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

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.