Flow cytometry is a data-rich technology that plays a critical role in basic research and clinical therapy for a variety of human diseases. Recent technological developments have greatly increased the areas of application and data throughput, and corresponding innovative analysis methods are needed. In order to be able to take advantage of these new capabilities researchers need access to high quality analysis tools that will help to identify subpopulations of cells with particular characteristics. The methods we are proposing include advanced methods for machine learning and visualization. We will apply our methods to a number of different scenarios such as the analysis of longitudinal data, and the analysis of data arising from clinical studies.
The aims of this project are to provide statistical and computational methods for the analysis of flow cytometry data. The impact of these tools will be to provide better, more reliable, tools for the analysis of flow cytometry data. The domain of application spans all diseases, but current applications are focused on HIV disease and cancer.
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