scDFN:利用深度融合网络增强单细胞 RNA-seq 聚类。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-09-23 DOI:10.1093/bib/bbae486
Tianxiang Liu, Cangzhi Jia, Yue Bi, Xudong Guo, Quan Zou, Fuyi Li
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引用次数: 0

摘要

单细胞核糖核酸测序(scRNA-seq)技术可用于对单个细胞的转录组进行高分辨率分析。因此,该技术在准确分析日益增多的异质单细胞数据集方面得到了广泛应用。解释 scRNA-seq 数据的核心是对细胞进行聚类,以解读转录组多样性并推断细胞行为模式。然而,由于其复杂性,有必要应用先进的方法来解决单细胞数据固有的异质性和有限的基因表达特性。在本文中,我们介绍了一种基于深度学习的新型单细胞聚类算法--scDFN,它能通过融合网络策略显著增强scRNA-seq数据的聚类能力。scDFN 算法采用双重机制,包括提取属性信息的自动编码器和捕捉拓扑细微差别的改进图自动编码器,并通过跨网络信息融合机制与三重自监督策略相辅相成。这种融合通过对四种不同损失函数的综合考虑进行优化。在多个数据集上与五种领先的 scRNA-seq 聚类方法进行的比较分析表明,scDFN 的归一化互信息(NMI)和调整后兰德指数(ARI)指标更胜一筹。此外,scDFN 还表现出强大的多集群数据集性能和对批处理效应的超强适应能力。消融研究强调了自动编码器和改进的图自动编码器组件的关键作用,以及四个联合损失函数对算法整体功效的重要贡献。通过这些改进,scDFN 为单细胞聚类树立了新的标杆,并可作为一种有效工具用于单细胞转录组学的细微分析。
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scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks.

Single-cell ribonucleic acid sequencing (scRNA-seq) technology can be used to perform high-resolution analysis of the transcriptomes of individual cells. Therefore, its application has gained popularity for accurately analyzing the ever-increasing content of heterogeneous single-cell datasets. Central to interpreting scRNA-seq data is the clustering of cells to decipher transcriptomic diversity and infer cell behavior patterns. However, its complexity necessitates the application of advanced methodologies capable of resolving the inherent heterogeneity and limited gene expression characteristics of single-cell data. Herein, we introduce a novel deep learning-based algorithm for single-cell clustering, designated scDFN, which can significantly enhance the clustering of scRNA-seq data through a fusion network strategy. The scDFN algorithm applies a dual mechanism involving an autoencoder to extract attribute information and an improved graph autoencoder to capture topological nuances, integrated via a cross-network information fusion mechanism complemented by a triple self-supervision strategy. This fusion is optimized through a holistic consideration of four distinct loss functions. A comparative analysis with five leading scRNA-seq clustering methodologies across multiple datasets revealed the superiority of scDFN, as determined by better the Normalized Mutual Information (NMI) and the Adjusted Rand Index (ARI) metrics. Additionally, scDFN demonstrated robust multi-cluster dataset performance and exceptional resilience to batch effects. Ablation studies highlighted the key roles of the autoencoder and the improved graph autoencoder components, along with the critical contribution of the four joint loss functions to the overall efficacy of the algorithm. Through these advancements, scDFN set a new benchmark in single-cell clustering and can be used as an effective tool for the nuanced analysis of single-cell transcriptomics.

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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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