Evidence map›Paper›PMID 39898810›Full record

ArticleBioinformatics (Oxford, England)2025

Trajectory Inference with Cell-Cell Interactions (TICCI): intercellular communication improves the accuracy of trajectory inference methods.

Yifeng Fu, Hong Qu, Dacheng Qu, Min Zhao

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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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0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yifeng FuSchool of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China.
Hong QuCenter for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, P.R. China.ORCID 0000-0001-7735-5748
Dacheng QuSchool of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China.
Min ZhaoSchool of Science, Technology and Engineering, University of the Sunshine Coast, Maroochydore DC, QLD 4558, Australia.ORCID 0000-0001-5498-3434

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AlgorithmsCell CommunicationComputational BiologySingle-Cell AnalysisCell DifferentiationGene Expression ProfilingHumansSoftwareTranscriptome

Identifiers

PMID39898810
PMCPMC11829803

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.