Table of Contents Author Guidelines Submit a Manuscript
Advances in Bioinformatics
Volume 2009, Article ID 584603, 19 pages
http://dx.doi.org/10.1155/2009/584603
Review Article

A Survey of Flow Cytometry Data Analysis Methods

Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada V5Z 1L3

Received 1 May 2009; Revised 20 July 2009; Accepted 22 August 2009

Academic Editor: George Luta

Copyright © 2009 Ali Bashashati and Ryan R. Brinkman. 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. R. C. Braylan, “Impact of flow cytometry on the diagnosis and characterization of lymphomas, chronic lymphoproliferative disorders and plasma cell neoplasias,” Cytometry A, vol. 58, no. 1, pp. 57–61, 2004. View at Google Scholar · View at Scopus
  2. R. L. Hengel and J. K. A. Nicholson, “An update on the use of flow cytometry in HIV infection and AIDS,” Clinics in Laboratory Medicine, vol. 21, no. 4, pp. 841–856, 2001. View at Google Scholar · View at Scopus
  3. O. C. Illoh, “Current applications of flow cytometry in the diagnosis of primary immunodeficiency diseases,” Archives of Pathology and Laboratory Medicine, vol. 128, no. 1, pp. 23–31, 2004. View at Google Scholar · View at Scopus
  4. F. L. Kiechle and C. A. Holland-Staley, “Genomics, transcriptomics, proteomics, and numbers,” Archives of Pathology and Laboratory Medicine, vol. 127, no. 9, pp. 1089–1097, 2003. View at Google Scholar
  5. F. F. Mandy, “Twenty-five years of clinical flow cytometry: AIDS accelerated global instrument distribution,” Cytometry A, vol. 58, no. 1, pp. 55–56, 2004. View at Google Scholar · View at Scopus
  6. A. Orfao, F. Ortuño, M. de Santiago, A. Lopez, and J. San Miguel, “Immunophenotyping of acute leukemias and myelodysplastic syndromes,” Cytometry A, vol. 58, no. 1, pp. 62–71, 2004. View at Google Scholar · View at Scopus
  7. C. B. Bagwell, “DNA histogram analysis for node-negative breast cancer,” Cytometry A, vol. 58, no. 1, pp. 76–78, 2004. View at Google Scholar · View at Scopus
  8. M. Keeney, J. W. Gratama, and D. R. Sutherland, “Critical role of flow cytometry in evaluating peripheral blood hematopoetic stem cell grafts,” Cytometry A, vol. 58, no. 1, pp. 72–75, 2004. View at Google Scholar · View at Scopus
  9. P. O. Krutzik, J. M. Irish, G. P. Nolan, and O. D. Perez, “Analysis of protein phosphorylation and cellular signaling events by flow cytometry: techniques and clinical applications,” Clinical Immunology, vol. 110, no. 3, pp. 206–221, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. H. Maecker and V. Maino, “Flow cytometric analysis of cytokines,” in Manual of Clinical Laboratory Immunology, ASM Press, Washington, DC, USA, 6th edition, 2002. View at Google Scholar
  11. P. Pozarowski and Z. Darzynkiewicz, “Analysis of cell cycle by flow cytometry,” Methods in Molecular Biology, vol. 281, pp. 301–311, 2004. View at Google Scholar · View at Scopus
  12. P. Pala, T. Hussell, and P. J. M. Openshaw, “Flow cytometric measurement of intracellular cytokines,” Journal of Immunological Methods, vol. 243, no. 1-2, pp. 107–124, 2000. View at Publisher · View at Google Scholar · View at Scopus
  13. I. Vermes, C. Haanen, and C. Reutelingsperger, “Flow cytometry of apoptotic cell death,” Journal of Immunological Methods, vol. 243, no. 1-2, pp. 167–190, 2000. View at Publisher · View at Google Scholar · View at Scopus
  14. A. K. Lehmann, S. Sørnes, and A. Halstensen, “Phagocytosis: measurement by flow cytometry,” Journal of Immunological Methods, vol. 243, no. 1-2, pp. 229–242, 2000. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. D. Mahnke and M. Roederer, “Optimizing a multicolor immunophenotyping assay,” Clinics in Laboratory Medicine, vol. 27, no. 3, pp. 469–485, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. D. Redelman, “CytometryML,” Cytometry A, vol. 62, no. 1, pp. 70–73, 2004. View at Publisher · View at Google Scholar · View at Scopus
  17. 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
  18. M. A. Suni, H. S. Dunn, P. L. Orr et al., “Performance of plate-based cytokine flow cytometry with automated data analysis,” BMC Immunology, vol. 4, article 9, 2003. View at Google Scholar
