Identifying Rare Cell Populations in Comparative Flow Cytometry

  • Ariful Azad
  • Johannes Langguth
  • Youhan Fang
  • Alan Qi
  • Alex Pothen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6293)


Multi-channel, high throughput experimental methodologies for flow cytometry are transforming clinical immunology and hematology, and require the development of algorithms to analyze the high-dimensional, large-scale data. We describe the development of two combinatorial algorithms to identify rare cell populations in data from mice with acute promyelocytic leukemia. The flow cytometry data is clustered, and then samples from the leukemic, pre-leukemic, and Wild Type mice are compared to identify clusters belonging to the diseased state. We describe three metrics on the clustered data that help in identifying rare populations. We formulate a generalized edge cover approach in a bipartite graph model to directly compare clusters in two samples to identify clusters belonging to one but not the other sample. For detecting rare populations common to many diseased samples but not to the Wild Type, we describe a clique-based branch and bound algorithm. We provide statistical justification of the significance of the rare populations.


flow cytometry edge cover clique mixture modeling KL divergence acute promyelocytic leukemia (APL) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Antoniak, C.E.: Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. Annals of Statistics 2(6), 1152–1174 (1974)CrossRefGoogle Scholar
  2. 2.
    Bashashati, A., Brinkman, R.: A survey of flow cytometry data analysis methods. In: Advances in Bioinformatics, pp. 1–19 (December 2009)Google Scholar
  3. 3.
    Boedigheimer, M., Ferbas, J.: Mixture modeling approach to flow cytometry data. Cytometry A 73, 421–429 (2008)CrossRefPubMedGoogle Scholar
  4. 4.
    Chan, C., Feng, F., Ottinger, J., et al.: Statistical mixture modeling for cell subtype identification in flow cytometry. Cytometry A 73(A), 693–701 (2008)CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Herzenberg, L., Tung, J., Moore, W., et al.: Interpreting flow cytometry data: A guide for the perplexed. Nature Immunology 7(7), 681–685 (2006)CrossRefPubMedGoogle Scholar
  6. 6.
    Kullback, S.: Information Theory and Statistics. Dover Publications Inc., Mineola (1968)Google Scholar
  7. 7.
    Meur, N., Rossini, A., Gasparetto, M., Smith, C., Brinkman, R., Gentleman, R.: Data quality assessment of ungated flow cytometry data in high throughput experiments. Cytometry A 71A, 393–403 (2007)CrossRefGoogle Scholar
  8. 8.
    Moore, D., McCabe, G.: Introduction to the Practice of Statistics. W. H. Freeman & Co., New York (2006)Google Scholar
  9. 9.
    Neal, R.: Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics 9, 249–265 (2000)Google Scholar
  10. 10.
    Pyne, S., Hu, X., Wang, K., et al.: Automated high-dimensional flow cytometric data analysis. PNAS 106(21), 8519–8524 (2009)CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Rasmussen, C.E.: The infinite Gaussian mixture model. In: Solla, S., Leen, T., Muller, K.R. (eds.) Advances in Neural Information Processing Systems, vol. 12. MIT Press, Cambridge (2000)Google Scholar
  12. 12.
    De Rosa, S., Brenchley, J., Roederer, M.: Beyond six colors: A new era in flow cytometry. Nature Medicine 9(1), 112–117 (2003)CrossRefPubMedGoogle Scholar
  13. 13.
    Schrijver, A.: Combinatorial Optimization — Polyhedra and Efficiency, Volume A: Paths, Flows, Matchings. Algorithms and Combinatorics, vol. 24. Springer, New York (2003)Google Scholar
  14. 14.
  15. 15.
    Wojiski, S., Gubal, F.C., Kindler, T., et al.: PML-RARα initiates leukemia by conferring properties of self-renewal to committed promyelocytic progenitors. Leukemia 23, 1462–1471 (2009)CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ariful Azad
    • 1
  • Johannes Langguth
    • 2
  • Youhan Fang
    • 1
  • Alan Qi
    • 1
  • Alex Pothen
    • 1
  1. 1.Dept. Computer SciencePurdue UniversityWest LafayetteUSA
  2. 2.Department of InformaticsUniversity of BergenBergenNorway

Personalised recommendations