Identifying gene network rewiring under different biological conditions is important for understanding the mechanisms underlying complex diseases. Gaussian graphical models, which assume the data follow the multivariate normal distribution, are widely used to identify gene network rewiring. However, the normality assume often fails in reality since the data are contaminated by extreme outliers in general. In this study, we propose a new robust differential graphical model to identify gene network rewiring between two conditions based on the multivariate t-distribution. The multivariate t-distribution is more robust to outliers than the normal distribution since it has heavy tails and allows values far from the mean. A fused lasso penalty is used to borrow information across conditions to improve the results. We develop an expectation maximization algorithm to solve the optimization model. Experiment results on simulated data show that our method outperforms the state-of-the-art methods. Our method is also applied to identify gene network rewiring between luminal A and basal-like subtypes of breast cancer. Several key genes which drive gene network rewiring are discovered.

中文翻译：

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使用具有多元t分布的稳健差分图形模型识别基因网络重新布线。

识别不同生物学条件下的基因网络重排对于理解复杂疾病的机制很重要。假设数据遵循多元正态分布的高斯图形模型被广泛用于识别基因网络的重新布线。但是，由于数据通常受到极端异常值的污染，因此正常假设在现实中经常会失败。在这项研究中，我们提出了一个新的鲁棒的差分图形模型，以基于多元t分布来识别两个条件之间的基因网络重排。多元t分布比正态分布对异常值的鲁棒性强，因为它的尾部很重并且允许值远离均值。套索套索罚分用于在各种条件下借阅信息以改善结果。我们开发了期望最大化算法来求解优化模型。模拟数据的实验结果表明，我们的方法优于最新方法。我们的方法还用于识别管腔A与乳腺癌的基底样亚型之间的基因网络重排。发现了几个驱动基因网络重新布线的关键基因。