TimeFlow 2 : An Unsupervised Cell Lineage Detection Method for Flow Cytometry Data

Article Article scientifique Article TimeFlow 2 : An Unsupervised Cell Lineage Detection Method for Flow Cytometry Data eng 2026-01-08 author Liarou Margarita 0009-0009-2043-5742 921843 Département d'informatique (DINFO) author Matthes Thomas 27368 author Marchand-Maillet Stéphane 0000-0002-4875-6101 518563 Département d'informatique (DINFO) 42-61 10.1002/cytoa.70006 41503891 1552-4922 https://onlinelibrary.wiley.com/doi/10.1002/cytoa.70006 Cell lineage detection refers to the inference of differentiation pathways from immature cells to distinct mature cell types. We developed TimeFlow 2, a new method for lineage inference in large flow cytometry datasets. It uses a single static snapshot of unordered cells and does not require prior knowledge of the number of pathways, cell types or temporal labels. TimeFlow 2 uses the cell orderings from TimeFlow and defines coarse cell states along pseudotime segments. By connecting these states, it constructs paths at the cell state level. To approximate the trajectory structure, it further groups the paths based on an optimal transport-based cost function. We used TimeFlow 2 on three healthy bone marrow samples and accurately assigned monocytes, neutrophils, erythrocytes and B-cells of different maturation stages to four distinct pathways. Marker dynamics across the inferred pathways showed highly correlated patterns for the corresponding lineages in all three patients. We compared the performance of TimeFlow 2 and three other established methods using standard classification and correlation metrics. TimeFlow 2 outperformed the others on flow cytometry datasets and remained competitive on the challenging mass cytometry datasets. Overall, TimeFlow 2 detects biologically informative pathways, allowing bioinformaticians to model and compare marker dynamics across cell lineages in a data-driven way. Source code in Python and tutorials are available at https://github.com/MargaritaLiarou1/TimeFlow2. Bone marrow data Cell differentiation Flow cytometry Optimal transport Path clustering Pseudotime analysis Trajectory inference Dewey 616.07 Dewey 616 Dewey 025.063 Swiss National Science Foundation Structural Intrinsic Dimensionality 207509 CH 32 Département d'informatique 18 137 Département de pathologie et immunologie 21F 56 Département de médecine 221 false 675 Viper group https://github.com/MargaritaLiarou1/TimeFlow2 https://osf.io/ykue7/files/osfstorage https://flowrepository.org/id/FR-FCM-ZYQV Cytometry. 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TimeFlow 2 : An Unsupervised Cell Lineage Detection Method for Flow Cytometry Data	Cytometry. Part A	2026	16	172
TimeFlow : A Density‐Driven Pseudotime Method for Flow Cytometry Data Analysis	Cytometry. Part A	2025	74	50

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