this study can deliver an efficient and cost-effective way of improving differential network analysis.

中文翻译：

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基因表达数据的辅助差分网络分析


在基因表达数据分析中，当存在两个或多个疾病状况/组（例如患病和正常、有反应者和无反应者以及多个阶段/亚​​型）时，已广泛进行差异分析以识别关键差异并具有重要意义。网络分析采用系统视角，比仅限于平均值和方差等简单统计数据提供更多信息。在差异网络分析中，常见的做法是首先估计每个条件/组的基因表达网络，然后可以将谱聚类应用于网络差异，以识别导致差异的关键基因和生物机制。与回归等“简单”分析相比，差分网络分析因参数数量明显增多而更具挑战性。在本研究中，利用多维分析数据的日益普及，我们开发了一种辅助分析策略，并建议纳入调控信息以改进关键基因（导致基因表达网络差异）的识别。开发了一种有效的计算算法。进行了全面的仿真，表明所提出的方法在识别精度方面优于基准替代方案。利用癌症基因组图谱肺腺癌数据，我们在拷贝数变异数据的辅助下分析了 KEGG 细胞周期通路中基因的表达。所提出的辅助分析得出的识别结果与替代方案相似，但估计不同。总体而言，这项研究可以提供一种有效且经济高效的方法来改进差分网络分析。