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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

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