Application of a new novel data-mining technique to cytometry data

James F. Leary; Scott R. McLaughlin; Lisa M. Reece

doi:10.1117/12.384236

27 April 2000 Application of a new novel data-mining technique to cytometry data

James F. Leary, Scott R. McLaughlin, Lisa M. Reece

Proceedings Volume 3921, Optical Diagnostics of Living Cells III; (2000) https://doi.org/10.1117/12.384236
Event: BiOS 2000 The International Symposium on Biomedical Optics, 2000, San Jose, CA, United States

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.

Citation Download Citation

James F. Leary, Scott R. McLaughlin, and Lisa M. Reece "Application of a new novel data-mining technique to cytometry data", Proc. SPIE 3921, Optical Diagnostics of Living Cells III, (27 April 2000); https://doi.org/10.1117/12.384236

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available