Comparing and Clustering Flow Cytometry Data

被引：2

作者：

Liu, Lin ^{[1
]}

Xiong, Li ^{[1
]}

Lu, James J. ^{[1
]}

Gernert, Kim M. ^{[2
]}

Hertzberg, Vicki ^{[3
]}

机构：

[1] Emory Univ, Dept Math CS, Atlanta, GA 30322 USA

[2] Emory Univ, BimCore, Atlanta, GA 30322 USA

[3] Emory Univ, Dept Biostat, Atlanta, GA 30322 USA

来源：

2008 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, PROCEEDINGS | 2008年

关键词：

D O I：

10.1109/BIBM.2008.61

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Flow cytometry technique produces large, multidimensional datasets of properties of individual cells that are helpful for biomedical science and clinical research. This paper explores an approach for comparing and clustering flow cytometry data. To overcome challenges posed by the irregularities and the high dimensions of the data, we develop a set of data preprocessing techniques to facilitate effective clustering of flow cytometry data files. We present a set of experiments using real data from the Protective Immunity Project (PIP) showing the effectiveness of the approach.

引用

页码：305 / +

页数：2

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