Table of Contents Author Guidelines Submit a Manuscript
Advances in Bioinformatics
Volume 2009, Article ID 235320, 9 pages
http://dx.doi.org/10.1155/2009/235320
Research Article

Tree-Based Methods for Discovery of Association between Flow Cytometry Data and Clinical Endpoints

1Division of Biostatistics, University of Massachusetts, Amherst, MA 01003, USA
2Immunology Program, Wistar Institute, Philadelphia, PA 19104, USA
3Clinical HIV Research Unit, University of Witwatersrand, Johannesburg, South Africa
4Department of Hematology and Molecular Medicine, National Health Laboratory Service and University of Witwatersrand, Johannesburg, South Africa

Received 19 May 2009; Revised 14 August 2009; Accepted 12 October 2009

Academic Editor: George Luta

Copyright © 2009 M. Eliot 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. L. Breiman, J. Friedman, C. Stone, and R. A. Olshen, Classiffication and Regression Trees, Chapman & Hall/CRC, Boca Raton, Fla, USA, 1984.
  2. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Scopus
  3. C. Kooperberg, I. Ruczinski, M. L. LeBlanc, and L. Hsu, “Sequence analysis using logic regression,” Genetic Epidemiology, vol. 21, supplement 1, pp. S626–S631, 2001. View at Google Scholar · View at Scopus
  4. I. Ruczinski, C. Kooperberg, and M. LeBlanc, “Logic regression,” Journal of Computational and Graphical Statistics, vol. 12, no. 3, pp. 475–511, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. M. R. Segal, J. D. Barbour, and R. M. Grant, “Relating HIV-1 sequence variation to replication capacity via trees and forests,” Statistical Applications in Genetics and Molecular Biology, vol. 3, no. 1, article 7, 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Bureau, J. Dupuis, K. Falls et al., “Identifying SNPs predictive of phenotype using random forests,” Genetic Epidemiology, vol. 28, no. 2, pp. 171–182, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. C. Kooperberg and I. Ruczinski, “Identifying interacting SNPs using Monte Carlo logic regression,” Genetic Epidemiology, vol. 28, no. 2, pp. 157–170, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. C. Kooperberg, J. C. Bis, K. D. Marciante, S. R. Heckbert, T. Lumley, and B. M. Psaty, “Logic regression for analysis of the association between genetic variation in the renin-angiotensin system and myocardial infarction or stroke,” American Journal of Epidemiology, vol. 165, no. 3, pp. 334–343, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. H. Schwender and K. Ickstadt, “Identification of SNP interactions using logic regression,” Biostatistics, vol. 9, no. 1, pp. 187–198, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. K. Ickstadt, M. Schäfer, A. Fritsch et al., “Statistical methods for detecting genetic interactions: a head and neck squamous-cell cancer study,” Journal of Toxicology and Environmental Health, Part A, vol. 71, no. 11-12, pp. 803–815, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. M. García-Magariños, I. López-de-Ullibarri, R. Cao, and A. Salas, “Evaluating the ability of tree-based methods and logistic regression for the detection of SNP-SNP interaction,” Annals of Human Genetics, vol. 73, no. 3, pp. 360–369, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Foulkes, Applied Statistical Genetics with R for Population-Based Association Studies, Springer, Berlin, Germany, 2009.
  13. 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
  14. 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 Publisher · View at Google Scholar · View at Scopus
  15. V. Ganju, R. B. Jenkins, J. R. O'Fallon et al., “Prognostic factors in gliomas: a multivariate analysis of clinical, pathologic, flow cytometric, cytogenetic, and molecular markers,” Cancer, vol. 74, no. 3, pp. 920–927, 1994. View at Publisher · View at Google Scholar · View at Scopus
  16. D. K. Glencross, G. Janossy, L. M. Coetzee et al., “Large-scale affordable PanLeucogated CD4+ testing with proactive internal and external quality assessment: in support of the South African national comprehensive care, treatment and management programme for HIV and AIDS,” Cytometry Part B, vol. 74, supplement 1, pp. S40–S51, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. L. E. Harrington, R. D. Hatton, P. R. Mangan et al., “Interleukin 17-producing CD4+ effector T cells develop via a lineage distinct from the T helper type 1 and 2 lineages,” Nature Immunology, vol. 6, no. 11, pp. 1123–1132, 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. J. D. Storey, “A direct approach to false discovery rates,” Journal of the Royal Statistical Society, Series B, vol. 64, no. 3, pp. 479–498, 2002. View at Publisher · View at Google Scholar · View at Scopus
  19. J. D. Storey, “The positive false discovery rate: a Bayesian interpretation and the q-value,” The Annals of Statistics, vol. 31, no. 6, pp. 2013–2035, 2003. View at Publisher · View at Google Scholar · View at Scopus
  20. M. J. van der Laan, “Statistical inference for variable importance,” The International Journal of Biostatistics, vol. 2, no. 1, article 2, 2006. View at Google Scholar
  21. C. Strobl, A.-L. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis, “Conditional variable importance for random forests,” BMC Bioinformatics, vol. 9, article 307, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. Y. Benjamini and D. Yekutieli, “The control of the false discovery rate in multiple testing under dependency,” The Annals of Statistics, vol. 29, no. 4, pp. 1165–1188, 2001. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Chehimi, L. Azzoni, M. Farabaugh et al., “Baseline viral load and immune activation determine the extent of reconstitution of innate immune effectors in HIV-1-infected subjects undergoing antiretroviral treatment,” The Journal of Immunology, vol. 179, no. 4, pp. 2642–2650, 2007. View at Google Scholar · View at Scopus
  24. D. Nash, M. Katyal, M. W. G. Brinkhof et al., “Long-term immunologic response to antiretroviral therapy in low-income countries: a collaborative analysis of prospective studies,” AIDS, vol. 22, no. 17, pp. 2291–2302, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. S. R. Søndergaard, H. Aladdin, H. Ullum, J. Gerstoft, P. Skinhøj, and B. K. Pedersen, “Immune function and phenotype before and after highly active antiretroviral therapy,” Journal of Acquired Immune Deficiency Syndromes, vol. 21, no. 5, pp. 376–383, 1999. View at Google Scholar · View at Scopus
  26. J. Chehimi, D. E. Campbell, L. Azzoni et al., “Persistent decreases in blood plasmacytoid dendritic cell number and function despite effective highly active antiretroviral therapy and increased blood myeloid dendritic cells in HIV-infected individuals,” The Journal of Immunology, vol. 168, no. 9, pp. 4796–4801, 2002. View at Google Scholar · View at Scopus
  27. A. S. Foulkes, V. De Gruttola, and K. Hertogs, “Combining genotype groups and recursive partitioning: an application to human immunodeficiency virus type 1 genetics data,” Journal of the Royal Statistical Society, Series C, vol. 53, no. 2, pp. 311–323, 2004. View at Publisher · View at Google Scholar · View at Scopus