  19. J. O. Ramsay and B. W. Silverman, Functional Data Analysis, Springer, Berlin, Germany, 1996.
  20. L. A. Herzenberg, D. Parks, B. Sahaf, O. Perez, M. Roederer, and L. A. Herzenberg, “The history and future of the fluorescence activated cell sorter and flow cytometry: a view from Stanford,” Clinical Chemistry, vol. 48, no. 10, pp. 1819–1827, 2002. View at Google Scholar · View at Scopus
  21. W. R. Overton, “Modified histogram subtraction technique for analysis of flow cytometry data,” Cytometry, vol. 9, no. 6, pp. 619–626, 1988. View at Google Scholar · View at Scopus
  22. M. Roederer, “Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats,” Cytometry, vol. 45, no. 3, pp. 194–205, 2001. View at Publisher · View at Google Scholar · View at Scopus
  23. S. C. De Rosa, J. M. Brenchley, and M. Roederer, “Beyond six colors: a new era in flow cytometry,” Nature Medicine, vol. 9, no. 1, pp. 112–117, 2003. View at Publisher · View at Google Scholar · View at Scopus
  24. S. P. Perfetto, P. K. Chattopadhyay, and M. Roederer, “Seventeen-colour flow cytometry: unravelling the immune system,” Nature Reviews Immunology, vol. 4, no. 8, pp. 648–655, 2004. View at Google Scholar · View at Scopus
  25. D. R. Parks, “Data processing and analysis: data management,” in Current Protocols in Cytometry, J. P. Robinson, Z. Darkzynkiewicz, P. N. Dean et al., Eds., pp. 10.1.1–10.1.6, John Wiley & Sons, New York, NY, USA, 1997. View at Google Scholar
  26. H. M. Shapiro, “The evolution of cytometers,” Cytometry A, vol. 58, no. 1, pp. 13–20, 2004. View at Google Scholar · View at Scopus
  27. Z. Darzynkiewickz, H. Crissman, and J. W. Jacobberger, “Cytometry of the cell cycle: cycling through history,” Cytometry A, vol. 58, no. 1, pp. 21–32, 2004. View at Google Scholar · View at Scopus
  28. L. S. Cram, J. W. Gray, and N. P. Carter, “Cytometry and genetics,” Cytometry A, vol. 58, no. 1, pp. 33–36, 2004. View at Google Scholar · View at Scopus
  29. G. Tzircotis, R. F. Thorne, and C. M. Isacke, “A new spreadsheet method for the analysis of bivariate flow cytometric data,” BMC Cell Biology, vol. 5, article 10, 2004. View at Publisher · View at Google Scholar · View at Scopus
  30. C. Chan, F. Feng, J. Ottinger, D. Foster, M. West, and T. B. Kepler, “Statistical mixture modeling for cell subtype identification in flow cytometry,” Cytometry A, vol. 73, no. 8, pp. 693–701, 2008. View at Google Scholar · View at Scopus
  31. G. Lizard, “Flow cytometry analyses and bioinformatics: interest in new softwares to optimize novel technologies and to favor the emergence of innovative concepts in cell research,” Cytometry A, vol. 71, no. 9, pp. 646–647, 2007. View at Publisher · View at Google Scholar · View at Scopus
  32. H. M. Shapiro, Practical Flow Cytometry, Oxford University Press, New York, NY, USA, 2003.
  33. D. W. Galbraith, “Cytometry and plant sciences: a personal retrospective,” Cytometry A, vol. 58, no. 1, pp. 37–44, 2004. View at Google Scholar · View at Scopus
  34. R. C. Leif, J. H. Stein, and R. M. Zucker, “A short history of the initial application of anti-5-BrdU to the detection and measurement of S phase,” Cytometry A, vol. 58, no. 1, pp. 45–52, 2004. View at Google Scholar · View at Scopus
  35. J. W. Gratama and F. Kern, “Flow cytometric enumeration of antigen-specific T lymphocytes,” Cytometry A, vol. 58, no. 1, pp. 79–86, 2004. View at Google Scholar · View at Scopus
  36. H. T. Maecker, A. Rinfret, P. D'Souza et al., “Standardization of cytokine flow cytometry assays,” BMC Immunology, vol. 6, article 13, 2005. View at Publisher · View at Google Scholar · View at Scopus
  37. M. Chicurel, “Bioinformatics: bringing it all together,” Nature, vol. 419, no. 6908, pp. 751–757, 2002. View at Publisher · View at Google Scholar · View at Scopus
  38. M. S. Boguski and M. W. McIntosh, “Biomedical informatics for proteomics,” Nature, vol. 422, no. 6928, pp. 233–237, 2003. View at Publisher · View at Google Scholar · View at Scopus
  39. M. Keeney, D. Barnett, and J. W. Gratama, “Impact of standardization on clinical cell analysis by flow cytometry,” Journal of Biological Regulators and Homeostatic Agents, vol. 18, no. 3-4, pp. 305–312, 2004. View at Google Scholar · View at Scopus
  40. P. M. Ravdin, G. M. Clark, J. J. Hough, M. A. Owens, and W. L. McGuire, “Neural network analysis of DNA flow cytometry histograms,” Cytometry, vol. 14, no. 1, pp. 74–80, 1993. View at Publisher · View at Google Scholar · View at Scopus
  41. W. G. Finn, K. M. Carter, R. Raich, L. M. Stoolman, and A. O. Hero, “Analysis of clinical flow cytometric immunophenotyping data by clustering on statistical manifolds: treating flow cytometry data as high-dimensional objects,” Cytometry B, vol. 76, no. 1, pp. 1–7, 2009. View at Publisher · View at Google Scholar · View at Scopus
  42. N. Le Meur, A. Rossini, M. Gasparetto, C. Smith, R. R. Brinkman, and R. Gentleman, “Data quality assessment of ungated flow cytometry data in high throughput experiments,” Cytometry A, vol. 71, no. 6, pp. 393–403, 2007. View at Publisher · View at Google Scholar · View at Scopus
  43. R. R. Brinkman, M. Gasparetto, S. J. J. Lee et al., “High-content flow cytometry and temporal data analysis for defining a cellular signature of graft-versus-host disease,” Biology of Blood and Marrow Transplantation, vol. 13, no. 6, pp. 691–700, 2007. View at Publisher · View at Google Scholar · View at Scopus
  44. F. Hahne, D. Arlt, M. Sauermann et al., “Statistical methods and software for the analysis of highthroughput reverse genetic assays using flow cytometry readouts,” Genome Biology, vol. 7, no. 8, p. R77, 2006. View at Publisher · View at Google Scholar · View at Scopus
  45. D. Jeffries, I. Zaidi, B. de Jong, M. J. Holland, and D. J. C. Miles, “Analysis of flow cytometry data using an automatic processing tool,” Cytometry A, vol. 73, no. 9, pp. 857–867, 2008. View at Publisher · View at Google Scholar · View at Scopus
  46. K. Lo, R. R. Brinkman, and R. Gottardo, “Automated gating of flow cytometry data via robust model-based clustering,” Cytometry A, vol. 73, no. 4, pp. 321–332, 2008. View at Publisher · View at Google Scholar · View at Scopus
  47. E. S. Costa, M. E. Arroyo, C. E. Pedreira et al., “A new automated flow cytometry data analysis approach for the diagnostic screening of neoplastic B-cell disorders in peripheral blood samples with absolute lymphocytosis,” Leukemia, vol. 20, no. 7, pp. 1221–1230, 2006. View at Publisher · View at Google Scholar · View at Scopus
  48. D. S. Frankel, S. L. Frankel, B. J. Binder, and R. F. Vogt, “Application of neural networks to flow cytometry data analysis and real-time cell classification,” Cytometry, vol. 23, no. 4, pp. 290–302, 1996. View at Publisher · View at Google Scholar · View at Scopus
  49. M. A. Suni, H. S. Dunn, P. L. Orr et al., “Performance of plate-based cytokine flow cytometry with automated data analysis,” BMC Immunology, vol. 4, 2003. View at Publisher · View at Google Scholar · View at Scopus
  50. M. F. Wilkins, S. A. Hardy, L. Boddy, and C. W. Morris, “Comparison of five clustering algorithms to classify phytoplankton from flow cytometry data,” Cytometry, vol. 44, no. 3, pp. 210–217, 2001. View at Publisher · View at Google Scholar · View at Scopus
  51. L. K. Habib and W. G. Finn, “Unsupervised immunophenotypic profiling of chronic lymphocytic leukemia,” Cytometry B, vol. 70, no. 3, pp. 124–135, 2006. View at Publisher · View at Google Scholar · View at Scopus
  52. M. Roederer, W. Moore, A. Treister, R. R. Hardy, and L. A. Herzenberg, “Probability binning comparison: a metric for quantitating multivariate distribution differences,” Cytometry, vol. 45, no. 1, pp. 47–55, 2001. View at Publisher · View at Google Scholar · View at Scopus
  53. Q. T. Zeng, J. P. Pratt, J. Pak, D. Ravnic, H. Huss, and S. J. Mentzer, “Feature-guided clustering of multi-dimensional flow cytometry datasets,” The Journal of Biomedical Informatics, vol. 40, no. 3, pp. 325–331, 2007. View at Publisher · View at Google Scholar · View at Scopus
  54. M. F. Wilkins, L. Boddy, C. W. Morris, and R. Jonker, “A comparison of some neural and non-neural methods for identification of phytoplankton from flow cytometry data,” Computer Applications in the Biosciences, vol. 12, no. 1, pp. 9–18, 1996. View at Google Scholar · View at Scopus
  55. G. K. Valet and H. G. Höffkes, “Automated classification of patients with chronic lymphocytic leukemia and immunocytoma from flow cytometric three-color immunophenotypes,” Cytometry, vol. 30, no. 6, pp. 275–288, 1997. View at Publisher · View at Google Scholar · View at Scopus
  56. G. Valet, M. Valet, D. Tschope et al., “White cell and thrombocyte disorders. Standardized, self-learning flow cytometric list mode data classification with the CLASSIF1 program system,” Annals of the New York Academy of Sciences, vol. 677, pp. 233–251, 1993. View at Publisher · View at Google Scholar · View at Scopus
  57. G. Valet, “Data pattern classification by the CLASSIF1 data sieving algorithm,” http://www.classimed.de/classif1.html.
  58. V. C. Maino and H. T. Maecker, “Cytokine flow cytometry: a multiparametric approach for assessing cellular immune responses to viral antigens,” Clinical Immunology, vol. 110, no. 3, pp. 222–231, 2004. View at Publisher · View at Google Scholar · View at Scopus
  59. M. J. Boedigheimer and J. Ferbas, “Mixture modeling approach to flow cytometry data,” Cytometry A, vol. 73, no. 5, pp. 421–429, 2008. View at Publisher · View at Google Scholar · View at Scopus
  60. C. M. Kitsos, P. Bhamidipati, I. Melnikova et al., “Combination of automated high throughput platforms, flow cytometry, and hierarchical clustering to detect cell state,” Cytometry A, vol. 71, no. 1, pp. 16–27, 2007. View at Publisher · View at Google Scholar · View at Scopus
  61. Y. W. Qian, D. Mital, and S. Lee, “An online decision support system for diagnosing hematopoietic malignancies by flow cytometry immunophenotyping,” in Proceedings of the AMIA Annual Symposium, p. 1084, 2007. View at Scopus
  62. J. Frelinger, T. B. Kepler, and C. Chan, “Flow: statistics, visualization and informatics for flow cytometry,” Source Code for Biology and Medicine, vol. 3, p. 10, 2008. View at Publisher · View at Google Scholar · View at Scopus
  63. H. T. Maeker and V. C. Maino, “Analyzing T-cell responses to cytomegalovirus by cytokine flow cytometry,” Human Immunology, vol. 65, no. 5, pp. 493–499, 2004. View at Publisher · View at Google Scholar · View at Scopus
  64. M. Dostál, Y. Giguère, T. Fait, J. Živný, and R. J. Šrám, “The distribution of major lymphocyte subsets in cord blood is associated with its pH,” Clinical Biochemistry, vol. 34, no. 2, pp. 119–124, 2001. View at Publisher · View at Google Scholar · View at Scopus
  65. S. Andreatta, M. M. Wallinger, T. Posch, and R. Psenner, “Detection of subgroups from flow cytometry measurements of heterotrophic bacterioplankton by image analysis,” Cytometry, vol. 44, no. 3, pp. 218–225, 2001. View at Google Scholar
  66. G. Grégori, A. Colosimo, and M. Denis, “Phytoplankton group dynamics in the Bay of Marseilles during a 2-year survey based on analytical flow cytometry,” Cytometry, vol. 44, no. 3, pp. 247–256, 2001. View at Google Scholar
  67. G. Valet, H. Kahle, F. Otto, E. Brautigam, and L. Kestens, “Prediction and precise diagnosis of diseases by data pattern analysis in multiparameter flow cytometry: melanoma, juvenile asthma, and human immunodeficiency virus infection,” Methods in Cell Biology, vol. 64, pp. 487–508, 2001. View at Google Scholar · View at Scopus
  68. U. Petrausch, D. Haley, W. Miller, K. Floyd, W. J. Urba, and E. Walker, “Polychromatic flow cytometry: a rapid method for the reduction and analysis of complex multiparameter data,” Cytometry A, vol. 69, no. 12, pp. 1162–1173, 2006. View at Publisher · View at Google Scholar · View at Scopus
  69. G. Valet, G. Roth, and W. Kellermann, “Risk assessment for intensive care patients by automated classification of flow cytometric data,” in Cytometric Cellular Analysis: Phagocyte Function, pp. 289–306, Wiley-Liss, New York, NY, USA, 1998. View at Google Scholar
  70. L. W. Diamond, D. T. Nguyen, M. Andreeff, R. L. Maiese, and R. C. Braylan, “A knowledge-based system for the interpretation of flow cytometry data in leukemias and lymphomas,” Cytometry, vol. 17, no. 3, pp. 266–273, 1994. View at Publisher · View at Google Scholar · View at Scopus
  71. W. J. Clancey, “Heuristic classification,” Artificial Intelligence, vol. 27, no. 3, pp. 289–350, 1985. View at Google Scholar
  72. T. C. Bakker Schut, B. G. De Grooth, and J. Greve, “Cluster analysis of flow cytometric list mode data on a personal computer,” Cytometry, vol. 14, no. 6, pp. 649–659, 1993. View at Google Scholar · View at Scopus
  73. R. F. Murphy, “Automated identification of subpopulations in flow cytometric list mode data using cluster analysis,” Cytometry, vol. 6, no. 4, pp. 302–309, 1985. View at Google Scholar · View at Scopus
  74. H. Balfoort, J. Snoek, J. Smiths, L. Breedveld, J. Hofstraat, and J. Ringelberg, “Automatic identification of algae: neural network analysis of flow cytometric data,” Journal of Plankton Research, vol. 14, pp. 575–589, 1992. View at Google Scholar
  75. M. F. Wilkins, L. Boddy, C. W. Morris, and R. R. Jonker, “Identification of phytoplankton from flow cytometry data by using radial basis function neural networks,” Applied and Environmental Microbiology, vol. 65, no. 10, pp. 4404–4410, 1999. View at Google Scholar · View at Scopus
  76. M. Godavarti, J. J. Rodriguez, T. A. Yopp, G. M. Lambert, and D. W. Galbraith, “Automated particle classification based on digital acquisition and analysis of flow cytometric pulse waveforms,” Cytometry, vol. 24, no. 4, pp. 330–339, 1996. View at Publisher · View at Google Scholar · View at Scopus
  77. R. Kothari, H. Cualing, and T. Balachander, “Neural network analysis of flow cytometry immunophenotype data,” IEEE Transactions on Biomedical Engineering, vol. 43, no. 8, pp. 803–810, 1996. View at Google Scholar · View at Scopus
  78. R. Jonker, R. Groben, G. Tarran et al., “Automated identification and characterisation of microbial populations using flow cytometry: the AIMS project,” Scientia Marina, vol. 64, no. 2, pp. 225–234, 2000. View at Google Scholar · View at Scopus
  79. R. J. Beckman, G. C. Salzman, and C. C. Stewart, “Classification and regression trees for bone marrow immunophenotyping,” Cytometry, vol. 20, no. 3, pp. 210–217, 1995. View at Publisher · View at Google Scholar · View at Scopus
  80. C. W. Morris, A. Autret, and L. Boddy, “Support vector machines for identifying organisms—a comparison with strongly partitioned radial basis function networks,” Ecological Modelling, vol. 146, no. 1–3, pp. 57–67, 2001. View at Publisher · View at Google Scholar · View at Scopus
  81. L. Boddy, C. W. Morris, M. F. Wilkins et al., “Identification of 72 phytoplankton species by radial basis function neural network analysis of flow cytometric data,” Marine Ecology Progress Series, vol. 195, pp. 47–59, 2000. View at Google Scholar · View at Scopus
  82. S. Demers, J. Kim, P. Legendre, and L. Legendre, “Analyzing multivariate flow cytometric data in aquatic sciences,” Cytometry, vol. 13, no. 3, pp. 291–298, 1992. View at Google Scholar · View at Scopus
  83. C. E. Pedreira, E. S. Costa, M. E. Arroyo, J. Almeida, and A. Orfao, “A multidimensional classification approach for the automated analysis of flow cytometry data,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 3, pp. 1155–1162, 2008. View at Publisher · View at Google Scholar · View at Scopus
  84. M. Steinbrich-Zöllner, J. R. Grün, T. Kaiser et al., “From transcriptome to cytome: integrating cytometric profiling, multivariate cluster, and prediction analyses for a phenotypical classification of inflammatory diseases,” Cytometry A, vol. 73, no. 4, pp. 333–340, 2008. View at Google Scholar
  85. R. Tibshirani, T. Hastie, B. Narasimhan, and G. Chu, “Diagnosis of multiple cancer types by shrunken centroids of gene expression,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 10, pp. 6567–6572, 2002. View at Publisher · View at Google Scholar · View at Scopus
  86. M. D. Richard and R. P. Lippmann, “Neural network classifiers estimate Bayesian a posteriori probabilities,” Neural Computation, vol. 3, pp. 461–483, 1991. View at Google Scholar
  87. E. Lugli, M. Pinti, M. Nasi et al., “Subject classification obtained by cluster analysis and principal component analysis applied to flow cytometric data,” Cytometry A, vol. 71, no. 5, pp. 334–344, 2007. View at Publisher · View at Google Scholar · View at Scopus
  88. I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” The Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 2003. View at Google Scholar
  89. Anonymous FlowQ, “Qualitiy control for flow cytometry,” http://www.bioconductor.org/packages/2.2/bioc/html/flowQ.html.
  90. B. Dykstra, D. Kent, M. Bowie et al., “Long-term propagation of distinct hematopoietic differentiation programs in vivo,” Cell Stem Cell, vol. 1, no. 2, pp. 218–229, 2007. View at Publisher · View at Google Scholar · View at Scopus
  91. D. Kent, B. Dykstra, and C. Eaves, “Isolation and assessment of long-term reconstituting hematopoietic stem cells from adult mouse bone marrow,” in Current Protocols in Stem Cell Biology, vol. 2, Unit 2A.4, 2007. View at Google Scholar
  92. J. D. Banfield and A. E. Raftery, “Model-based gaussian and non-gaussian clustering,” Biometrics, vol. 49, no. 3, pp. 803–821, 1993. View at Google Scholar · View at Scopus
  93. C. Fraley and A. E. Raftery, “Model-based clustering, discriminant analysis, and density estimation,” Journal of the American Statistical Association, vol. 97, no. 458, pp. 611–631, 2002. View at Publisher · View at Google Scholar · View at Scopus
  94. D. Peel and G. J. McLachlan, “Robust mixture modelling using the t distribution,” Statistics and Computing, vol. 10, no. 4, pp. 339–348, 2000. View at Google Scholar · View at Scopus
  95. S. Lallich, F. Muhlenbach, and D. A. Zighed, “Improving classification by removing or relabeling mislabeled instances,” in Proceedings of the 13th International Symposium on Foundations of Intelligent Systems (ISMIS '02), Lecture Notes in Computer Science, pp. 5–15, 2002.
