论文标题
在发展中的人类前额叶皮层中揭示了可解释的开发特异性基因特异性基因签名
Unveiling interpretable development-specific gene signatures in the developing human prefrontal cortex with ICGS
论文作者
论文摘要
在本文中，为了在人类PFC中揭示可解释的发展特异性基因的特异性基因特异性基因特异性基因，我们提出了一种新型的基因选择方法，称为可解释的因果关系基因选择（ICGS），该方法采用了贝叶斯网络（BN）代表多个基因变量和发展变量之间的因果关系。所提出的ICGS方法将基于积极的对比度学习与各种自动编码器（VAE）结合在一起，以获得这种最佳的BN结构，并使用Markov Blanket（MB）来识别与开发变量有因果关系的基因签名。此外，差异表达基因（DEG）用于在选择基因之前过滤冗余基因。为了识别基因特征，我们将提出的ICG应用于人类PFC单细胞转录组学数据。实验结果表明，所提出的方法可以有效地识别人类PFC中可解释的特异性基因特异性基因。基因本体富集分析和与ASD相关的基因分析表明，这些已鉴定的基因特征揭示了人类PFC中的关键生物学过程和途径，并且具有更大的神经发育障碍治疗潜力。这些基因的特征有望为理解人类的PFC发展异质性和功能带来重要意义。
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.
