scRDAN: a robust domain adaptation network for cell type annotation across single-cell RNA sequencing data.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Yan Sun, Yan Zhao, Junliang Shang, Baojuan Qin, Xiaohan Zhang, Jin-Xing Liu
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引用次数: 0

Abstract

Single-cell RNA sequencing technology facilitates the recognition of diverse cell types and subgroups, playing a crucial role in investigating cellular heterogeneity. Cell type annotation, a crucial process in single-cell RNA sequencing analysis, is often influenced by noise and batch effects. To address these challenges, we propose scRDAN, which is a robust domain adaptation network comprising three modules: the denoising domain adaptation module, the fine-grained discrimination module, and the robustness enhancement module. The denoising domain adaptation module mitigates noise interference through feature reconstruction in domains, while leveraging adversarial learning to align data distributions, improving annotation accuracy and robustness against batch effects. The fine-grained discrimination module maintains intra-class compactness and enhances inter-class separability, reducing feature overlap and improving cell type distinction. Finally, the robustness enhancement module introduces noise from various perspectives in both domains, enhancing robustness and generalization. We evaluate scRDAN on simulated, cross-platforms, and cross-species datasets, comparing it with advanced methods. Results demonstrate that scRDAN outperforms existing methods in handling batch effects and cell type annotation.

scRDAN:一个强大的区域适应网络,用于跨单细胞RNA测序数据的细胞类型注释。
单细胞RNA测序技术有助于识别不同的细胞类型和亚群,在研究细胞异质性方面起着至关重要的作用。细胞类型标注是单细胞RNA测序分析中的一个关键环节,经常受到噪声和批效应的影响。为了解决这些问题,我们提出了一种鲁棒域自适应网络scRDAN,它包括三个模块:去噪域自适应模块、细粒度识别模块和鲁棒性增强模块。降噪域自适应模块通过域内的特征重构来减轻噪声干扰,同时利用对抗性学习来对齐数据分布,提高标注准确性和对批处理效应的鲁棒性。细粒度识别模块保持类内紧密性,增强类间可分离性,减少特征重叠,提高细胞类型区分。最后,鲁棒性增强模块从两个领域的不同角度引入噪声,增强鲁棒性和泛化。我们在模拟、跨平台和跨物种数据集上评估了scRDAN,并将其与先进的方法进行了比较。结果表明,scRDAN在处理批处理效果和细胞类型标注方面优于现有方法。
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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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