The long term goal of this project is to improve the tools available for quantitative analysis of multivariate flow cytometry data so that the biological measurements made with flow cytometers will become more quantitative. In immunophenotyping, flow cytometry has been used to assist in making positive/negative decisions about antigen expression and to determine the fraction of subsets present. Statistical and other mathematical tools exist to help in making more refined decisions. However, these tools are not widely used in the flow cytometry community because the tools have not been evaluated thoroughly with flow cytometry data and because the data analysis packages available are very general and frequently difficult to use. Further, the results are not usually in a form that facilitates biological interpretation. We propose to investigate several strategies for finding subpopulations in multivariate data and to evaluate the methods with a variety of flow cytometry data. We propose to develop Macintosh computer programs to implement these various multivariate data analysis strategies and to distribute the programs to our clinical collaborators and to others in the flow cytometry community. We propose to write a book on flow cytometry data analysis that will include a diskette containing the programs developed during this project. We propose to hold two data analysis workshops focused on clustering methods in multivariate data analysis.
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