Real-time multivariate statistical classification of cells for flowcytometry and cell sorting: a data mining application for stem cell isolationand tumor purging,

摘要

Abstract: Multivariate statistics can be used for visualization of cell subpopulations in multidimensional data space and for classification of cells within that data space. New data mining techniques we have developed, such as subtractive clustering, can be used to find the differences between test and control multiparameter flow cytometric data, e.g. in the problem of human stem cell isolation with tumor purging. They also can provide training data for subsequent multivariate statistical classification techniques such as discriminant function or logistic regression analyses. Using lookup tables, these multivariate statistical calculations can be performed in real-time, and can even include probabilities of misclassification. Thus, the only distinction between off-line classification of cells in data analysis and real-time statistical decision-making for cell sorting is the time limit in which a classification decision must be made. For real-time cell sorting we presently are able to perform these classifications in less than 625 microseconds, corresponding to the time that it takes the cell to travel from the laser intersection point to the sort decision point in a flow cytometer/cell sorter. Statistical decision making and the ability to include the costs of misclassification into that decision process will become important as flow cytometry/cell sorting moves from diagnostics to therapeutics. !27