Advances in single-cell transcriptomics provide an unprecedented opportunity to explore complex biological processes. However, computational methods for analyzing single-cell transcriptomics still have room for improvement especially in dimension reduction, cell clustering, and cell–cell communication inference. Herein, we propose a versatile method, named DcjComm, for comprehensive analysis of single-cell transcriptomics. DcjComm detects functional modules to explore expression patterns and performs dimension reduction and clustering to discover cellular identities by the non-negative matrix factorization-based joint learning model. DcjComm then infers cell–cell communication by integrating ligand-receptor pairs, transcription factors, and target genes. DcjComm demonstrates superior performance compared to state-of-the-art methods.

中文翻译：

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使用 DcjComm 对单细胞转录组进行降维、细胞聚类和细胞间通讯推断


单细胞转录组学的进步为探索复杂的生物过程提供了前所未有的机会。然而，用于分析单细胞转录组学的计算方法仍然有改进的空间，特别是在降维、细胞聚类和细胞间通讯推理方面。在这里，我们提出了一种名为 DcjComm 的多功能方法，用于单细胞转录组学的综合分析。 DcjComm 检测功能模块以探索表达模式，并通过基于非负矩阵分解的联合学习模型执行降维和聚类以发现细胞身份。然后，DcjComm 通过整合配体-受体对、转录因子和靶基因来推断细胞间通讯。与最先进的方法相比，DcjComm 表现出了卓越的性能。