Machine Learning Challenges for Single Cell Data

  • Sofie Van GassenEmail author
  • Tom Dhaene
  • Yvan Saeys
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9853)


Recent technological advances in the fields of biology and medicine allow measuring single cells into unprecedented depth. This results in new types of high-throughput datasets that shed new lights on cell development, both in healthy as well as diseased tissues. However, studying these biological processes into greater detail crucially depends on novel computational techniques that efficiently mine single cell data sets. In this paper, we introduce machine learning techniques for single cell data analysis: we summarize the main developments in the field, and highlight a number of interesting new avenues that will likely stimulate the design of new types of machine learning algorithms.


Bioinformatics Single cell analysis Machine learning 


  1. 1.
    Van Gassen, S., et al.: FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87(7), 636–645 (2015)CrossRefGoogle Scholar
  2. 2.
    Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(85), 2579–2605 (2008)zbMATHGoogle Scholar
  3. 3.
    Aghaeepour, N., et al.: Critical assessment of automated flow cytometry data analysis techniques. Nat. Methods 10(5), 445–445 (2013)CrossRefGoogle Scholar
  4. 4.
    Aghaeepour, N., et al.: A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry A (2015). doi: 10.1002/cyto.a.22732
  5. 5.
    Van Gassen, S., Vens, C., Dhaene, T., Lambrecht, B.N., Saeys, Y.: FloReMi: flow density survival regression using minimal feature redundancy. Cytometry A (2015). doi: 10.1002/cyto.a.22734
  6. 6.
    Vens, C., Van Gassen, S., Dhaene, T., Saeys, Y.: Complex aggregates over clusters of elements. In: Davis, J., Ramon, J. (eds.) ILP 2014. LNCS(LNAI), vol. 9046, pp. 181–193. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  7. 7.
    Bendall, S.C., et al.: Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157(3), 714–725 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.VIB Inflammation Research CenterGhentBelgium
  2. 2.Department of Information TechnologyGhent University, iMindsGhentBelgium
  3. 3.Department of Internal MedicineGhent UniversityGhentBelgium

Personalised recommendations