{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lan T"],"funding":["Australian Research Council Discovery Project"],"pagination":["e122"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9757071"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["50(21)"],"pubmed_abstract":["Tree- and linear-shaped cell differentiation trajectories have been widely observed in developmental biologies and can be also inferred through computational methods from single-cell RNA-sequencing datasets. However, trajectories with complicated topologies such as loops, disparate lineages and bifurcating hierarchy remain difficult to infer accurately. Here, we introduce a density-based trajectory inference method capable of constructing diverse shapes of topological patterns including the most intriguing bifurcations. The novelty of our method is a step to exploit overlapping probability distributions to identify transition states of cells for determining connectability between cell clusters, and another step to infer a stable trajectory through a base-topology guided iterative fitting. "],"journal":["Nucleic acids research"],"pubmed_title":["Density-based detection of cell transition states to construct disparate and bifurcating trajectories."],"pmcid":["PMC9757071"],"funding_grant_id":["DP180100120"],"pubmed_authors":["Hutvagner G","Lan T","Li J","Zhang X","Wong L","Liu T"],"additional_accession":[]},"is_claimable":false,"name":"Density-based detection of cell transition states to construct disparate and bifurcating trajectories.","description":"Tree- and linear-shaped cell differentiation trajectories have been widely observed in developmental biologies and can be also inferred through computational methods from single-cell RNA-sequencing datasets. However, trajectories with complicated topologies such as loops, disparate lineages and bifurcating hierarchy remain difficult to infer accurately. Here, we introduce a density-based trajectory inference method capable of constructing diverse shapes of topological patterns including the most intriguing bifurcations. The novelty of our method is a step to exploit overlapping probability distributions to identify transition states of cells for determining connectability between cell clusters, and another step to infer a stable trajectory through a base-topology guided iterative fitting. ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2026-05-31T11:03:04.354Z","creation":"2025-04-19T10:46:54.938Z"},"accession":"S-EPMC9757071","cross_references":{"pubmed":["36124665"],"doi":["10.1093/nar/gkac785"]}}