Unsupervised Trajectory Inference Using Graph Mining
Cell differentiation is a complex dynamic process and although the main cellular states are well studied, the intermediate stages are often still unknown. Single cell data (such as obtained by flow cytometry) is typically analysed by clustering the cells into distinct cell types, which does not model these gradual changes. Alternative approaches that explicitly model such gradual changes using seriation methods seems promising, but are only able to model a single differentiation pathway. In this paper, we introduce a new, graph-based approach that is able to model multiple branching differentiation pathways as continuous trajectories. Results on synthetic and real data show that this is a promising approach which is moreover robust to parameter changes.
KeywordsFalse Positive Rate High False Positive Rate Flow Cytometry Data Single Trajectory Start Cell
We would like to thank Lianne van de Laar and Bart Lambrecht for providing a biologically relevant dataset to test our algorithm. Sofie Van Gassen is funded by the Flanders Agency for Innovation by Science and Technology (IWT).
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