基于贝叶斯非参数协变量依赖定向循环图模型的空间变化基因调控网络
Spatially Varying Gene Regulatory Networks via Bayesian Nonparametric Covariate-Dependent Directed Cyclic Graphical Models
AI总结：
该研究提出了一种基于贝叶斯非参数协变量依赖定向循环图模型的方法，用于学习空间变化的基因调控网络，通过划分平均获得平滑变化的图结构，并在空间转录组学数据中揭示了具有反馈回路的调控网络。
摘要：
空间转录组学技术能够通过空间上下文测量基因表达，为理解基因调控网络在组织区域间的差异提供了机会。然而，现有的图模型主要集中在无向图或有向无环图上，限制了它们捕捉基因调控中普遍存在的反馈回路的能力。此外，确保循环图所谓的稳定性条件，同时允许图结构随着空间协变量连续变化，带来了显著的统计和计算挑战。我们提出了BNP-DCGx，一种通过协变量依赖的定向循环图模型学习空间变化基因调控网络的贝叶斯非参数方法。我们的方法引入了一个协变量依赖的随机划分作为分层模型中的中介层，将协变量空间划分为簇，每个簇具有特定的稳定有向循环图。通过划分平均，我们能够在空间上获得平滑变化的图结构，同时保持理论上的稳定性保证。我们开发了一种高效的并行退火马尔可夫链蒙特卡洛算法进行后验推断，并通过模拟证明我们的方法能够准确恢复分段常数和连续变化的图结构。应用于人类背外侧前额叶皮质的空间转录组学数据，揭示了具有反馈回路的空间变化调控网络，识别了基于不同调控机制的潜在细胞亚型，并提供了关于大脑组织中基因调控空间组织的新见解。
英文摘要：
Spatial transcriptomics technologies enable the measurement of gene expression with spatial context, providing opportunities to understand how gene regulatory networks vary across tissue regions. However, existing graphical models focus primarily on undirected graphs or directed acyclic graphs, limiting their ability to capture feedback loops that are prevalent in gene regulation. Moreover, ensuring the so-called stability condition of cyclic graphs, while allowing graph structures to vary continuously with spatial covariates, presents significant statistical and computational challenges. We propose BNP-DCGx, a Bayesian nonparametric approach for learning spatially varying gene regulatory networks via covariate-dependent directed cyclic graphical models. Our method introduces a covariate-dependent random partition as an intermediary layer in a hierarchical model, which discretizes the covariate space into clusters with cluster-specific stable directed cyclic graphs. Through partition averaging, we obtain smoothly varying graph structures over space while maintaining theoretical guarantees of stability. We develop an efficient parallel tempered Markov chain Monte Carlo algorithm for posterior inference and demonstrate through simulations that our method accurately recovers both piecewise constant and continuously varying graph structures. Application to spatial transcriptomics data from human dorsolateral prefrontal cortex reveals spatially varying regulatory networks with feedback loops, identifies potential cell subtypes within established cell types based on distinct regulatory mechanisms, and provides new insights into spatial organization of gene regulation in brain tissue.
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