ScAtt: an Attention based architecture to analyze Alzheimer's disease at cell type level from single-cell RNA-sequencing data

Xiaoxia Liu, Robert R Butler III, Prashnna K Gyawali, Frank M Longo, Zihuai He
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Abstract

Alzheimer's disease (AD) is a pervasive neurodegenerative disorder that leads to memory and behavior impairment severe enough to interfere with daily life activities. Understanding this disease pathogenesis can drive the development of new targets and strategies to prevent and treat AD. Recent advances in high-throughput single-cell RNA sequencing technology (scRNA-seq) have enabled the generation of massive amounts of transcriptomic data at the single-cell level provided remarkable insights into understanding the molecular pathogenesis of Alzheimer's disease. In this study, we introduce ScAtt, an innovative Attention-based architecture, devised specifically for the concurrent identification of cell-type specific AD-related genes and their associated gene regulatory network. ScAtt incorporates a flexible model capable of capturing nonlinear effects, leading to the detection of AD-associated genes that might be overlooked by traditional differentially expressed gene (DEG) analyses. Moreover, ScAtt effectively infers a gene regulatory network depicting the combined influences of genes on the targeted disease, as opposed to examining correlations among genes in conventional gene co-expression networks. In an application to 95,186 single-nucleus transcriptomes from 17 hippocampus samples, ScAtt shows substantially better performance in modeling quantitative changes in expression levels between AD and healthy controls. Consequently, ScAtt performs better than existing methods in the identification of AD-related genes, with more unique discoveries and less overlap between cell types. Functional enrichments of the corresponding gene modules detected from gene regulatory network show significant enrichment of biologically meaningful AD-related pathways across different cell types.
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ScAtt:基于注意力的架构,从单细胞 RNA 序列数据分析阿尔茨海默病的细胞类型水平
阿尔茨海默病(AD)是一种普遍的神经退行性疾病,会导致严重的记忆和行为障碍,以至于影响日常生活。了解这种疾病的发病机制可以推动开发预防和治疗阿尔茨海默病的新靶点和策略。近年来,高通量单细胞 RNA 测序技术(scRNA-seq)的进步使得在单细胞水平上生成大量转录组数据成为可能,这为了解阿尔茨海默病的分子发病机制提供了重要启示。在这项研究中,我们介绍了基于注意力的创新架构 ScAtt,它是专为同时鉴定细胞类型特异性 AD 相关基因及其相关基因调控网络而设计的。ScAtt 采用了一个灵活的模型,能够捕捉非线性效应,从而检测出传统的差异表达基因(DEG)分析可能会忽略的 AD 相关基因。此外,ScAtt 还能有效地推断基因调控网络,描述基因对目标疾病的综合影响,而不是研究传统基因共表达网络中基因之间的相关性。在对来自17个海马体样本的95,186个单核转录组的应用中,ScAtt在模拟AD和健康对照组之间表达水平的定量变化方面表现出了更好的性能。从基因调控网络中检测到的相应基因模块的功能富集显示,不同细胞类型中具有生物学意义的AD相关通路显著富集。
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