Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Rd, Manchester, M13 9PL, UK.
Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Rd, Manchester, M13 9PL, UK.
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbae615.
Single-cell multiomics have opened up tremendous opportunities for understanding gene regulatory networks underlying cell states by simultaneously profiling transcriptomes, epigenomes, and proteomes of the same cell. However, existing computational methods for integrative analysis of these high-dimensional multiomics data are either computationally expensive or limited in interpretation. These limitations pose challenges in the implementation of these methods in large-scale studies and hinder a more in-depth understanding of the underlying regulatory mechanisms. Here, we propose TriTan (Triple inTegrative fast non-negative matrix factorization), an efficient joint factorization method for single-cell multiomics data. TriTan implements a highly efficient factorization algorithm, greatly improving its computational performance. Three matrix factorization produced by TriTan helps in clustering cells, identifying signature features for each cell type, and uncovering feature associations across omics, which facilitates the identification of domains of regulatory chromatin and the prediction of cell-type-specific regulatory networks. We applied TriTan to the single-cell multiomics data obtained from different technologies and benchmarked it against the state-of-the-art methods where it shows highly competitive performance. Furthermore, we showed a range of downstream analyses conducted utilizing TriTan outputs, highlighting its capacity to facilitate interpretation in biological discovery.
单细胞多组学通过同时分析同一细胞的转录组、表观基因组和蛋白质组，为理解细胞状态下的基因调控网络提供了巨大的机会。然而，现有的用于整合分析这些高维多组学数据的计算方法要么计算成本高，要么在解释方面受到限制。这些限制在大规模研究中实施这些方法时带来了挑战，并阻碍了对潜在调控机制的更深入理解。在这里，我们提出了 TriTan（三重综合快速非负矩阵分解），这是一种用于单细胞多组学数据的高效联合分解方法。TriTan 实现了一种高效的分解算法，大大提高了其计算性能。TriTan 产生的三个矩阵分解有助于对细胞进行聚类，识别每种细胞类型的特征，并揭示组学之间的特征关联，这有助于识别调控染色质的域和预测细胞类型特异性的调控网络。我们将 TriTan 应用于从不同技术获得的单细胞多组学数据，并将其与最先进的方法进行了基准测试，结果表明它具有很高的竞争力。此外，我们展示了利用 TriTan 输出进行的一系列下游分析，突出了其在生物学发现中促进解释的能力。
