Combined multiple clusterings on flow cytometry data to automatically identify chronic lymphocytic leukemia

You-Wen Qian, William Cukierski, Mona Osman, Lauri Goodell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

We described a combined multiple clustering approach to automatically identify chronic lymphocytic leukemia neoplastic population by flow cytometry immunophenotyping. Flow cytometry data from various specimens were preprocessed by data cross-linking and subset selection before undergoing subspace and consensus clustering. This approach was implemented as a Server-side application, with results comparable to those performed by manual gating on commercial software.

Original languageEnglish
Title of host publicationICBBT 2010 - 2010 International Conference on Bioinformatics and Biomedical Technology
Pages305-309
Number of pages5
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 International Conference on Bioinformatics and Biomedical Technology, ICBBT 2010 - Chengdu, China
Duration: Apr 16 2010Apr 18 2010

Other

Other2010 International Conference on Bioinformatics and Biomedical Technology, ICBBT 2010
CountryChina
CityChengdu
Period4/16/104/18/10

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Keywords

  • Chronic lymphocytic leukemia
  • Clustering
  • Flow cytometry

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Qian, Y-W., Cukierski, W., Osman, M., & Goodell, L. (2010). Combined multiple clusterings on flow cytometry data to automatically identify chronic lymphocytic leukemia. In ICBBT 2010 - 2010 International Conference on Bioinformatics and Biomedical Technology (pp. 305-309). [5478955] https://doi.org/10.1109/ICBBT.2010.5478955