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Published VersionCC BY V4.0, Open Access
Journal article
Trajectory Inference with Cell-Cell Interactions (TICCI): intercellular communication improves the accuracy of trajectory inference methods
Bioinformatics, Vol.41(2), pp.1-11
2025
PMID: 39898810
Abstract
Motivation:
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.
Results:
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.
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.
Details
- Title
- Trajectory Inference with Cell-Cell Interactions (TICCI): intercellular communication improves the accuracy of trajectory inference methods
- Authors
- Yifeng Fu - Beijing Institute of TechnologyHong Qu (Corresponding Author) - Peking UniversityDacheng Qu (Corresponding Author) - Beijing Institute of TechnologyMin Zhao (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
- Publication details
- Bioinformatics, Vol.41(2), pp.1-11
- Publisher
- Oxford University Press
- Date published
- 2025
- DOI
- 10.1093/bioinformatics/btaf027
- ISSN
- 1367-4811; 1367-4803
- PMID
- 39898810
- Copyright note
- © The Author(s) 2025. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
- Data Availability
- Source code and binaries freely available for download at https://github.com/mine41/TICCI.
- Organisation Unit
- Cancer Research Cluster; School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991125706002621
- Output Type
- Journal article
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- Domestic collaboration
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- Web Of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Statistics & Probability
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