Abstract #302400

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JSM 2003 Abstract #302400
Activity Number: 10
Type: Invited
Date/Time: Sunday, August 3, 2003 : 2:00 PM to 3:50 PM
Sponsor: ASA, San Francisco Chapter
Abstract - #302400
Title: A Density-based Clustering Method for Flow Cytometry Data
Author(s): Michael D. Lock*+
Companies: BD Biosciences
Address: 2350 Qume Dr., San Jose, CA, 95131-1812,
Keywords: clustering ; density estimation ; kernel ; flow cytometry ; edge-finding
Abstract:

Flow cytometry is an advanced technology that allows life science researchers to enumerate and classify thousands of cells in seconds; for example, flow cytometry is the principal method used to enumerate the number of CD4+ T-helper lymphocytes in HIV+ patients. Researchers usually identify different cell types by looking for distinct clusters in two dimensional scatter plots from a multidimensional dataset. Clusters identified in two dimensions can then be isolated--a process called "gating,"--and examined further in scatter plots of other dimensions. The method described here is a customized clustering technique that seeks to automate this visual process. First, a kernel-based density estimate is constructed using a fast Gaussian filter algorithm. Second, clusters are determined from the density estimate using a tree-based algorithm. After these first two steps the data are clustered, but our method continues to a third step to derive boundaries around the data; such boundaries are useful for gating. The complete method is similar to edge-finding algorithms used in image processing--a fast, easy to use, automatic method for encircling data clusters.

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