  96. K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, New York, NY, USA, 1990.
  97. W. J. Krzanowski, T. C. Bailey, D. Partridge, J. E. Fieldsend, R. M. Everson, and V. Schetinin, “Confidence in classification: a bayesian approach,” Journal of Classification, vol. 23, no. 2, pp. 199–220, 2006. View at Publisher · View at Google Scholar · View at Scopus
  98. R. Davis, “Expert Systems: where are we? And where do we go from here?” Massachusetts Institute of Technology, Artificial Intelligence Laboratory, 1982, http://hdl.handle.net/1721.1/5677.
  99. L. Boddy, M. F. Wilkins, and C. W. Morris, “Pattern recognition in flow cytometry,” Cytometry, vol. 44, no. 3, pp. 195–209, 2001. View at Google Scholar · View at Scopus
  100. L. Al-Haddad, C. W. Morris, and L. Boddy, “Training radial basis function neural networks: effects of training set size and imbalanced training sets,” Journal of Microbiological Methods, vol. 43, no. 1, pp. 33–44, 2000. View at Publisher · View at Google Scholar · View at Scopus
  101. C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121–167, 1998. View at Google Scholar · View at Scopus
  102. A. Krogh and J. A. Hertz, “A simple weight decay can improve generalization,” in Advances in Neural Information Processing Systems, 1992. View at Google Scholar
  103. N. Morgan and H. Bourlard, Generalization and Parameter Estimation in Feedforward Nets: Some Experiments, Morgan Kaufmann, San Fransisco, Calif, USA, 1990.
  104. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, Upper Saddle River, NJ, USA, 2008.
  105. L. Prechelt, “Automatic early stopping using cross validation: quantifying the criteria,” Neural Networks, vol. 11, no. 4, pp. 761–767, 1998. View at Publisher · View at Google Scholar · View at Scopus
  106. W. Finnoff, F. Hergert, and H. G. Zimmermann, “Improving model selection by nonconvergent methods,” Neural Networks, vol. 6, pp. 771–783, 1993. View at Google Scholar
  107. E. B. Baum and D. Haussler, “What size net gives valid generalization?” Neural Computation, vol. 1, pp. 151–160, 1989. View at Google Scholar
  108. V. N. Vapnik, Statistical Learning Theory, John Wiley & Sons, New York, NY, USA, 1998.
  109. M. Buhmann, “Radial basis functions,” Acta Numerica, vol. 9, pp. 1–38, 2001. View at Google Scholar
  110. T. K. Ho, J. J. Hull, and S. N. Srihari, “Decision combination in multiple classifier systems,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 1, pp. 66–75, 1994. View at Publisher · View at Google Scholar · View at Scopus
  111. C. Biernacki and G. Govaert, “Choosing models in model-based clustering and discriminant analysis,” Journal of Statistical Computation and Simulation, vol. 64, no. 1, pp. 49–71, 1999. View at Google Scholar · View at Scopus
  112. 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
  113. P. J. Rousseeuw, L. Kaufman, and E. Trauwaert, “Fuzzy clustering using scatter matrices,” Computational Statistics and Data Analysis, vol. 23, no. 1, pp. 135–151, 1996. View at Publisher · View at Google Scholar · View at Scopus
  114. M. Maynadié, F. Picard, B. Husson et al., “Immunophenotypic clustering of myelodysplastic syndromes,” Blood, vol. 100, no. 7, pp. 2349–2356, 2002. View at Publisher · View at Google Scholar · View at Scopus
  115. G. McLachlan and D. Peel, Finite Mixture Models, Wiley-Interscience, New York, NY, USA, 2004.
  116. G. Box and D. Cox, “An analysis of transformations,” Journal of the Royal Statistical Society B, pp. 211–252, 1964. View at Google Scholar
  117. K. L. Lange, “Robust statistical modeling using the t distribution,” Journal of the American Statistical Association, pp. 881–896, 1989. View at Google Scholar