Advances in Bioinformatics
Volume 2009 (2009), Article ID 247646, 12 pages
doi:10.1155/2009/247646
Research Article

Merging Mixture Components for Cell Population Identification in Flow Cytometry

Greg Finak,1 Ali Bashashati,2 Ryan Brinkman,2 and Raphaël Gottardo1,3

1Computational Biology Unit, Clinical Research Institute of Montreal, 110 Pine Avenue West, Montreal, QC, H2W1R7, Canada
2Terry Fox Laboratory, BC Cancer Research Center, Vancouver, BC, V5Z 1L3, Canada
3Département de Biochimie, Université de Montreal, Montreal, QC, Canada

Received 7 April 2009; Revised 6 July 2009; Accepted 22 August 2009

Academic Editor: George Luta

Copyright © 2009 Greg Finak et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. W. Gratama, J. Kraan, M. Keeney, V. Granger, and D. Barnett, “Reduction of variation in T-cell subset enumeration among 55 laboratories using single-platform, three or four-color flow cytometry based on CD45 and SSC-based gating of lymphocytes,” Clinical Cytometry, vol. 50, no. 2, pp. 92–101, 2002. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  2. C. Satoh, K. Dan, T. Yamashita, R. Jo, H. Tamura, and K. Ogata, “Flow cytometric parameters with little interexaminer variability for diagnosing low-grade myelodysplastic syndromes,” Leukemia Research, vol. 32, no. 5, pp. 699–707, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  3. M. Van Blerk, M. Bernier, X. Bossuyt, et al., “National external quality assessment scheme for lymphocyte immunophenotyping in Belgium,” Clinical Chemistry and Laboratory Medicine, vol. 41, no. 3, pp. 323–330, 2003. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  4. R. Achuthanandam, J. Quinn, R. J. Capocasale, P. J. Bugelski, L. Hrebien, and M. Kam, “Sequential univariate gating approach to study the effects of erythropoietin in murine bone marrow,” Cytometry Part A, vol. 73, no. 8, pp. 702–714, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  5. M. J. Boedigheimer and J. Ferbas, “Mixture modeling approach to flow cytometry data,” Cytometry Part A, vol. 73, no. 5, pp. 421–429, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  6. K. Lo, R. R. Brinkman, and R. Gottardo, “Automated gating of flow cytometry data via robust model-based clustering,” Cytometry Part A, vol. 73, no. 4, pp. 321–332, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  7. M. Roederer and R. R. Hardy, “Frequency difference gating: a multivariate method for identifying subsets that differ between samples,” Cytometry, vol. 45, no. 1, pp. 56–64, 2001. View at Publisher · View at Google Scholar · View at Scopus
  8. C. A. Sugar and G. M. James, “Finding the number of clusters in a dataset: an information-theoretic approach,” Journal of the American Statistical Association, vol. 98, no. 463, pp. 750–763, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. G. McLachlan and D. Peel, “Robust cluster analysis via mixtures of multivariate t-distributions,” in Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition, vol. 1451 of Lecture Notes in Computer Science, pp. 658–666, January 1998.
  10. G. Schwarz, “Estimating the dimension of a model,” The Annals of Statistics, vol. 6, no. 2, pp. 461–464, 1978.
  11. C. Biernacki, G. Celeux, and G. Govaert, “Assessing a mixture model for clustering with the integrated completed likelihood,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 7, pp. 719–725, 2000. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Baudry, A. Raftery, G. Celeux, K. Lo, and R. Gottardo, “Combining mixture components for clustering,” Tech. Rep., Universite Paris Sud, University of Washington, University of British Columbia, Vancouver, Canada, August 2008.
  13. S. Pyne, X. Hu, K. Wang, et al., “Automated high-dimensional flow cytometric data analysis,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 21, pp. 8519–8524, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  14. R. C. Gentleman, V. J. Carey, D. M. Bates, et al., “Bioconductor: open software development for computational biology and bioinformatics,” Genome Biology, vol. 5, no. 10, article R80, 2004.
  15. S. Richardson and P. J. Green, “On bayesian analysis of mixtures with an unknown number of components,” Journal of the Royal Statistical Society. Series B, vol. 59, no. 4, pp. 731–792, 1997. View at Scopus
  16. U. Naumann and M. P. Wand, “Automation in high-content flow cytometry screening,” Cytometry Part A, vol. 75, no. 9, pp. 789–797, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus