ArticleBioinformatics (Oxford, England)2025
Trajectory Inference with Cell-Cell Interactions (TICCI): intercellular communication improves the accuracy of trajectory inference methods.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Understanding the immune microenvironment of ovarian cancer.Frontiers in oncology · 2026Review
- Elevated Serum Chromogranin-A and Characteristic Duodenal Enteroendocrine Cell Distribution in Pancreatic Fibrosis and Chronic Pancreatitis Compared with Other Dyspeptic Disorders: A Case Series Study.Diagnostics (Basel, Switzerland) · 2025Article
- Finding spatially variable ligand-receptor interactions with functional support from downstream genes.Nature communications · 2025Article
- Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions.Current issues in molecular biology · 2025Review
- Single-cell sequencing reveals the tumor immune microenvironment in thyroid cancer: a narrow review.Frontiers in immunology · 2025Review
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4 authors.
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Abstract
motivationUnderstanding 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.
resultsTICCI 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. AVAILABILITY AND IMPLEMENTATION: 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.
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