Towards Flow Cytometry Data Clustering on Graphics Processing Units

Like many modern techniques for scientific analysis, flow cytom-etry produces massive amounts of data that must be analyzed and clustered intelligently to be useful. Current manual binning tech-niques are cumbersome and limited in both the quality and quan-tity of analysis produced. To address the quality of results, a new framework applying two different sets of clustering algorithms and inference methods are implemented. The two methods investigated are fuzzy c-means and minimum description length inference and k-medoids with BIC. These approaches lend themselves to large scale parallel processing. To address the computational demands, the Nvidia CUDA framework and Tesla architecture are utilized. The resulting performance demonstrated 1-2 orders of magnitude improvement over an equivalent sequential version. The quality of results is promising and motivates further research and develop-ment in this direction. 1