{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T11:58:56Z","timestamp":1772539136027,"version":"3.50.1"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T00:00:00Z","timestamp":1738540800000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,2,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>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\u2013Cell Interaction (TICCI), a novel way to address these challenges by integrating intercellular communication information. In recognizing crucial intercellular communication during development, TICCI proposes Cell\u2013Cell 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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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\u2013Liu 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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf027","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T14:48:16Z","timestamp":1738594096000},"source":"Crossref","is-referenced-by-count":5,"title":["Trajectory Inference with Cell\u2013Cell Interactions (TICCI): intercellular communication improves the accuracy of trajectory inference methods"],"prefix":"10.1093","volume":"41","author":[{"given":"Yifeng","family":"Fu","sequence":"first","affiliation":[{"name":"School of Computer Science & Technology, Beijing Institute of Technology , Beijing 100081,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7735-5748","authenticated-orcid":false,"given":"Hong","family":"Qu","sequence":"additional","affiliation":[{"name":"Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University , Beijing 100871,","place":["P.R. China"]}]},{"given":"Dacheng","family":"Qu","sequence":"additional","affiliation":[{"name":"School of Computer Science & Technology, Beijing Institute of Technology , Beijing 100081,","place":["China"]},{"name":"Information Center, China Association for Science and Technology , Beijing 100863,","place":["P.R. China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5498-3434","authenticated-orcid":false,"given":"Min","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Science, Technology and Engineering, University of the Sunshine Coast , Maroochydore DC, QLD 4558,","place":["Australia"]}]}],"member":"286","published-online":{"date-parts":[[2025,2,3]]},"reference":[{"key":"2025030422275602100_btaf027-B1","doi-asserted-by":"crossref","first-page":"e1010715","DOI":"10.1371\/journal.pcbi.1010715","article-title":"Inferring a spatial code of cell\u2013cell interactions across a whole animal body","volume":"18","author":"Armingol","year":"2022","journal-title":"PLoS Comput Biol"},{"key":"2025030422275602100_btaf027-B2","doi-asserted-by":"publisher","first-page":"e0069121","DOI":"10.1128\/msystems.00691-00621","article-title":"Uniform manifold approximation and projection (UMAP) reveals composite patterns and resolves visualization artifacts in microbiome 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