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This article is part of the supplement: Selected articles from the Second IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS 2012): Genomics

Open Access Research

Automatic B cell lymphoma detection using flow cytometry data

Ming-Chih Shih1, Shou-Hsuan Stephen Huang1*, Rachel Donohue2, Chung-Che Chang3 and Youli Zu2

Author Affiliations

1 Department of Computer Science, University of Houston, Houston, Texas, USA

2 Department of Pathology and Genomic Medicine, The Methodist Hospital, Houston, Texas, USA

3 Department of Pathology, University of Central Florida, Orlando, Florida, USA

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BMC Genomics 2013, 14(Suppl 7):S1  doi:10.1186/1471-2164-14-S7-S1

Published: 5 November 2013



Flow cytometry has been widely used for the diagnosis of various hematopoietic diseases. Although there have been advances in the number of biomarkers that can be analyzed simultaneously and technologies that enable fast performance, the diagnostic data are still interpreted by a manual gating strategy. The process is labor-intensive, time-consuming, and subject to human error.


We used 80 sets of flow cytometry data from 44 healthy donors, 21 patients with chronic lymphocytic leukemia (CLL), and 15 patients with follicular lymphoma (FL). Approximately 15% of data from each group were used to build the profiles. Our approach was able to successfully identify 36/37 healthy donor cases, 18/18 CLL cases, and 12/13 FL cases.


This proof-of-concept study demonstrated that an automated diagnosis of CLL and FL can be obtained by examining the cell capture rates of a test case using the computational method based on the multi-profile detection algorithm. The testing phase of our system is efficient and can facilitate diagnosis of B-lymphocyte neoplasms.