Title: A rich analytical environment for flow cytometry experimental results

Authors: Janet Siebert, Krzysztof J. Cios, M. Karen Newell

Addresses: Department of Computer Science and Engineering, University of Colorado at Denver and Health Sciences Center, Campus Box 109, P.O. Box 173364, Denver, CO 80217 3364, USA. ' Department of Computer Science and Engineering, University of Colorado at Denver and Health Sciences Center, Campus Box 109, P.O. Box 173364, Denver, CO 80217-3364, USA; Department of Computer Science, University of Colorado at Boulder; Department of Preventive Medicine & Biometrics (School of Medicine), University of Colorado at Denver, Denver, CO, USA. ' University of Colorado at Colorado Springs, CU Institute of Bioenergetics, 1420 Austin Bluffs Parkway, Science Building Room 142, Colorado Springs, CO. 80918, USA

Abstract: Existing analysis tools for flow cytometry data offer specialised but limited functionality. This work presents advantages of combining the cytometer|s data with sample-specific information. Data is loaded into a relational database, where the analyst can query based on sample characteristics such as species, gender, diet type or sample stain type.

Keywords: flow cytometry; immunology; data analysis; data mining; knowledge discovery; bioinformatics; relational database.

DOI: 10.1504/IJBRA.2006.009193

International Journal of Bioinformatics Research and Applications, 2006 Vol.2 No.1, pp.52 - 62

Published online: 09 Mar 2006 *

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