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References

  1. DC Walley, BW Tripp, YC Song, KR Walley, SJ Tebbutt. MACGT: Multi-dimensional automated clustering genotyping tool for analysis of microarray-based mini-sequencing. Data Bioinformatics 2006 May 1;22(9):1147-9.
  2. M Roederer, A Treister, W Moore, LA Herzenberg. Probability binning comparison: A metric for quantitating univariate distribution differences. Cytometry 2001 Sep 1;45(1):37-46.
  3. Rafael Nunez, Associate Laboratory Member, Memorial Sloan-Kettering Cancer Center. Introduction to the Field of Cytometry and Its Importance in Biomedicine. Curr. Issues Mol. Biol. 2001;3(2):37-38.
  4. PGP Corp., 200 Jefferson Drive, Menlo Park, CA, 94025, United States Phone: 650-319-9000.
  5. Philippe Kruchten. The rational unified process: an introduction. Addison-Wesley, 2004.
  6. J Paul Robinson. Multispectral cytometry: the next generation. Biophotonics International October 2004;36-39.
  7. MA Suni, HS Dunn, PL Orr, R de Laat, E Sinclair, SA Ghanekar, BM Bredt, JF Dunne, VC Maino, HT Maecker. Performance of plate-based cytokine flow cytometry with automated data analysis. BMC Immunology 2003;4:9.
  8. LC Seamer, CB Bagwell, L Barden, D Redelman, GC Salzman, JCS Wood, RF Murphy. Proposed new data file standard for flow cytometry, version FCS 3.0. Cytometry 1997;28(2):118-22.
  9. Ryan Brinkman, British Columbia Cancer Agency; Robert Gentleman, Fred Hutchinson Cancer Research Center; Raphael Gottardo, Clinical Research Institute of Montréal; Richard H. Scheuermann, University of Texas Southwestern Medical Center; and Jill Schoenfeld, Tree Star, Inc. (Organizing Committee). Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) e-mail: flowcap@flowsite.org
  10. Flow Informatics and Computational Cytometry Society (FICCS) connects people sharing an interest in new software tools, methods, and standards for flow cytometry. Members of the society represent a cross-disciplinary international collaborative group of bioinformaticists, computational statisticians, software and hardware developers, and clinician scientists; members are from academia and industry.
  11. JO Ramsay, Giles Hooker, Spencer Graves. Functional data analysis with R and Matlab use. R. Springer, 2009 ISBN: 978-0-387-98184-0.
  12. Ding Zhou, Jia Li, Hongyuan Zha. A new Mallows distance based metric for comparing clusterings. Proceedings of the 22nd International Conference on Machine Learning Bonn, Germany: 2005 (ICML 2005).
  13. Andrew Rosenberg, Julia Hirschberg. V-measure: a conditional entropy-based external cluster evaluation measure. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning Prague: June 2007; 410–420.
  14. John Quinn, Paul W Fisher, Renold J Capocasale, Ram Achuthanandam, Moshe Kam, Peter J Bugelski, Leonid Hrebien. A statistical pattern recognition approach for determining cellular viability and lineage phenotype in cultured cells and murine bone marrow. Cytometry 2007;71A:612-624.
  15. Paolo Sonego, András Kocsor, Sándor Pongor. ROC analysis: applications to the classification of biological sequences and 3D structures. Briefings in Bioinformatics Oxford University Press, Jan. 18, 2008.
  16. The International Centre for Genetic Engineering and Biotechnology (ICGEB). This organization conducts advanced research and training in molecular biology and biotechnology, particularly in the fields of biomedicine, crop improvement, environmental protection/remediation, biopharmaceuticals, and biopesticide production. The Centre forms an interactive network with Affiliated Centres in ICGEB Member States and has components in Trieste, Italy; New Delhi, India; and Cape Town, South Africa. ICGEB is part of the United Nations System.
  17. John Quinn, Ph.D. private communication, July 24, 2009.
  18. A Kertész-Farkas, S Dhir, P Sonego, M Pacurar, S Netoteia, H Nijveen, A Kuzniar, JA Leunissen, A Kocsor, S Pongor. Benchmarking protein classification algorithms via supervised cross-validation. J Biochem Biophys Methods 2008 Apr 24;70(6):1215-23. e-pub 2007 May 31.
  19. H Maecker, A Rinfret, P D'Souza, J Darden, E Roig, C Landry, P Hayes, J Birungi, O Anzala, M Garcia, A Harari, I Frank, R Baydo, M Baker, J Holbrook, J Ottinger, L Lamoreaux, CL Epling, E Sinclair, MA Suni, K Punt, S Calarota, S El-Bahi, G Alter, H Maila, E Kuta, J Cox, C Gray, M Altfeld, N Nougarede, J Boyer, L Tussey, T Tobery, B Bredt, M Roederer, R Koup, VC Maino, K Weinhold, G Pantaleo, J Gilmour, H Horton, RP Sekaly. Standardization of cytokine flow cytometry essays. BMCImmunol 2005;6:13.
