Fu Yifeng, Qu Hong, Qu Dacheng, Zhao Min
School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China.
Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, P.R. China.
Bioinformatics. 2025 Feb 4;41(2). doi: 10.1093/bioinformatics/btaf027.
Understanding cell differentiation and development dynamics is key for single-cell transcriptome analysis. Current cell differentiation trajectory inference algorithms face challenges such as high dimensionality, noise, and a need for users to possess certain biological information about the datasets to effectively utilize the algorithms. Here, we introduce Trajectory Inference with Cell-Cell Interaction (TICCI), a novel way to address these challenges by integrating intercellular communication information. In recognizing crucial intercellular communication during development, TICCI proposes Cell-Cell Interactions (CCI) at single-cell resolution. We posit that cells exhibiting higher gene expression similarity patterns are more likely to exchange information via biomolecular mediators.
TICCI is initiated by constructing a cell-neighborhood matrix using edge weights composed of intercellular similarity and CCI information. Louvain partitioning identifies trajectory branches, attenuating noise, while single-cell entropy (scEntropy) is used to assess differentiation status. The Chu-Liu algorithm constructs a directed least-square model to identify trajectory branches, and an improved diffusion fitted time algorithm computes cell-fitted time in nonconnected topologies. TICCI validation on single-cell RNA sequencing (scRNA-seq) datasets confirms the accuracy of cell trajectories, aligning with genealogical branching and gene markers. Verification using extrinsic information labels demonstrates CCI information utility in enhancing accurate trajectory inference. A comparative analysis establishes TICCI proficiency in accurate temporal ordering.
Source code and binaries freely available for download at https://github.com/mine41/TICCI, implemented in R (version 4.32) and Python (version 3.7.16) and supported on MS Windows. Authors ensure that the software is available for a full two years following publication.
理解细胞分化和发育动态是单细胞转录组分析的关键。当前的细胞分化轨迹推断算法面临诸如高维度、噪声等挑战，并且需要用户拥有关于数据集的某些生物学信息才能有效利用这些算法。在这里，我们引入了细胞 - 细胞相互作用轨迹推断（TICCI），这是一种通过整合细胞间通信信息来应对这些挑战的新方法。在识别发育过程中的关键细胞间通信时，TICCI以单细胞分辨率提出细胞 - 细胞相互作用（CCI）。我们认为，表现出更高基因表达相似性模式的细胞更有可能通过生物分子介质交换信息。
TICCI通过使用由细胞间相似性和CCI信息组成的边权重构建细胞邻域矩阵来启动。Louvain分区识别轨迹分支，减弱噪声，同时单细胞熵（scEntropy）用于评估分化状态。Chu - Liu算法构建有向最小二乘模型以识别轨迹分支，并且一种改进的扩散拟合时间算法在非连通拓扑中计算细胞拟合时间。在单细胞RNA测序（scRNA - seq）数据集上对TICCI的验证证实了细胞轨迹的准确性，与谱系分支和基因标记一致。使用外部信息标签的验证证明了CCI信息在增强准确轨迹推断方面的效用。比较分析确定了TICCI在准确时间排序方面的熟练度。
源代码和二进制文件可在https://github.com/mine41/TICCI上免费下载，用R（版本4.32）和Python（版本3.7.16）实现，并在MS Windows上支持。作者确保该软件在发表后整整两年内可用。
