Analysis of clinical flow cytometric immunophenotyping data by clustering on statistical manifolds: Treating flow cytometry data as high-dimensional objects

Cytometry Part B - Clinical Cytometry

Volumn 76, Issue 1, 2009, Pages 1-7

  Finn, William G ^a   Carter, Kevin M ^b   Raich, Raviv ^c   Stoolman, Lyod M ^a   Hero, Alfred O ^b  

^a UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

^c Oregon State University   (United States)

Author keywords

Flow cytometry; Immunophenotype clustering; Immunophenotyping; Information geometry; Statistical manifold

Indexed keywords

ACUTE LYMPHOBLASTIC LEUKEMIA; ADOLESCENT; ADULT; AGED; ARTICLE; CELL EXPANSION; CELL POPULATION; CELL SELECTION; CHILD; CLINICAL PRACTICE; CLUSTER ANALYSIS; CONTROLLED STUDY; ENTROPY; FEMALE; FLOW CYTOMETRY; GEOMETRY; HUMAN; HUMAN CELL; IMMUNOPHENOTYPING; LYMPHOBLAST; MALE; PRE B LYMPHOCYTE; PRIORITY JOURNAL; PROBABILITY; STATISTICAL ANALYSIS;

ADOLESCENT; ADULT; AGED; ALGORITHMS; ANTIGENS, CD; CHILD; CHILD, PRESCHOOL; CLUSTER ANALYSIS; DATA INTERPRETATION, STATISTICAL; FEMALE; FLOW CYTOMETRY; HUMANS; IMMUNOPHENOTYPING; INFANT; MALE; MIDDLE AGED; PRECURSOR CELL LYMPHOBLASTIC LEUKEMIA-LYMPHOMA; PRECURSOR CELLS, B-LYMPHOID; YOUNG ADULT;

EID: 62349137000     PISSN: 15524949     EISSN: 15524957     Source Type: Journal    
DOI: 10.1002/cyto.b.20435     Document Type: Article

References (23)

1. Valet GK, Hoffkes HG. Automated classi.cation of patients with chronic lymphocytic leukemia and immunocytoma from flow cytometric three-color immunophenotypes. Cytometry 1997;30:275-288.
2. Zamir E, Geiger B, Cohen N, Kam Z, Katz BZ. Resolving and classifying haematopoietic bone-marrow cell populations by multi-dimensional analysis of flow-cytometry data. Br J Haematol 2005;129:420-431.
3. Collins GS, Krzanowski WJ. Nonparametric discriminant analysis of phytoplankton species using data from analytical flow cytometry. Cytometry 2002;48:26-33.
4. Boddy L, Wilkins MF, Morris CW. Pattern recognition in flow cytometry. Cytometry 2001;44:195-209.
5. Toedling J, Rhein P, Ratei R, Karawajew L, Spang R. Automated insilico detection of cell populations in flow cytometry readouts and its application to leukemia disease monitoring. BMC Bioinformatics 2006;7:282.
6. Zeng QT, Pratt JP, Pak J, Ravnic D, Huss H, Mentzer SJ. Feature-guided clustering of multi-dimensional flow cytometry datasets. Biomed Inform 2007;40:325-331.
7. Roederer M, Hardy RR. Frequency difference gating: A multivariate method for identifying subsets that differ between samples. Cytometry 2001;45:56-64.
8. Pedreira CE, Costa ES, Arroyo ME, Almeida J, Orfao A. A multidimensional classification approach for the automated analysis of flow cytometry data. IEEE Trans Biomed Eng 2008;55:1155-1162.
9. Perez OD, Nolan GP. Phospho-proteomic immune analysis by flow cytometry: From mechanism to translational medicine at the single-cell level. Immunol Rev 2006;210:208-228.
10. Law M. Manifold Learning (Web Page). 2008. Available at: http://www.cse.msu.edu/~lawhiu/manifold/. Accessed January 25, 2008.
11. Lee S, Abbott AL, Clark N, Araman P. Active contours on statistical manifolds and texture segmentation. In the IEEE International Conference on Image Processing, IEEE; Genoa, Italy: 2005. pp 828-831.
12. Carter KM, Raich R, Hero AO. FINE: Information embedding for document classification. In the Proceedings of the 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing, Las Vegas, NV, IEEE; 2008. pp 1861-1864.
13. Amari S, Nagoaka H. Differential-Geometrical Methods in Statistics. New York: Springer; 1990.
14. Carter KM, Raich R, Hero AO. Learning on statistical manifolds for clustering and visualization. In 45th Allerton Conference on Communication, Control, and Computing, Monticello, Illinois; 2007.
15. McKenna RW, Washington LT, Aquino DB, Picker LJ, Kroft SH. Immunophenotypic analysis of hematogones (B-lymphocyte precursors) in 662 consecutive bone marrow specimens by 4-color flow cytometry. Blood 2001;98:2498-2507.
16. Brunning RD, Borowitz M, Matutes E, Head D, Flandrin G, Swerdlow SH, Bennett JM. Precursor B lymphoblastic leukaemia/lymphoblastic lymphoma. In: Jaffe ES, Harris NL, Stein H, Vardiman JW, editors. World Health Organization Classification of Tumours: Pathology & Genetics: Tumours of the Haematopoietic and Lymphoid Tissues. Lyon: IARC Press; 2001. pp 111-114.
17. Terrell GR. The maximal smoothing principle in density estimation. J Am Stat Assoc 1990;85:470-477.
18. Balis UJ. Implementation of a region of interest-based query using vector quantization, generalized af.ne class-based vocabularies, and multimodal Chebyshev polynomial normalization to retrieve context-matched imagery from existing digital image repositories (abstract). Arch Pathol Lab Med 2005;129:811.
19. Habib LK, Finn WG. Unsupervised immunophenotypic profiling of chronic lymphocytic leukemia. Cytometry B Clin Cytom 2006;70B:124-135.
20. 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: 1557-1565.
21. Maynadie M, Picard F, Husson B, Chatelain B, Cornet Y, Le Roux G, Campos L, Dromelet A, Lepelley P, Jouault H, Imbert M, Rosenwadj M, Verge V, Bissieres P, Raphael M, Bene MC, Feuillard J. Immunophenotypic clustering of myelodysplastic syndromes. Blood 2002;100:2349-2356.
22. De Rosa SC, Brenchley JM, Roederer M. Beyond six colors: A new era in flow cytometry. Nat Med 2003;9:112-117.
23. Roederer M, Moore W, Treister A, Hardy RR, Herzenberg LA. Probability binning comparison: A metric for quantitating multivariate distribution differences. Cytometry 2001;45:47-55.