  20. JF Leary. Subtractive Clustering for Use in Analysis of Data. (data-mining algorithms and related software) U.S. Patent 7,043,500; 2006
  21. M Smith. Neural Networks for Statistical Modeling. New York: Van Nostrand Reinhold, 1993.
  22. DR Parks, M Roederer, WA Moore. A new "logicle" display method avoids deceptive effects of logarithmic scaling for low signals and compensated data. Cytometry 2006;69A:541–551.
  23. A Treister, D Briggs. Flow cytometry markup language: a proposal for structured representation of experimental metadata. Abstract Number 6889: Emerging Technologies. Informatics ISAC XX.
  24. GT Stelzer, KE Shults, MR Loken. CD45 gating for routine flow cytometric analysis of human bone marrow specimens. Ann NY Acad Sci March 1993;677:265-80.
  25. Flow Cytometry Consultation. Yale-New Haven Hospital Laboratory Manual, 1998-2005. Department of Laboratory Medicine, Yale Medical School.
  26. N Baumgarth, M Roederer. A practical approach to multicolor flow cytometry for immunophenotyping. Journal of Immunological Methods 2000;243:77–97.
  27. C Kamath. Mining science and engineering data: an overview. In: handbook of data mining. Nong Ye, editor. 549-572. New Jersey: Lawrence Erlbaum Associates, 2003. UCRL-JC-147018, February 2002.
  28. JF Leary, SR McLaughlin, LM Reece. Application of a novel data
    mining technique to cytometry data. Proc. of SPIE 2000;392:84-89.
  29. L Boddy, CW Morris, MF Wilkins, GA Tarran, PH Burkill. Neural network analysis of flow cytometric data for 40 marine phytoplankton species. Cytometry 1994 Apr 1;15(4):283-93.
  30. J Quinn, R Achuthananandam, R Capocasale, P Fisher, P Bugelski, M Kam, L Hrebien. Development of a statistical pattern recognition approach for analysis of flow cytometric data. Poster presented at the International Society of Analytical Cytology (ISAC), Quebec, May 2006.
  31. R Baraglia, D Laforenza, S Orlando, P Palmerini, R Perego. Design of efficient input/output intensive data mining applications. ERCIM News 2001; 44.
  32. IH Witten, E Frank. Data mining: practical machine learning tools and techniques, second edition. San Francisco: Morgan Kaufman Publishers, 2005.
  33. D Pe’er. Bayesian network analysis of signaling networks: a primer. Science STKE 2005;281:l4.
  34. D Woods, G Guliani. Open source for the enterprise: managing risks reaping rewards Sebastopol, CA: O'Reilly Media, 2005.
  35. M Simm, A Treister. Using the polyvariate plot tool. Poster presented at the International Society of Analytical Cytology (ISAC), Quebec, May 2006.
  36. A Treister, M Simm, T Franks. Automating large-scale, high-throughput, repetitive clinical analyses with FlowDx. Poster presented at the International Society of Analytical Cytology (ISAC). Quebec, May 2006.
  37. J Toedling, P Rhein, R Ratei, L Karawajew, R Spang. Automated in-silico detection of cell populations in flow cytometry readouts and its application to leukemia disease monitoring. BMC Bioinformatics 2006;7:282.
  38. J Handl, J Knowles, DB Kell. Computational cluster validation in post-genomic data analysis. Bioinformatics Advance Access May 24, 2005.
  39. KY Yeung, WL Ruzzo. Details of the adjusted rand index and clustering algorithms. Bioinformatics 2001;17(4):309-18.
  40. Marina Meilă. Comparing clusterings. University of Washington Technical Report October, 2002;418.
  41. Mario Roederer, private communication.
  42. J Quinn. PW Fisher, RJ Capocasale,R Achuthanandam, M Kam, PJ Bugelski, L Hrebien. A statistical pattern recognition approach for determining cellular viability and lineage phenotype in cultured cells and murine bone marrow. Cytometry 2007;71A(8):612-24.        
  43. General principles of software validation. Final guidance for industry and FDA Staff. http://www.fda.gov/cdrh/comp/guidance/938.html .
  44. V Pareto. Manual of Political Economy. 1971 translation of 1927 edition.
    Augustus M. Kelley, 1971.
  45. R Brinkman. High-content flow cytometry and temporal data analysis for defining a cellular signature of graft-versus-host disease. Biology of Blood and Marrow Transplantation 2007;13:691-700.
