先恢复后聚合:利用全局结构信息对单细胞数据进行统一的跨模态深度聚类。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Ziyi Wang, Peng Luo, Mingming Xiao, Boyang Wang, Tianyu Liu, Xiangyu Sun
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引用次数: 0

摘要

单细胞跨模态联合聚类已被广泛用于研究肿瘤微环境。尽管提出了许多方法,但准确聚类仍是主要挑战。首先,由于测量的局限性,基因表达矩阵经常包含大量缺失值。现有的大多数聚类方法都将其作为典型的多模态数据集处理,而不做进一步处理。很少有方法会在聚类前进行恢复,也没有充分参与基础研究,从而导致了次优结果。此外,现有的跨模态信息融合策略无法确保不同模态表征的一致性,可能导致冲突信息的融合,从而降低性能。为了应对这些挑战,我们提出了 "先恢复后聚合 "策略,并引入了统一跨模态深度聚类模型。具体来说,我们开发了一种基于邻域相似性的数据增强技术,对拉普拉斯矩阵迭代施加秩约束,从而更新相似性矩阵并恢复掉队事件。与此同时,我们还整合了跨模态特征,并采用对比学习将特定模态表征与一致的表征相统一,从而增强了对不同模态信息的有效整合。在五个真实世界多模态数据集上进行的综合实验证明了这种方法在单细胞聚类任务中的卓越功效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recover then aggregate: unified cross-modal deep clustering with global structural information for single-cell data.

Single-cell cross-modal joint clustering has been extensively utilized to investigate the tumor microenvironment. Although numerous approaches have been suggested, accurate clustering remains the main challenge. First, the gene expression matrix frequently contains numerous missing values due to measurement limitations. The majority of existing clustering methods treat it as a typical multi-modal dataset without further processing. Few methods conduct recovery before clustering and do not sufficiently engage with the underlying research, leading to suboptimal outcomes. Additionally, the existing cross-modal information fusion strategy does not ensure consistency of representations across different modes, potentially leading to the integration of conflicting information, which could degrade performance. To address these challenges, we propose the 'Recover then Aggregate' strategy and introduce the Unified Cross-Modal Deep Clustering model. Specifically, we have developed a data augmentation technique based on neighborhood similarity, iteratively imposing rank constraints on the Laplacian matrix, thus updating the similarity matrix and recovering dropout events. Concurrently, we integrate cross-modal features and employ contrastive learning to align modality-specific representations with consistent ones, enhancing the effective integration of diverse modal information. Comprehensive experiments on five real-world multi-modal datasets have demonstrated this method's superior effectiveness in single-cell clustering tasks.

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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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