Original Article

You have full text access to this OnlineOpen article

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

  1. William G. Finn1,*,
  2. Kevin M. Carter2,
  3. Raviv Raich3,
  4. Lloyd M. Stoolman1,
  5. Alfred O. Hero2

Article first published online: 18 JUL 2008

DOI: 10.1002/cyto.b.20435

Issue

Cytometry Part B: Clinical Cytometry

Cytometry Part B: Clinical Cytometry

Volume 76B, Issue 1, pages 1–7, January 2009

Additional Information

How to Cite

Finn, W. G., Carter, K. M., Raich, R., Stoolman, L. M. and Hero, A. O. (2009), Analysis of clinical flow cytometric immunophenotyping data by clustering on statistical manifolds: Treating flow cytometry data as high-dimensional objects. Cytometry, 76B: 1–7. doi: 10.1002/cyto.b.20435

Author Information

  1. 1

    Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109

  2. 2

    Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109

  3. 3

    School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331

*Department of Pathology, University of Michigan, 1301 Catherine Road, Room M5242, Ann Arbor, MI 48109-0602

  1. †

    How to cite this article: Finn WG, Carter KM, Raich R, Stoolman LM, Hero AO. Analysis of clinical flow cytometric immunophenotyping data by clustering on statistical manifolds: Treating flow cytometry data as high-dimensional objects. Cytometry Part B 2009; 76B: 1–7.

Publication History

  1. Issue published online: 9 DEC 2008
  2. Article first published online: 18 JUL 2008
  3. Manuscript Accepted: 27 MAY 2008
  4. Manuscript Received: 13 FEB 2008

Funded by

  • National Science Foundation. Grant Number: CCR-0325571

SEARCH

SEARCH BY CITATION

ARTICLE TOOLS

View Full Article (HTML) Get PDF (326K)

LITERATURE CITED

  • 1
    Valet GK,Hoffkes HG. Automated classification of patients with chronic lymphocytic leukemia and immunocytoma from flow cytometric three-color immunophenotypes. Cytometry 1997; 30: 275–288.
    Direct Link:
  • 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.
    Direct Link:
  • 3
    Collins GS,Krzanowski WJ. Nonparametric discriminant analysis of phytoplankton species using data from analytical flow cytometry. Cytometry 2002; 48: 26–33.
    Direct Link:
  • 4
    Boddy L,Wilkins MF,Morris CW. Pattern recognition in flow cytometry. Cytometry 2001; 44: 195–209.
    Direct Link:
  • 5
    Toedling J,Rhein P,Ratei R,Karawajew L,Spang R. Automated in-silico 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. J 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.
    Direct Link:
  • 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.
    Direct Link:
  • 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: JaffeES, HarrisNL, SteinH, VardimanJW, 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 affine 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.
    Direct Link:
  • 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.
    Direct Link:
View Full Article (HTML) Get PDF (326K)