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Proceedings Paper

Algorithm for the discovery of clusters in WBC data
Author(s): Shu-Jen Yeh; Eric L. Russell
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Paper Abstract

Analysis of white blood cell flow cytometry light scatter data remains a challenging problem. Conventional methods for analyzing flow cytometry data use 2D scatterplots of multidimensional data. We have developed an automated method to locate and characterize clusters within WBC data. Our method uses the full dimensionality of the data and employs the recursive application of a two-step algorithm. Input to the first step is a dataset of cellular events divided into two assumed clusters. A clustering algorithm iteratively refines these initial clusters. This algorithm is based upon an extension of the k-means clustering algorithm. If two populations are confirmed, the data for each cluster are passed, in turn, to the second step, a splitting algorithm. This algorithm determines the potential for further subdivision of the data. When this potential exists, an approximate division of the data is made. These new subclusters are passed as input to step one and the process is repeated. The process terminates when either the clustering algorithm converges to a single population or when the splitting algorithm finds no potential for further subdivisions. Using the full dimensionality of the data, our method can characterize clusters within WBC data even in cases where these clusters overlap in the standard 2D scatterplots.

Paper Details

Date Published: 10 May 1996
PDF: 13 pages
Proc. SPIE 2678, Optical Diagnostics of Living Cells and Biofluids, (10 May 1996); doi: 10.1117/12.239513
Show Author Affiliations
Shu-Jen Yeh, Abbott Labs. (United States)
Eric L. Russell, Abbott Labs. (United States)

Published in SPIE Proceedings Vol. 2678:
Optical Diagnostics of Living Cells and Biofluids
Daniel L. Farkas; Robert C. Leif; Alexander V. Priezzhev; Toshimitsu Asakura; Bruce J. Tromberg, Editor(s)

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