A rich analytical environment for flow cytometry experimental results

  • Authors:

  • Janet Siebert;Krzysztof J. Cios;M. Karen Newell

  • Affiliations:

  • 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.;Univ. of Colorado at Denver and Health Sciences Center, Denver, CO/ Univ. of Colorado at Boulder;University of Colorado at Colorado Springs, CU Institute of Bioenergetics, 1420 Austin Bluffs Parkway, Science Building Room 142, Colorado Springs, CO. 80918, USA

  • Venue:
  • International Journal of Bioinformatics Research and Applications
  • Year:
  • 2006

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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.