Summary of the comparison methods.

The development of single-cell multi-omics sequencing technologies has enabled the simultaneous analysis of multi-omics data within the same cell. Accurate clustering of these cells is crucial for downstream analyses of complex biological functions. Despite significant advances in multi-omics integration approaches, current methodologies exhibit two major limitations. First, they inadequately incorporate prior biological knowledge from various omic layers. Second, these methods often conduct independent dimensionality reduction on individual omic datasets, thereby failing to capture the intrinsic complementary information and potentially overlooking crucial cross-platform interactions. Motivated by these, this study investigates a non-negative matrix factorization model called PLNMFG, which integrates the unified latent representation learning that retains the features between and within omics and the cluster structure learning that retains the intrinsic structure of the data into one joint framework. Specially, PLNMFG performs adaptive imputation to handle dropout events and uses prior pseudo-labels as constraints during the process of collective non-negative matrix factorization, as a result, a more robust latent representation that preserves the double similarity information is obtained. Graph Laplacian constraint is applied during clustering which further preserves structure characteristic of multi-omics data. In addition, the weight of each omic is adaptively learned based on the omic contribution. A series of experiments on 8 benchmark datasets show that our model performs well in terms of clustering accuracy and computational efficiency.

单细胞多组学测序技术（single-cell multi-omics sequencing technologies）的发展，使研究者得以在单个细胞内同步分析多组学数据。对这些细胞进行精准聚类，对于复杂生物学功能的下游分析至关重要。尽管多组学整合方法已取得显著进展，但当前的方法论仍存在两大主要局限：其一，未能充分融入来自不同组学层面的先验生物学知识；其二，此类方法通常会对单个组学数据集执行独立的降维操作，因而无法捕捉到内在的互补信息，还有可能错失关键的跨组学检测平台交互作用。基于上述局限，本研究提出了一种名为PLNMFG的非负矩阵分解模型（non-negative matrix factorization model），该模型将保留组学间与组学内特征的统一隐式表征学习，以及保留数据内在结构的聚类结构学习整合至同一联合框架中。具体而言，PLNMFG会执行自适应插补以处理脱落事件（dropout events），并在集体非负矩阵分解过程中以预先生成的伪标签作为约束条件，由此得到可保留双重相似性信息的更鲁棒隐式表征。聚类过程中还应用了图拉普拉斯约束（Graph Laplacian constraint），可进一步保留多组学数据的结构特性。此外，模型会根据各组学的贡献度自适应学习每个组学的权重。在8个基准数据集上开展的一系列实验结果表明，所提模型在聚类精度与计算效率方面均表现优异。