Shi Yuchen, Wan Jian, Zhang Xin, Liang Tingting, Yin Yuyu
Hangzhou Dianzi University, Hangzhou City, Zhejiang Province, China.
Hangzhou Dianzi University, the Key Laboratory of Biomedical Intelligent Computing Technology of Zhejiang Province, and Zhejiang University of Science and Technology, Hangzhou City, Zhejiang Province, China.
Brief Bioinform. 2024 Mar 27;25(3). doi: 10.1093/bib/bbae204.
Trajectory inference is a crucial task in single-cell RNA-sequencing downstream analysis, which can reveal the dynamic processes of biological development, including cell differentiation. Dimensionality reduction is an important step in the trajectory inference process. However, most existing trajectory methods rely on cell features derived from traditional dimensionality reduction methods, such as principal component analysis and uniform manifold approximation and projection. These methods are not specifically designed for trajectory inference and fail to fully leverage prior information from upstream analysis, limiting their performance. Here, we introduce scCRT, a novel dimensionality reduction model for trajectory inference. In order to utilize prior information to learn accurate cells representation, scCRT integrates two feature learning components: a cell-level pairwise module and a cluster-level contrastive module. The cell-level module focuses on learning accurate cell representations in a reduced-dimensionality space while maintaining the cell-cell positional relationships in the original space. The cluster-level contrastive module uses prior cell state information to aggregate similar cells, preventing excessive dispersion in the low-dimensional space. Experimental findings from 54 real and 81 synthetic datasets, totaling 135 datasets, highlighted the superior performance of scCRT compared with commonly used trajectory inference methods. Additionally, an ablation study revealed that both cell-level and cluster-level modules enhance the model's ability to learn accurate cell features, facilitating cell lineage inference. The source code of scCRT is available at https://github.com/yuchen21-web/scCRT-for-scRNA-seq.
轨迹推断是单细胞RNA测序下游分析中的一项关键任务，它可以揭示生物发育的动态过程，包括细胞分化。降维是轨迹推断过程中的重要一步。然而，大多数现有的轨迹方法依赖于从传统降维方法（如主成分分析和均匀流形近似与投影）导出的细胞特征。这些方法并非专门为轨迹推断而设计，未能充分利用上游分析的先验信息，从而限制了它们的性能。在此，我们介绍了scCRT，一种用于轨迹推断的新型降维模型。为了利用先验信息来学习准确的细胞表示，scCRT集成了两个特征学习组件：一个细胞级成对模块和一个聚类级对比模块。细胞级模块专注于在低维空间中学习准确的细胞表示，同时保持原始空间中的细胞-细胞位置关系。聚类级对比模块使用先验细胞状态信息来聚集相似细胞，防止在低维空间中过度分散。来自54个真实数据集和81个合成数据集（共135个数据集）的实验结果突出了scCRT与常用轨迹推断方法相比的卓越性能。此外，一项消融研究表明，细胞级和聚类级模块都增强了模型学习准确细胞特征的能力，有助于细胞谱系推断。scCRT的源代码可在https://github.com/yuchen21-web/scCRT-for-scRNA-seq获取。
