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

Author

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

Author_Institution

Dept. of Pathology & Lab. Med., Univ. of Med. & Dentistry of New Jersey, New Brunswick, NJ, USA

fYear

2010

fDate

16-18 April 2010

Firstpage

305

Lastpage

309

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.

Keywords

bioinformatics; biological fluid dynamics; biological techniques; cancer; cellular biophysics; patient diagnosis; pattern clustering; Server-side application; chronic lymphocytic leukemia; combined multiple clusterings; data cross-linking; flow cytometry data; flow cytometry immunophenotyping; neoplastic population; subset selection; Blood; Bone diseases; Clustering algorithms; Data analysis; Dentistry; Immune system; Iterative algorithms; Laboratories; Partitioning algorithms; Pathology; chronic lymphocytic leukemia; clustering; flow cytometry;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedical Technology (ICBBT), 2010 International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-6775-4

Type

conf

DOI

10.1109/ICBBT.2010.5478955

Filename

5478955