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Unsupervised Trajectory Inference Using Graph Mining

  • Leen De BaetsEmail author
  • Sofie Van Gassen
  • Tom Dhaene
  • Yvan Saeys
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9874)

Abstract

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.

Keywords

False Positive Rate High False Positive Rate Flow Cytometry Data Single Trajectory Start Cell 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Leen De Baets
    • 1
    Email author
  • Sofie Van Gassen
    • 1
    • 2
  • Tom Dhaene
    • 1
  • Yvan Saeys
    • 2
    • 3
  1. 1.Internet Based Communication Networks and Services (IBCN)Ghent University - iMindsGhentBelgium
  2. 2.Data Mining and Modelling for Biomedicine (DaMBi)VIB Inflammation Research CenterGhentBelgium
  3. 3.Department of Internal MedicineGhent UniversityGhentBelgium

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