As previously reported, the Flow Cytometry Advanced Data Analysis Project (FC/ADA) is a collaborative laboratory automation project to design and implement a production-oriented basic research support facility capable of the acquisition, archiving, and in-depth analysis of multi-parameter flow cytometry data. The facility permits analytical techniques, such as non-hierarchical cluster analysis and multidimensional gated histogramming, to be applied to experimental data. A data staging and archiving system scaled to match production data acquisition rates is also provided for near-online access to experimental data for an extended period of time and automatic archival storage (and retrieval) of all experimental data. Supporting more than 55 Experimental Immunology Branch (EIB) investigators, the software and techniques being developed under this project are also shared with other flow cytometry facilities within the NIH intramural research program and with the FDA Center for Biologics Evaluation and Research. In FY92 the production workload of the EIB facility was shifted to the FACSStar Plus cytometer and its associated VAX/VMS data management and analysis system. A 30 Gbyte magneto-optical disk Hierarchical File Storage System (HFSS) was purchased to augment the existing 8mm tape archiving system with near-online storage. The Cluster Analysis Program (CAP) has been refined and more thoroughly documented. The VMS hosting of the Laboratory Application Package (LAP) has been improved and the user documentation and flow cytometry specific features have been significantly enhanced. Work in FY93 will center on system tuning and load balancing, completion of system and user documentation and personnel training. Upon arrival of the HFSS (early 1993), the hardware and software will be installed first in CSL for testing prior to permanent installation at the EIB production facility, end of FY93. The HFSS will be available to the EIB facility during this period over the NIHnet. Work on CAP will include significant user interface improvements and the development of additional classification algorithms and tactics.
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