scGEMOC, A Graph Embedded Contrastive Learning Single-cell Multiomics Clustering Model
scGEMOC, A Graph Embedded Contrastive Learning Single-cell Multiomics Clustering Model
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DOI：
10.1109/bibm58861.2023.10385267
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发表时间：
2023-12
期刊：
影响因子：
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通讯作者：
Bingjun Li;S. Nabavi
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文献类型：
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作者：
Bingjun Li;S. Nabavi
单细胞多组学测序的最新进展创造了新的研究机会，但也提出了挑战，特别是在细胞聚集方面。一个主要的挑战是特征融合。早期融合模型是稳健的，但忽略了组学的独特分布，不能处理不同的组学维度。大多数当前的聚类方法使用晚期融合，为每个OMIC使用独立的编码器。然而，提取的基因组特征属于不同的潜在空间，这给组学比对带来了困难。此外，现有的细胞聚类方法没有考虑生物学先验知识，如组学内部和组间的相互作用，而这些知识在定义细胞类型方面起着关键作用。ScGEMOC利用先前的生物学知识将组学间和组内的联系表示为异质图。它应用图嵌入技术将组学交互数据聚合为一个伪组，并使用对比学习在潜在空间中有效地对齐组学。我们在三个公共数据集上对照五个最先进的基线模型对scGEMOC进行了评估。与所有数据集的基准模型相比，scGEMOC实现了卓越的集群性能。一项消融研究证实了每个组件的重要贡献，并确定了最具影响力的组件。
Recent advancements in single-cell multiomics sequencing create new research opportunities but also pose challenges, particularly in cell clustering. One major challenge is feature fusion. Early fusion models are robust but ignore the unique distributions of omics and cannot handle various omic dimensions. Most current clustering methods use late fusion, employing independent encoders for each omic. However, the extracted omic features belong to different latent spaces, leading to difficulties in aligning omics. Additionally, current cell clustering methods do not incorporate prior biological knowledge, such as interactions within and across omics, which has been shown plays a key role in defining cell types.To address these shortcomings, we propose a novel, scalable, end-to-end clustering method, called single-cell graph embedding multiomics cluster (scGEMOC). scGEMOC utilizes prior biological knowledge to represent inter- and intra-omics connections as a heterogeneous graph. It applies graph embedding to aggregate omics interaction data as a pseudo omic and employs contrastive learning for effectively aligning omics in the latent space. We evaluated scGEMOC on three public datasets against five state-of-the-art baseline models. scGEMOC achieves superior clustering performance compared to the baseline models on all datasets. An ablation study confirms the significant contribution of each component and identifies the most impactful one.