用ICGS揭示发育中的人类前额叶皮层中可解释的发育特异性基因特征
Unveiling interpretable development-specific gene signatures in the developing human prefrontal cortex with ICGS
摘要：
在本文中，揭示可解释的发育特异性基因签名在人类PFC中，我们提出了一种新的基因选择方法，命名为可解释的因果关系基因选择（ICGS），它采用贝叶斯网络（BN）来表示多个基因变量和发育变量之间的因果关系。所提出的ICGS方法将基于正实例的对比学习与变分自动编码器（VAE）相结合以获得该最优BN结构，并使用马尔可夫毯（MB）来识别与发育变量因果相关的基因签名。此外，在基因选择之前，差异表达基因（DEG）被用来过滤冗余基因。为了识别基因签名，我们将所提出的ICGS应用于人类PFC单细胞转录组学数据。实验结果表明，该方法能有效识别人类PFC中发育特异性基因，基因本体富集分析和ASD相关基因分析表明，这些识别出的基因揭示了人类PFC中的关键生物学过程和通路，对神经发育障碍的治疗具有更大的潜力。这些基因特征有望为理解PFC在人类中的发育异质性和功能带来重要影响。
英文摘要：
In this paper, to unveil interpretable development-specific gene signatures in human PFC, we propose a novel gene selection method, named Interpretable Causality Gene Selection (ICGS), which adopts a Bayesian Network (BN) to represent causality between multiple gene variables and a development variable. The proposed ICGS method combines the positive instances-based contrastive learning with a Variational AutoEncoder (VAE) to obtain this optimal BN structure and use a Markov Blanket (MB) to identify gene signatures causally related to the development variable. Moreover, the differential expression genes (DEGs) are used to filter redundant genes before gene selection. In order to identify gene signatures, we apply the proposed ICGS to the human PFC single-cell transcriptomics data. The experimental results demonstrate that the proposed method can effectively identify interpretable development-specific gene signatures in human PFC. Gene ontology enrichment analysis and ASD-related gene analysis show that these identified gene signatures reveal the key biological processes and pathways in human PFC and have more potential for neurodevelopment disorder cure. These gene signatures are expected to bring important implications for understanding PFC development heterogeneity and function in humans.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
