Automated in-silico detection of cell populations in flow cytometry readouts and its application to leukemia disease monitoring

BMC Bioinformatics

Volumn 7, Issue , 2006, Pages

  Toedling, Joern ^a,c   Rhein, Peter ^b   Ratel, Richard ^b   Karawajew, Leonard ^b   Spang, Rainer ^a  

^a MAX PLANCK INSTITUTE FOR MOLECULAR GENETICS   (Germany)

^b Section of Pathology   (Germany)

^c EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ACUTE LYMPHOBLASTIC LEUKEMIA; DETECTION ACCURACY; DISEASE MONITORING; INTRACELLULAR PROTEINS; MULTIVARIATE CLASSIFICATION; RELIABLE DETECTION; SVM CLASSIFICATION; TWO-DIMENSIONAL PROJECTION;

CELL CULTURE; CELL PROLIFERATION; CELLS; DISEASES; FLOW CYTOMETRY; PATIENT TREATMENT; SUPPORT VECTOR MACHINES;

CLASSIFICATION (OF INFORMATION);

CELL PROTEIN; CELL SURFACE PROTEIN; GENETIC MARKER;

ACUTE LYMPHOBLASTIC LEUKEMIA; ALGORITHM; ARTICLE; AUTOMATION; B CELL LEUKEMIA; BLOOD SAMPLING; CELL POPULATION; CELL SIZE; CHILD; CHILDHOOD LEUKEMIA; CLASSIFICATION; CLINICAL ARTICLE; COMPUTER MODEL; CONTROLLED STUDY; COST CONTROL; DIAGNOSTIC ACCURACY; FLOW CYTOMETRY; GRANULAR CELL; HUMAN; HUMAN CELL; LEUKEMIA CELL; MATHEMATICAL COMPUTING; MULTIVARIATE ANALYSIS; PRE B LYMPHOCYTE; PROTEIN EXPRESSION; RELIABILITY; SENSITIVITY AND SPECIFICITY; TIME; BIOLOGY; BLOOD; CHROMOSOME ANALYSIS; COMPUTER; COMPUTER LANGUAGE; GENETIC MARKER; IMMUNOPHENOTYPING; LEUKEMIA; PROCEDURES; PROGNOSIS; STATISTICAL ANALYSIS;

COMPUTATIONAL BIOLOGY; COMPUTERS; CYTOGENETIC ANALYSIS; DATA INTERPRETATION, STATISTICAL; FLOW CYTOMETRY; GENETIC MARKERS; HUMANS; IMMUNOPHENOTYPING; LEUKEMIA; MULTIVARIATE ANALYSIS; PROGNOSIS; PROGRAMMING LANGUAGES; SENSITIVITY AND SPECIFICITY;

EID: 33745948907     PISSN: 14712105     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-7-282     Document Type: Article

References (22)

1. Ratei R, Ludwig WD: Flow-Cytometric Methods for the Detection of Residual Leukemia. In Leukemia and Lymphoma: Detection of Minimal Residual Disease. Edited by: Zipf T, Johnston D, Totowa, NJ. USA: Humana Press Inc; 2003:1-19.
2. Sun T, Sangaline R, Ryder J, Gibbens K, Rollo C, Stewart S, Rajagopalan C: Gating strategy for immunophenotyping of leukemia and lymphoma. Am J Clin Pathol 1997, 108(2):152-157. [PubMed Abstract]
3. Campana D, Coustan-Smith E: Detection of minimal residual disease in acute leukemia by flow cytometry. Cytometry 1999, 38(4):139-152. [PubMed Abstract] [Publisher Full Text]
4. Valet G, Valet M, Tschope D, Gabriel H, Rothe G, Kellermann W, Kahle H: White cell and thrombocyte disorders. Standardized, self-learning flow cytometric list mode data classification with the CLASSIF1 program system. Ann N Y Acad Sci 1993, 677:233-251. [PubMed Abstract]
5. De Zen L, Bicciato S, te Kronnie G, Basso G: Computational analysis of flow-cytometry antigen expression profiles in childhood acute lymphoblastic leukemia: an MLL/AF4 identification. Leukemia 2003, 17(8):1557-1565. [PubMed Abstract] [Publisher Full Text]
6. Mardia K, Kent J, Bibby J: Multivariate Analysis. San Diego: Academic Press; 1979.
7. Roederer M, Hardy RR: Frequency difference gating: a multivariate method for identifying subsets that differ between samples. Cytometry 2001, 45:56-64. [PubMed Abstract] [Publisher Full Text]
8. Schölkopf B, Smola AJ: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. Cambridge, Massachusetts: The MIT Press; 2002.
9. Vapnik VN: The nature of statistical learning theory. New York, USA: Springer; 1995.
10. Vapnik VN: Statistical Learning Theory. New York, USA: John Wiley & Sons; 1998.
11. Bennet KP, Campbell C: Support Vector Machines: Hype or Hallelujah? SIGKDD Explorations 2000, 2(2):1-13.
12. Boser BE, Guyon IM, Vapnik VN: A training algorithm for optimal margin classifiers. In Proceedings of the 5th annual ACM workshop on Computational Learning Theory. ACM Press; 1992:144-152.
13. Hastie T, Tibshirani R, Friedman J: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer-Verlag; 2001.
14. Quinlan JR: The effect of noise on concept learning. In Machine Learning, an Artificial Intelligence Approach. Volume 2. Edited by: Michalski RS, Carbonell JG, Mitchell TM, San Mateo. USA: Morgan Kaufmann; 1986:149-166.
15. Holmström L, Koistinen P: Using additive noise in back-propagation training. IEEE Transactions on Neural Networks 1992, 3:24-38.
16. Loken MR, Shah VO, Dattilio KL, Civin CI: Flow cytometric analysis of human bone marrow. II. Normal B lymphocyte development. Blood 1987, 70(5):1316-1324. [PubMed Abstract]
17. Shapiro H: Practical Flow Cytometry. 4th edition. New York, USA: Wiley-Lyss; 2003.
18. Fritsch G, Stimpfl M, Kurz M, Leitner A, Printz D, Buchinger P, Hoecker P, Gadner H: characterization of hematopoietic stem cells. Ann N Y Acad Sci 1995, 770:42-52. [PubMed Abstract]
19. Owens MA, Vall HG, Hurley AA, Wormsley SB: Validation and quality control in clinical flow cytometry. J Immunol Methods 2000, 243:33-50. [PubMed Abstract] [Publisher Full Text]
20. Seamer LC, Bagwell CB, Barden L, Redelman D, Salzman GC, Wood JC, Murphy RF: Proposed new data file standard for flow cytometry, version FCS 3.0. Cytometry 1997, 28(2):118-122. [PubMed Abstract] [Publisher Full Text]
21. R Development Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing Vienna Austria 2004. [ISBN 3-900051-00-3].
22. Meyer D: Support Vector Machines. [http://CRAN.R-project.org/doc/Rnews/]R News 2001, 1(3):23-26.