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Proceedings Paper

Application of a new novel data-mining technique to cytometry data
Author(s): James F. Leary; Scott R. McLaughlin; Lisa M. Reece
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Paper Abstract

Most cytometry data analysis routines focus on individual data sets and require human decision-making in determining what is important to compare between two or more dat sets. As cytometry data becomes more complex, the experimenter could benefit from data mining techniques which can help guide the experimenter to the major similarities and differences between data sets. These similarities and differences are not always obvious, particularly in complex, multi parameter data and those involving rare-events. 'Subtractive clustering', a novel form of exploratory dat analysis/data mining we have recently developed compares the similarities and differences between two ro more Listmode data files. It represents a powerful new tool for cytometry. The program uses variable sized multidimensional data bins from test and control files while making no assumptions about the data. This presentation deals with subtractive clustering data mining of flow cytometry data, but the technique is entirely general and could be easily adapted to image cytometry and to any other situation involving comparison of large multidimensional data sets. This software allows for 'subtractive clustering' of data between files of varying sizes and permits the analysis of very large files. Various controllable parameters in the program allow for definition of 'similarity' of data clusters in multidimensional space. Subtraction is not a simple subtraction of multidimensional data points, but rather a subtraction of 'similar' dat objects which may or may not have the same data values. In addition, since the multidimensional coordinates of each cell are not stored in a multidimensional array, the program is not limited by data dimensionality or resolution. Data are processed and binned form Lismore data into sub-lists that permit rapid comparison between test and control files. By utilizing a Windows interface, the input of the experienced researcher is taken into account.

Paper Details

Date Published: 27 April 2000
PDF: 6 pages
Proc. SPIE 3921, Optical Diagnostics of Living Cells III, (27 April 2000); doi: 10.1117/12.384236
Show Author Affiliations
James F. Leary, Univ. of Texas Medical Branch/Galveston (United States)
Scott R. McLaughlin, Univ. of Texas Medical Branch/Galveston (United States)
Lisa M. Reece, Univ. of Texas Medical Branch/Galveston (United States)

Published in SPIE Proceedings Vol. 3921:
Optical Diagnostics of Living Cells III
Daniel L. Farkas; Daniel L. Farkas; Robert C. Leif, Editor(s)

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