A rich analytical environment for flow cytometry experimental results.
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.
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. jsiebert@acm.org
Janet
J
Siebert
Krzysztof J
KJ
Cios
M Karen
MK
Newell
D000755:Anemia, Sickle Cell / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D004867:Equipment Design; D005434:Flow Cytometry / Q000379:methods*; D006678:HIV / Q000378:metabolism; D006801:Humans; D007158:Immunologic Techniques; D016247:Information Storage and Retrieval; D008055:Lipids / Q000737:chemistry; D021962:Membrane Microdomains; D009369:Neoplasms / Q000378:metabolism; D011381:Programming Languages; D013050:Spectrometry, Fluorescence / Q000379:methods; D014584:User-Computer Interface
D008055:Lipids