STORAGE AND ANALYSIS OF FLOW CYTOMETRY DATA AT THE CELLULAR LEVEL USING RELATIO

这个子项目是众多研究子项目之一

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Storage and analysis of flow cytometry data at the cellular level using relational database technology and supercomputing resources. Principal Investigator: Keith Boyce Associate Director Bioinformatics Immune Tolerance Network / UCSF 2585 Freeport Road - Suite 207 Pittsburgh, PA 15238 V: 412 820 8807 (x 2312) F: 412-820-8912 Email: kboyce@immunetolerance.org Introduction: The fundamental objective of the Immune Tolerance Network, or ITN, is to accelerate the clinical development of new tolerance therapeutics in human disease. The ITN conducts clinical trials and tolerance assay studies in a number of disease areas: Islet Transplantation Kidney & Liver Transplantation Autoimmune Diseases Allergy & Asthma. One of the primary mechanistic assay technologies used by the ITN is flow cytometry. The ITN Data Center has developed a novel method of storing and retrieving flow cytometry experimental data (panels, tubes, antibody used etc.) and derived results at the summary / specimen level. With over 2500 specimens across 11 Clinical Trials there are nearly a million rows of derived results from the original flow cytometry laboratory assessment. Raw results are archived in a file repository in their native, industry standard FCS file format. Any re-analysis of the raw data requires a loading of the raw cellular / event data from the FCS files. While FCS files are designed to an approved standard, they are not analyzable without an FCS analysis software package or data adapters that are aware of the FCS data format / standard. After discussions with many other Data Centers and Flow Cytometry laboratories this storage and re-analysis approach appears to be a common method. Abstract: We are proposing to work with the CyberInfrastructure Partnership and TeraGrid on an initial Development Allocation (DAC) to create an Oracle database of cellular event data. Once the database is created we would like to explore potential collaborations with TeraGrid on data mining, analysis, visualization, and simulation methods based on TeraGrid capabilities which would lead to a Medium or Large Allocation project in the future. As part of our data loading and storage we are proposing to retain the cellular level event data in a relational database rather than in an associated FCS file. While the number of events can be set during the running of the instrument, typical ITN assays vary from 20,000 to 100,000 cells / events per flow cytometry tube analyzed. The ITN currently has over 2700 analyzed specimen across 11 different clinical trials. This would potentially result in the need to store results for 45,000,000,000 individual cellular events with typically 3 7 fluorescent intensity measurements per event. The ITN currently has 4 or 5 measures per event. The numbers of events will most likely increase on an annual basis between 10 20 billion events. During on-going trials much of the analysis focuses on time-course analysis of derived assay data from participants across multiple treatment arms of the trial. As the clinical trials complete there will be more instances where cross-trial analysis of immune tolerance data will need to be performed. These analyses could require the analysis of a very large number of these raw cellular events. Additionally, data mining and exploration for potential biomarkers of immune tolerance across multiple the trials will likely need to explore all of this cellular data plus many other data dimensions related to clinical outcomes, phenotypes, genotypes, demographics, treatments / therapies and results from other mechanistic assays conducted by the ITN. Problem addressed: Flow cytometry is a rich source of cellular information which can be used in a wide variety of bioinformatics applications. We believe that by using TeraGrid processing and relational database technology many of these applications and ideas will be enabled. This DAC can demonstrate that processing, scalability, and system limitations related to the analysis of raw flow cytometry data which are imposed by the current methodologies related to storing cellular event data in FCS files can be significantly reduced. Current processing of FCS files often requires in-memory management of many data loading and manipulation and summarization / filtering functions which can be better handled by database management systems. This would enable bioinformatics professionals to focus their efforts on building high performance parallel analysis and modeling functions rather than file, data and memory management.