  46. U Naumann, MP Wand. Automation in high-content flow cytometry screening. http://www.uow.edu.au/%7Emwand/banpap.pdf .
  47. Lee JA, Spidlen J, Boyce K, Cai J, Crosbie N, Dalphin M, Furlong J, Gasparetto M, Goldberg M, Goralczyk EM, Hyun B, Jansen K, Kollmann T, Kong M, Leif R, McWeeney S, Moloshok TD, Moore W, Nolan G, Nolan J, Nikolich-Zugich J,Parrish D, Purcell B, Qian Y, Selvaraj B, Smith C, Tchuvatkina O, Wertheimer A, Wilkinson P, Wilson C, Wood J, Zigon R; International Society for Advancement of Cytometry Data Standards Task Force, Scheuermann RH, Brinkman RR. MIFlowCyt: The minimum information about a flow cytometry experiment. International Society for Advancement of Cytometry Data Standards Task Force Cytometry 2008;73A(10):926-30.
  48. Andrew Rosenberg, Julia Hirschberg. V-measure: A conditional entropy-based external cluster evaluation measure. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) 2007;410–420.
  49. Ding Zhou, Jia Li, Hongyuan Zha. A new mallows distance based metric for comparing clusterings. Proc. 22nd Internal Conference on Machine Learning 2005;1–8.
  50. Greg Finak, Raphal Gottardo. Estimation of misclassification rates of automated gating algorithms against a panel of experts’ manual gates. unpublished.
  51. Kenneth Lo, Florian Hahne, Ryan Brinkman, Rafael Gottardo. Flowclust: a bioconductor package for automated gating of flow cytometry data. BMC Bioinformatics 2009;10(1):145.
  52. Nima Aghaeepour, Alireza Hadj Khodabakhshi, Ryan Brinkman. External cluster evaluation for flow cytometry, 2009. pending publication.
  53. Nima Aghaeepour, Alireza Hadj Khodabakhshi, Ryan Brinkman. Empirical study of v-measure and misclassification rate for automated gating algorithms. Accepted for presentation at NIPS 2009 Workshop, December 11, 2009. http://nips.cc/Conferences/2009/CallForWorkshops
  54. Nima Aghaeepour, Alireza Hadj Khodabakhshi, Ryan Brinkman. An empirical study of cluster evaluation metrics using flow cytometry data. unpublished.
  55. U.S. Flow Cytometry Markets. March 5, 2008. Frost & Sullivan web site. (subscription required) (accessed December 18, 2008).
  56. BH Littman, L Di Mario, M Plebani, FM Marincola. What's next in translational medicine? Clin Sci (London) 2007;112(4):217-27.
  57. Hahne F, LeMeur N, Brinkman RR, Ellis B, Haaland P, Sarkar D, Spidlen J, Strain E, Gentleman R. flowcore: a bioconductor package for high throughput flow cytometry, BMC Bioinformatics 2009;10:106.
  58. FlowDx Project Plan, Database Architectural Plan, http://flowdx.com/dbinfrastructure.html
  59. Immune Tolerance Network. http://www.immunetolerance.org/professional
  60. FlowDx Project Plan. Magnetic Gating Description & Report http://flowdx.com/magnetic.html
  61. Per Kroll. The RUP: An industry-wide platform for best practices. IBM Technical Library http://www.ibm.com/developerworks/rational/library/873.html
  62. FlowDx Project Plan. Initial Operational Capability http://flowdx.com/initialoperation.html
  63. J Schoenfeld. Quantitative evaluation of flow cytometry gating algorithms. Talk presented at Flow Informatics and Computational Cytometry Society (FICCS) Annual Meeting 2009, Seattle, WA  http://www.ficcs.org/meetings/ficcs5/ficcs5.html
  64. Personal correspondence with Howard Shapiro
  65. AgileUP. http://www.ambysoft.com/unifiedprocess/agileUP.html
  66. MySQL http://www.mysql.com/
  67. Apache Web server http://www.apache.org
  68. Tomcat Application Server http://tomcat.apache.org
  69. TCP server application http://www.tcpipguide.com/free/t_TCPCommonApplicationsandServerPortAssignments.htm
  70. Java Language http://www.java.com/en/about/
  71. Linux http://www.linux.org/
  72. FDA's 21 CFR Part 11 Rule on electronic records and signatures www.21cfrpart11.com
  73. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) http://www.hhs.gov/ocr/privacy/
  74. Health Level 7 Regulation (HL7) http://www.hl7.org/about/index.cfm  
  75. Cytobank http://www.ficcs.org/meetings/ficcs3/presentations/NikeshKotecha-cytobank.pdf
  76. Eclipse http://www.eclipse.org/
  77. Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) http://flowcyt.sourceforge.net/miflowcyt/
  78. Analytical Cytometry Standard (ACS) http://flowcyt.sourceforge.net/acs/
  79. Gating ML  http://flowcyt.sourceforge.net/gating/latest.pdf September 2009
  80. R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. www.R-project.org.
  81. Le Meur, L, Rossini, A., Gasparetto, M., Smith, C., Brinkman, R.R. & Gentleman, R. Data quality assessment of ungated flow cytometry data in high throughput experiments. Cytometry Part A(2007);71A, 393–403.
  82. Gentleman, R., Carey, V.J., Bates, D.M. & others. Bioconductor: Open software development for computational biology and bioinformatics. Genome Biology (2004);5, R80.