scEGG:单细胞转录组数据的外源基因引导聚类方法。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-09-23 DOI:10.1093/bib/bbae483
Dayu Hu, Renxiang Guan, Ke Liang, Hao Yu, Hao Quan, Yawei Zhao, Xinwang Liu, Kunlun He
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引用次数: 0

摘要

近年来,单细胞数据分析领域取得了重大进展,尤其是在聚类方法的开发方面。尽管取得了这些进步,但大多数算法仍然主要侧重于分析所提供的单细胞矩阵数据。然而,在医学领域,单细胞数据往往包含大量外源信息,如基因网络。忽视这一点可能会导致信息丢失,并产生缺乏重要临床意义的聚类结果。为了解决这一局限性,我们为单细胞数据引入了一种创新的深度聚类方法,该方法利用外源基因信息生成具有区分性的细胞表征。具体来说,我们开发了一种注意力增强图自动编码器,以有效捕捉细胞间的拓扑信号模式。与此同时,在外源蛋白质-蛋白质相互作用网络上进行随机游走可获取基因嵌入。最终,聚类过程需要整合和重建基因-细胞合作嵌入,从而产生一种判别表征。广泛的实验证明了所提方法的有效性。这项研究有助于深入了解细胞的特征,从而为疾病的早期诊断和治疗奠定基础。数据集和代码可在 https://github.com/DayuHuu/scEGG 的资源库中公开访问。
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scEGG: an exogenous gene-guided clustering method for single-cell transcriptomic data.

In recent years, there has been significant advancement in the field of single-cell data analysis, particularly in the development of clustering methods. Despite these advancements, most algorithms continue to focus primarily on analyzing the provided single-cell matrix data. However, within medical contexts, single-cell data often encompasses a wealth of exogenous information, such as gene networks. Overlooking this aspect could result in information loss and produce clustering outcomes lacking significant clinical relevance. To address this limitation, we introduce an innovative deep clustering method for single-cell data that leverages exogenous gene information to generate discriminative cell representations. Specifically, an attention-enhanced graph autoencoder has been developed to efficiently capture topological signal patterns among cells. Concurrently, a random walk on an exogenous protein-protein interaction network enabled the acquisition of the gene's embeddings. Ultimately, the clustering process entailed integrating and reconstructing gene-cell cooperative embeddings, which yielded a discriminative representation. Extensive experiments have demonstrated the effectiveness of the proposed method. This research provides enhanced insights into the characteristics of cells, thus laying the foundation for the early diagnosis and treatment of diseases. The datasets and code can be publicly accessed in the repository at https://github.com/DayuHuu/scEGG.

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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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