Automated analysis of high dimensional flow cytometry data

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The recent increase in the amount of data generated by flow cytometry (FCM) poses unique informatics and statistical challenges, and it is widely recognized that one basic challenge for FCM is to simplify the extraction of data and statistical information. Traditionally, the majority of FCM experiments have been analyzed visually, through time-consuming and subjective serial inspection of one or two dimensions at a time, of up to 17 dimensional data. While commercially available analysis tools are designed to facilitate this process, such visual procedures necessarily neglect the higher dimensionality of the data. Furthermore, the subjective character of visual analysis is a major obstacle to the automation and reproducibility of research. In contrast to 2D visual analysis, a multivariate statistical analysis can mine the full set of markers simultaneously.